All right, welcome everyone. Welcome everyone joining in person and also those of you joining virtually to our first committee member of the National Academies of Science, Engineering and Medicine, study on community wastewater-based infectious disease surveillance. My name is Guy Palmer, I'm the chair of the committee. I'm a professor at Washington State University where I serve as senior director of global health for the university. Our committee has been tasked to review community level wastewater disease surveillance and its potential value towards prevention and control of infectious diseases in the United States. If I can advance this slide. Next slide. Yep. The statement of task is presented here and that is available on the website for the committee for those of you who want to review this in depth. But an ad hoc committee and I'll introduce the committee members here in just a moment. We'll review this community level wastewater-based surveillance based on the statement of task. You can see that here, basically it's to describe what wastewater-based surveillance disease surveillance is and how it is different from other approaches to disease surveillance. We'll review how wastewater-based surveillance has been useful in COVID-19 during the pandemic and how that has informed public health decisions. We'll also examine the potential value of wastewater-based disease surveillance for understanding preventing disease and illness beyond COVID and the strengths and limitations of this approach in the United States. We'll describe the general characteristics of an approach for a national system for wastewater-based disease surveillance and discuss broad approaches to increase the public health impact, basically, how this will inform decision-making. For the purposes of this study, it's important to emphasize that community level wastewater-based surveillance does not include local surveillance at a neighborhood or institutional level. Next slide, please. And this is what will be the current focus, which is the phase one part of our study. Phase two is defined here and is present on the committee. I think for today, we're going to focus really on our phase one of our study proposal, but this is available on the committee website. Phase two, obviously, this is linked as we progress through the different phases. Next slide, please. So the study schedule for phase one, we've had, this is the first hybrid committee meeting. We'll have a second hybrid committee meeting in June with a goal of compiling this report in October with a public report release in November and then progress to phase two, which is tentative at this time. Next slide, please. Okay, the committee roster, I'm going to actually ask the committee members to introduce themselves starting with Ami. And if you just say basically what your area of expertise is and the institution you're located at. Thank you, my name is Ami Pat. I'm an associate professor of medicine and genetics at Stanford University. And my area of expertise is the human gut microbiome and measuring viruses and bacteria in human soil. Thank you. Marissa, Marissa's not here. Raul. Hello, everyone. I'm an environmental scientist at Hampton Road Sanitation District. And my background is in molecular quantification of markers in surface waters. Chuck? Chuck Haas, Drexel University, professor of environmental engineering. And my expertise is microbial risk assessment and state and transported pathogens in the water environment. Lauren. Good morning. I'm Lauren Hopkins. I'm a chief environmental science officer for the city of Houston. And I run our moist water surveillance program and public health intervention for the city. Nataki. Nataki Osborne-Jelts, assistant professor of environmental and health sciences at Spelman College in Atlanta, Georgia. My work is at the intersection of water resources management and public health. Christine. Good morning. I'm Christine Critter-Johnson. I'm an epidemiologist at the University of California Davis and my expertise in infectious disease and surveillance. Rob. Hi, I'm Rob Knight. I direct the Center for Microbiome Innovation at UC San Diego, where I'm a professor of pediatrics, bioengineering and computer science and engineering. And my expertise is in microbiome technology and data analysis. Sandra. Good morning. I'm a professor in the School of Freshwater Sciences and my expertise is in environmental microbiology and bacterial genetics. And our lab develops new indicators to track pollution sources in the environment, especially untreated sewage. Michelle. Hi, I'm Michelle Mello. I'm a professor of law and health policy at Stanford. And my work focuses on issues of public health ethics and public health law. Scott. I'm Scott Meshti. I'm a professor and associate chair in environmental occupational health sciences in the School of Public Health at the University of Washington. And I focus on fake transport, detection and control pathogens in the environment. Reika. Good morning, everybody. My name is Reika Singh. And I am serving as a wastewater surveillance program manager at Virginia Department of Health. And my expertise is environmental science engineering and public health. Nourage. Good morning, everyone. I'm a professor at the University of Southern California and my expertise is in health policy and economics. And Krista. Hello, I'm Krista Wiginton. I'm an associate professor at the University of Michigan in civil and environmental engineering. My research focuses on the mechanistic fate of viruses in the environment and their detection. And we have one other member of the committee who's not here this morning. I think she's going to be joining in the afternoon and that's Marisa Eisenberg of the University of Michigan. Before we kick off this first session, I do want to note that this is an open on the record session. This is being live streamed and will be archived for later viewing after the event, after the event. This meeting is being held to gather information to help the committee conduct its study. The committee will examine the information material obtained during this and other public meetings in order to inform its work. Comments made by individuals, including members of the committee should not be interpreted as positions of the committee or of the academies. Committee members will often ask probing questions in these information gathering sessions that may not be indicative of their personal views or the views of the committee. The committee will deliberate thoroughly before writing its draft report. Moreover, once the report is actually drafted and written by the committee, it undergoes rigorous independent review by experts who are anonymous to the committee and the committee must respond to this review with appropriate revisions to satisfy the academies report review committee. And only once the NAS president has signed off is this considered an official academies report and the recommendations held within. Therefore observers should not draw conclusions about the committee's work based on today's discussions. Doing so would be premature. Next slide. This is the agenda we're gonna start with. I think we're just a couple of minutes behind schedule. So let's go ahead and progress with that. We're gonna have an overview of the National Wastewater Surveillance System by speakers from the CDC. And our first speaker is Amy Kirby who will give us an overview of this system. And she is a microbiologist and senior service fellow at the CDC. Amy. Thank you. That's not fine. All right, thank you for that introduction. Yes, my name is Amy Kirby. I'm the program lead for the National Wastewater Surveillance System. Thank you everyone for taking the time to be here today and talk about wastewater surveillance. As Guy said, we will start with an overview this morning of the National Wastewater Surveillance System and how it is structured. In my initial introduction, I'm really going to focus on the implementation of the system and the decisions we had to make to get the system up and running quickly in the context of the COVID-19 pandemic. And then we'll dive into more of the science of the laboratory testing and the data analytics with other talks later today. So I always start with this slide. This is the four major advantages of wastewater surveillance, specifically for SARS-CoV-2, although many of these advantages will apply to other health targets as well. The first, and we recognize this very early on, is that about 50% of individuals that are infected with SARS-CoV-2 will shed detectable viral RNA in their feces. And this shedding happens whether they have symptoms or not, it happens in adults and children, and it happens very, very early in the infection. And so what that means is that by looking at wastewater as sort of a pooled community stool sample, we can get data on the full spectrum of diseases that are in a community, which is very different from our clinical surveillance, which is primarily gonna detect symptomatic and severe infections. Second, and what I think is really the most powerful benefit of wastewater surveillance is that it's independent of healthcare seeking behavior and testing access. So it doesn't matter if people go to the doctor, it doesn't matter if they get tested, it doesn't matter if that test is reported to health departments as we see now changing with the movement towards home testing. As long as that community is using toilets that are connected to a sewer system, we can get information on the overall infection levels in that community through wastewater surveillance. Third, it's a very efficient method of getting community level data. So with that single sample collected at a wastewater treatment plant, we can get information on hundreds, thousands, even millions of people in our very largest systems here in the US. And finally, it's fast. From the time the toilet is flushed until we have data in hand is about five to seven days. And throughout the course of the pandemic, we've seen that this is a leading indicator of trends in the community. So we've detect trends in wastewater on average four to six days before we see those same trends reflected in case data and about 10 days before we see those trends reflected in hospitalization data. So as I said, I really wanna focus my talk this morning on the structure and how we implemented this system. So this is an overview of the data flow for news. So if you start at the top left, of course we have communities as our source of wastewater. That wastewater flows to wastewater treatment plants where the sample is collected. The sample then goes to laboratories for testing. That data is sent to health department and then they submit the data to CDC for analysis and we report it back both to the health departments and to the public for use. But let's look through each of those stakeholders because each of them have some impacts on how the system was ultimately set up. So let's start with CDC. And I think it's worth taking a step back and thinking about what CDC's role is in national disease surveillance. And really it comes down to these four functions. First and foremost, CDC's role is to ensure data comparability across jurisdictions and over time. For wastewater surveillance, that is particularly critical because we did not and still do not have standard methods for wastewater testing for SARS-CoV-2. And so it was, this is sort of a factor, a complication of environmental microbiology, right? We don't have diagnostics the way they have for clinical testing. And so we had to evaluate these methods as they were being developed, but we wanted to put them to use as soon as there was evidence that they could be useful. And so we just chose to take the approach of letting, as my colleague Rory Walsh says, you'll hear from him later today, we chose the method of letting 1,000 methods bloom and seeing which ones are performing well over time and protecting that flexibility to be able to change methods as the pandemic evolved. And so really one of our key focuses then is to make sure that even though the data is being collected, we're using different methods, ultimately the metrics that we're looking at across jurisdictions are as comparable as they can be. Second, we of course analyze data but really we provide public health interpretation and guidance and that goes with that third bullet which is providing technical assistance to implementers. How do you design a sampling frame? What is the best way to sample and test? How do we evaluate and use this data once we have it back in our hands for our communities? And finally, CDC does a lot of summary, national level summaries of data and making that data available for states, the public as well as other stakeholder organizations. I think it's worth taking just a minute to look at what a typical surveillance system looks like and the timeline for establishing it because timeline played a big role in how news was stood up. And so I always think about news and comparison to PulseNet which is a system that was established just over 25 years ago to address foodborne outbreaks. So in 1993, there was the large jack-in-the-box E. coli 0157H7 outbreak and that was really the inciting outbreak for the development of PulseNet. So that's 1993. A year later, there was validation of the method they were gonna use which is Pulse Field Gel Electrophoresis or PFGE. And then a year after that, CDC and APHL developed the concept for PulseNet as a surveillance system. And then a year after that, PulseNet was launched in four pilot states. So we're already three years after this inciting outbreak and keep in mind we knew about E. coli 0157H7 even prior to 1993. So this is a more typical timeframe for establishment of a new surveillance system. So four states in 1996. In 1997, we have the establishment of a funding mechanism for our public health jurisdictions which is the epidemiology and laboratory capacity cooperative agreement which we still rely heavily on today. And then in 2000, they added more software to enable analysis at scale. And then by 2001, five years later, they had scaled PulseNet to all 50 states. And then worth noting that in 2015, they began shifting away from PFGE to an improved analysis method which is whole genome sequencing and that transition took three years to get all 50 states switched over. So that's a 25 year timeframe. What has news looks like? I want to first draw your attention to the fact that our arrow is now only six years represented there. So our pathogen that we're targeting SARS-CoV-2 first emerged in late 2019. In January 2020, of course, we had the first US case. And then over the course of that year, we evaluated the concept of wastewater surveillance, gathered data on how it was performing, how well it correlated with trends and cases and developed the system that I'm going to tell you about. And in September of 2020, we were able to launch news in eight states. Now, about a year later, in August of 2021, we had our first major funding round through again that epidemiology and laboratory capacity cooperative agreement. And in that round, we have 43 jurisdictions funded. So a major expansion just in the first year. As of this month, we have 841 utilities that are reporting data through their state to CDC. And that's in 44 states. For the future, so over the next three years, we're going to continue developing the system. So adding new methods, variant tracking through sequencing, we're going to be transitioning to digital PCR. So converging on a single method as opposed to the letting all methods bloom approach. We're going to be adding non-COVID targets, which you'll hear more about this afternoon from Rory Welsh. And our goal is to expand to all 50 states and also to territories and tribal nations. So very quickly, only six years from the pathogen first being identified to full 50 state implementation and sort of the full vision of what news can be. So we're under a very quick timeline and that has had some implications on how the system is set up. So that's what happened at CDC. I'm working backwards through our data flow, state and local health departments are our primary point of contact with these systems. So every state is going to implement their own Wakesport or surveillance network. And that's primarily because CDC has that ELC mechanism available to directly fund our state and local health departments to support these public health activities. And so we have been initially funding them through COVID supplements, but now we are part of the annual ELC funding cycle. So every year there's going to be an opportunity for state, local and territorial health departments to apply for funding from CDC to support wastewater surveillance. And we've been quite broad with how that funding can be used. So obviously surveillance coordination is going to be the top of the list, but they can also use that funding to support sample collection and shipping, laboratory testing, and I'll talk more about that in just a minute, IT resources to manage all of this data and make sure that it gets to CDC. Many of them have used these IT resources to build their own internal or public facing dashboards for wastewater data. And they can also use it for contract support. So if they want to contract out the laboratory testing, if they're able to support their utilities through contracts to utilities, they are able to do that. So we really want this funding to be as flexible as it can be to support the state needs. That state led implementation leads to a lot more variability when we get to the laboratory and wastewater treatment plants that are enrolled. So if we focus on the laboratories first, we see a real diversity in the way states have chosen to implement this. So some of them are able to do this testing in their public health laboratories and environmental health laboratories. And so that is a more traditional approach to disease surveillance, to have it be essentially in-house to your health departments. Others have chosen to partner with academic researchers that are able to do this type of testing. That's really where the majority of the expertise for this type of testing lay in 2020. And so they have been able to rapidly build up this capacity to do real-time testing and provide that data to health department partners. And then there are some utilities that are able to do this testing themselves. So in some cases, the utility is also the testing lab. And then finally, there's been a real boom in the amount of private laboratories that are offering this type of testing. So some of our states have partnered with private labs to get this testing done. And I'll talk more about a testing contract that CDC has put in place as well. And then that state-led variability and decision-making also has impacts on the wastewater treatment plants that are involved. We have let states make their own decisions about how they are going to enroll wastewater utilities into their system. Some of them have opted to do full coverage. So every wastewater plant in the state is enrolled. Others have targeted high-risk utilities. So thinking about large cities, transportation hubs, particularly vulnerable communities. And frankly, others have only done a few pilot implementations because remember they are building a new system while they are also dealing with a pandemic. So our health departments were very stretched and many of them were very clear that bandwidth was a limitation on how extensively they could implement news. And so we have quite a bit of variability in the scale of coverage that our states have implemented in the past year. Despite all of those barriers, we have had great growth in the news system over the past year and a half. So this graphic, Zach made it yesterday for us. So this is showing enrollment of sampling sites over the past year and a half. The dots are scaled by the population that each of those sampling sites represents. And this is both our state-led systems, which is our core news testing capacity. But we have also chosen to supplement those state systems with the nationwide commercial testing contract. So this is working with those private industries that are able to offer this testing. We have a contract that will provide testing twice a week to an additional 500 sites. This is not going to be a permanent component of news. This is a way for us to rapidly scale up coverage as our states develop their own system. Together we have 741 sites that are reporting data into news. Again, that's 44 states with data. The District of Columbia and eight tribes that have data in the system. And collectively the population covered by all of those sites is over 109 million people. So we're already at over 30% of the US population. Now, there are of course challenges. There is a hard limit to how much wastewater surveillance can grow. And that's really based on wastewater system coverage. So estimates range from 20 to 25% of US residents are not connected to sewer systems. And so they will not be accessible. Those communities and households will not be accessible by wastewater surveillance. I also like to note that while that lack of sewer coverage is primarily rural, it is not exclusively rural. You can find pockets of septic systems even within large cities. So it's always important to know what your septic system coverage is in any community that you're performing wastewater surveillance in. Also in the past 15 years or so, there has been a real movement for large facilities to do their own onsite wastewater treatment. And so not making use of these municipal systems. Importantly for wastewater surveillance, that trend is particularly pronounced with universities and correctional facilities. And so many times if you're doing wastewater surveillance in a community and there's a large university, it may not be contributing to the community sample because they're doing their own onsite treatment. So very important to work closely with utility partners to know what exactly is part of the wastewater coming into a treatment plant. We'll hear more about these other barriers going in the next few presentations, but I do wanna note that it is not easy to communicate about wastewater data. It requires careful analysis and communication and it can be a real challenge for plain language communication to the public. And one of the core messages that we really get concerned about people misinterpreting is if, for example, SARS-CoV-2 is not detected in your community wastewater, that does not mean that your community is free of cases. It just means it's below the limit of detection for this surveillance method. So we need to be really careful when we start getting into low incidence periods where we're getting negative results that that doesn't mean that we're free of COVID. We also have some barriers to sustainability of the program. There was hesitance to implement what people saw as a pilot program. I'm hearing less of that recently. So I think there, I know that with the agency, there is a dedication to this being part of our surveillance portfolio. And I think that message is getting out to the implementer community. But we also want to try and formalize some of that structure, especially around the laboratory testing and move the laboratory testing as much as we can into public health laboratories. So those are the labs that are built for sustainable surveillance testing. And we need to be able to free up, particularly our academic partners to do the research that's needed to move this forward. And we have seen wastewater data be used for many different things in our communities. What we hear most commonly from our implementers is that they value wastewater surveillance because it helps them distinguish the signal from the noise and all of the surveillance data that's coming in. And that's really because it is independent of healthcare seeking behavior and clinical testing. So almost all of our other surveillance indicators are linked to clinical care. They get skewed by many of the same drivers, but wastewater surveillance because it's independent does not. And so it gives them that check of, cases are doing something funny. What do we see with wastewater surveillance? Is it going up or is it going down or is it stable? And then they're using that information to make decisions about resource allocation. Where are we going to send additional mobile testing units? Where are we going to send additional? Which hospitals need additional support because they're going to be seeing cases on their door in the next week or two. And wastewater surveillance is really great for that near-term forecasting of what's going to happen in the next week or two because again, it's that first signal of cases in the community. And we've also increasingly seen wastewater used as a way to monitor the impact of interventions. The first use of this was a study that was a joint between NIH and CDC looking at the impact of home testing. So this was back in mid-2021. And they used wastewater surveillance as a community level indicator of the impact of this community level intervention. And I've linked you there to our MMWR which gives more details about how states are using this type of data to inform their response. So that brings us now to a really critical transition point for wastewater surveillance. We have seen that surveillance overall for SARS-CoV-2 is changing with less of a focus on massive screening tests and more of a focus on the type of indicators that would be associated with severe disease. But wastewater surveillance is still very important for providing that situational awareness. So this is a good time to really dive into what is the role for wastewater surveillance for SARS-CoV-2, but also for these other pathogens that we want to expand to. And importantly, how can we leverage this infrastructure that we have built for wastewater surveillance to get the samples collected by the utilities to get them shipped to laboratories to move all of this data from laboratories to health departments to CDC and back again and out to the public? How can we leverage that to provide the best possible data for public health action, not just now but also in the future, five, 10, 20 years from now? And so I'm really very excited to see what the committee comes up with and what your thoughts are on the potential applications of wastewater surveillance in the future. And with that, I would be happy to answer questions if we have time. I can't remember what the timing is here. Yeah, thank you, Amy. Now, we have about 10 minutes for questions from the committee. I think the... Chuck has the samples. Okay. I can't see. Oh, there's, yeah. Chuck. Hi, Amy. Two questions for you. First of all, looking at your state map looks like there's seven states that have not opted in. And so, you're insight into why that has occurred. And then my other question tackle both of ones is how about situations which they're certainly not uncommon. I don't have a sense of what the proportion is where the geographic coverage maps of the local health agencies are different than the geographic coverage maps of the utilities and thoughts on reconciling that. Okay. So the first question about the states that have not opted in, for many of them it's a bandwidth issue. So they, these were the states that were particularly concerned that this is a pilot system and that we're only doing it for COVID and then it's gonna be stood down and it's gonna be abandoned and standing up this whole new system just to have it be shut back down was more than they could accommodate in the midst of the pandemic which is absolutely fair from a resource standpoint. I am hopeful that now in this next round of funding we will be able to get some of those states on board because we have worked very hard to make it clear that this is not a pilot. This is a longstanding surveillance system. That said, I would be remiss if I didn't also say that there are political decisions in here. So some states are not interested in really engaging with the COVID response and of course wastewater surveillance is tightly linked to that. So I'm sure there's some of that in some of these states as well. As far as the geographic coverage, I guess I need a little more information on what exactly you are seeing different. Well, is it? So I mean, the utilities often serve as geographic areas that are different than the local health departments. And so what happens in a case like that? I mean, the one that quickly comes to mind based on my experience is Chicago, where Chicago has not took coterminous with the city. Yeah, yeah. I mean, that's sort of geographic boundary issue is huge for all aspects of wastewater surveillance, right? Because a wastewater systems boundaries often don't align with any other jurisdictional boundary. It doesn't align with a county, a city, a health department, any of those things. It's something that we always have in mind and primarily why most of our work is through state health departments because there you have less of those issues. There are a few wastewater systems that cross state boundaries, but they're the exception, not the rule. However, we do have some very large local health departments that run their own wastewater systems. Chicago is a great example, Houston is another one, LA is another one. And those systems tend to be, they have very close coordination with their utilities to understand where their boundaries are. We have also provided a R code that will allow them to use the geographic data within their case data and align it with their system boundaries from the utility so that they're comparing only cases that live within the geographic area of those sewersheds. So we refer to that as sewershed level case data and Zach can speak more to that. So we're trying to build as many tools as we can to address that. We can also visualize those boundaries in our internal dashboard, which Zach will show you more about. So trying to really make sure everyone recognizes that those boundaries aren't the same and we need to account for that. Yeah, thank you. We've got three questions. There'll be Michelle, then Neroj, then Reka. Thanks very much for your presentation. I'm really struck by the speed with which the system was stood up. And I wonder if you could just talk about some of the trade-offs that you had to make in order to make that happen or maybe putting it a different way. Is there anything that you would have done differently if speed had not been such a factor? Yeah, the biggest one is the method. If we had had the time, it would have been easier I think in the long run to do a full method evaluation from the beginning, identify an ideal single sampling processing testing method for news so that everybody was using the same approach. But we didn't want to slow down the implementation waiting for that. And we also didn't feel like we had enough data suggesting that a single method was outperforming the others to force everyone to one from the beginning. And then in total transparency, we now see that there are a handful of methods that are working very well but we know we're moving towards this multi-pathogen platform. So we don't want to ask states to switch to a single COVID test to then a year later come back and say, and now there's this new one that's a multi-pathogen. So the SARS-CoV-2 surveillance seems to be working well, allowing those multiple methods to be used, but we are going to convert on that single method as we expand targets. Neeraj. So Amy, thank you for your presentation. I had a couple of questions. So the first one was, what is the time lag from sample collection from a wastewater plant to actually kind of getting an actionable report? So how much time does it take to do that because I guess that would influence how actionable the data is. And the second question I had was, you mentioned that one of the limitations was that there might be low sensitivity, which is there is COVID in the community or some disease in the community and the test result would be negative. So I just want to know what the sensitivity is if there is a range that's available. Yeah, so the time lag can be very short. So it can be the shortest two to three days if there is frankly money put into making everything go as quickly as possible. So shipping with first arrival, whatever they call it, the one where they deliver by 10 o'clock in the morning and then a real emphasis on 24-hour turnaround in the lab and getting data from the health department to CDC as soon as it arrives in their laboratory. That can be very quick. However, we know there's delays at every step. What we see in our system is that from sample collection until we get data in the CDC system is about six days right now, but there is a long tail on that. And so what we are doing now is trying to identify where the barriers are to timely data transfer and submission and eliminate those and make them as small as we possibly can. For example, facilitating data submission through something like an API. As far as the sensitivity, we don't have an estimate for the national system overall as far as sensitivity. What I can tell you is our epidemiology team here as part of the COVID response has done a sensitivity analysis of sentinel surveillance systems in low incidence periods and they compared wastewater surveillance to overall case surveillance, school-based surveillance and nursing home-based surveillance. And what they found is that regardless of how they did this analysis, wastewater surveillance was always the most sensitive approach and was the first to flag in those low incidence periods. So we think the sensitivity is very, very low but we're still working to get good numbers for that. Okay, our last question is gonna come from Rekha but Scott and Christo, write your questions down because we will get to them one way or the other because they're important. Can I go now? Yeah. Okay, thank you. Thank you, Amy for a great presentation and you covered pretty much some part of my question. As you said, different states are exploring, implementing this program little differently with limited resources. So a national guidance document on not only collecting data but also on educating public would be very helpful. And I'm not sure like what are our plans to come up with that? Do we have any plans on that? Creating a national guidance document. So we do have resources that are available to our health department partners. We have not, we don't have plans right now for a overall national guidance document but I think that's a really great, a great suggestion. The challenge there is identifying who your audience is because if your audience is utilities it's very different than health departments which is very different from the public. And so we've been trying to provide resources as they're needed. So resources for the public as we roll out things like COVID data tracker so that they can understand how this data can be used to help them make decisions on an everyday basis. But yes, there is always a need for more resources and technical support. Thank you. All right, thank you, Amy. That was a great overview to get us started. Really appreciate it. Next we're gonna hear from Rachel West about building an engaging trust for the wastewater system, national wastewater surveillance system. She is a health scientist and presidential management fellow at the Waterborne Disease Prevention Branch at CDC. Can everyone hear me okay? Yes. Okay, thank you so much. Or at least I can. Okay. Great, well hopefully everyone else can hear me as well. Thank you for having me today to present to you on building community trust and engagement. I am the acting program support and coordination lead for NEWS and today I wanted to expand a bit on Amy's discussion of implementation, more on the utilities and community trust side as well as some considerations of ethics surrounding wastewater surveillance. So let's see if the slide will advance. There we go, okay. So as Amy described, this is our framework for the national wastewater surveillance system. And in my talk, I'll be focusing on what is in this green circle, our communities and the wastewater treatment plants and really how they interact with our state, tribal, local and territorial health departments. So as many of you know, wastewater surveillance really requires significant public health collaboration with environmental health. And this primarily depends on the wastewater surveillance timeliness and that depends upon our utilities sampling. So there's this cycle and building of relationships between the health department expertise, utility outreach, as well as community engagement. And we've found throughout the pandemic and throughout the history of NEWS that utilities are of course a key partner that we have not traditionally engaged quite as much in public health but coordinating with them throughout building a wastewater surveillance program really helps ensure its success and its applicability and effectively connecting these public health partners with environmental health partners early on really helps to ensure that that communication and coordination is there from an early point. So what really goes into wastewater sampling out of utility, as Amy mentioned, it requires quite a bit of input and time and so far this has been completely voluntary on utilities parts. And so this system certainly rests on their shoulders. The samples that they provide gives us more data and that provides better, more reliable trends that we can then communicate to our communities. But to get timely data, sampling protocols are best to have consistent and also if you can design that with a utility partner that is really optimal. And we would typically recommend sampling twice a week, once a week if that's not possible. And these samples can be a variety of types of samples. So it can be a grab sample, it can be a composite sample. It sort of depends on the tools that are available to that treatment plant or that sampling site. But this also brings into play staffing needs. So while everyone has experienced some probable stress through the pandemic, our wastewater utility partners of course have been continuing to serve throughout the pandemic in their own capacities. And on top of their daily duties, having to sample for wastewater for COVID-19 surveillance, it can take significant time, especially if that sampling is beyond the scope or frequency of what they normally do. And so respecting that is really important as well in understanding their capacity and their bandwidth. In addition, the shipping and storage of samples, making sure that they have adequate shipping supplies, they know exactly where the sample should be going, that can really better ensure that timely data that we need. As I mentioned, so far, utilities are primarily participating voluntarily. Some states and some jurisdictions are able to provide compensation, but this is not standard across the board. And so this is something that should also be considered when trying to implement a wastewater surveillance system and getting their buy-in is understanding truly how much this will add to their plate. And some states and jurisdictions are better poised than others to provide things like stipends or financial support for sampling. And we have heard that that aids in utility retention. So as I mentioned, promoting these partnerships early on is really key to program successes. And several of our implementing partners have actually taught us at news quite a bit about how to best engage with utilities, challenges they've faced, but we do recommend that if you are building a wastewater surveillance system, or even if you're in the midst of building it, connect with your utility partners as soon as you can. They have great insight into the infrastructure of systems. They also know their community very well and making sure to establish that relationship early on, not only for coordination purposes, but also for trust purposes and for sharing data, that can really benefit that relationship and strengthen that for the future to ensure that you retain those utilities and you're able to sample regularly and in a timely way. And so this would include early consulting of the utility capacity, really better trying to understand what they can really handle. Is that twice weekly sampling? Is that once weekly? Is it once per month? How long will it take them to find a shipping location to ship these samples for more remote utilities? That can be a significant amount of time. And so utility input and designing that sampling plan can really be helpful, not only for the health department to understand what kinds of samples are available, but to better establish what the sampling time is, what the frequency is, so that everyone is on the same page about expectations. And then regularly communicating that data to the utilities. Some of our implementing partners have been really wonderful about doing this, but although public health action primarily resides with the health department partners, utilities are providing these samples and they really value seeing the data. Not all utilities may be interested, but it's important to recognize those that would like to see the data and better understand what's going on in their community and how they're contributing to that. They're also very interested in how the data are being used, which I'll get into a bit more when we talk about ethics of wastewater surveillance, having that clear purpose of data use. And then continuing to have consistent meetings really helps with utility retention. So better understanding challenges as they arise, if there's a temporary challenge that may disrupt sampling or if there's something major coming up, if there's a staffing shortage or if someone is moving on from their position, better coordinating that so that all of the correct points of contact know who the other one is, that is also really important in maintaining high quality data. So at News we're really working to better support utilities and this is through some of our partnerships with other federal agencies as well as partners like the Water Environment Federation who have a great rapport with utilities and really understand their needs. One thing that we are also focusing on that Water Environment Federation has some valuable connections with with more rural or remote utilities that have different challenges than our urban utilities that are understanding what those might be and how we can support them. We're also trying to explore methods and ways to financially support utilities. Right now our ELC funding, our states and our jurisdictions are able to use that funding in some cases to support utilities. But we still wanna find other ways to do this so that we're showing utilities how important they are to this effort and trying to support them as best as we can. And further, we're now working to provide more tools to aid that health department in utility communication. So in our internal platform decipher, we have utility reports that can be downloaded and provided directly to the utility that provide a subset of data and metrics. It's kind of a quick look at what is going on in your community but we've provided that so that it's a very easy way for data sharing and that might promote further conversations. Now, of course, we have to move beyond the utilities and also discuss community trust. And Amy discussed our implementation at state and jurisdiction levels very well already but one of the things that we have found and we've heard from our partners is that community trust in the data really is bolstered by regular data sharing and also through communicating public health action. So how are these data being used? Are they being used to improve mitigation measures? Are they being used to help with resource allocation and help hospitals understand when surges might be coming? And on this slide, I have one of our implementing partners dashboards that's public facing. This is separate from our COVID data tracker but they use data from our internal dashboard decipher and they're able to create their own dashboards and this can be tailored to community needs. This is one of several I'm happy to say dashboards that our implementing partners have created but these types of tools to really bring the community in and involve them in the data have been really valuable in promoting that trust and in educating the community on what wastewater data are, what they can do and what their limitations are. And one particularly valuable element of that is understanding how to integrate wastewater data with other surveillance data and really using it to better understand community health. So sharing that purpose and the why behind the data collection helps strengthen community buy-in and it also provides transparency in that we're collecting this data but it's for this purpose and then it can help show the value of the data so that taxpayers know where their dollars are going and what they're supporting and how it's really helping keep their community safe. So to sum all of this up, of course, we have our state health department expertise and support. They have a great view of multiple communities within their state and what priority areas may be. And we also try to have discussions with our health department partners to better understand what communities might most benefit from wastewater surveillance, which might be historically under-resourced, which may be vulnerable populations, especially rural communities, we're trying to reach them more. And as Amy mentioned, some communities may not be on a sewer system and so this might not be the right solution for them but for those that are interested, we really leverage expertise with our state health department partners to better understand how we can implement systems and how we can support them in expanding wastewater surveillance. Oh, I'm sorry, the graphic got a little bit squished. Also, leveraging local health department expertise. Our state health departments have amazing expertise in so many levels, but our local health departments, we get really great feedback from them and in some cases in very large jurisdictions, the local health departments help us understand what's happening at a more granular level and they can really help design wastewater systems, no limitations of the community, whether that's the wastewater systems themselves or better helping us understand, oh, is this a community that experiences a lot of tourism or is this one that is really more residential? And then of course, utility infrastructure and knowledge, as I mentioned, that's really important in designing these systems. And then of course, understanding public priorities for wastewater surveillance and for public health, what are they concerned about throughout the pandemic? Of course, there's been SARS-CoV-2, but as we move forward with looking at other targets, what are their priorities and what are they concerned about and how do we communicate and educate them on what new targets we might be looking at and why we're looking at them? So part of really this public engagement is building trust and one way that we're really interested in exploring further in the future is ethical considerations around wastewater surveillance. So this is a relatively new public health tool in the US and wastewater samples are very rich. There are many microbes and there's a lot of genetic material. And currently, of course, there's no way to have individually identifiable genomic information in these samples, but it's important to acknowledge how rich these samples are, especially when thinking about a future use of samples. We also want to acknowledge that we're bridging environmental health and public health with wastewater surveillance. And while clinical public health surveillance has precedence and guidance for ethical considerations, environmental health, especially with wastewater surveillance is still growing in that area. And so we really want to try to bridge those two, those two important branches of wastewater surveillance so that we can better tailor ethical considerations and communicate these to the public. Why are we using these data? What will they be used for in the future? What are limitations? And we've encountered some of this with jurisdictions concerns about surveillance data and how they communicate that to the community. And we think it's important to acknowledge that and really listen to their priorities and concerns along the way so that we can better establish trust. So building trust in the system, a very complex goal, but one way that we're hoping to do that is through ethical considerations. And so right now, ethical guidance surrounding wastewater surveillance is largely at the local level or through academia. And when I say local, in some cases those are states or entire jurisdictions, but we don't have sort of a centralized guidance or considerations around wastewater surveillance and wastewater surveillance ethics. And all this is valuable and these efforts are really interesting and provide valuable insight into what we should consider. At news, we feel that it's important to sort of bring this to the national wastewater surveillance system stage and better consider these. And this is something we're really interested in the committee's feedback on is how we can properly do that without too much bias from our side, but also engaging the right people in considering these ethical priorities. And so especially for future use of wastewater data and samples, considering different relevant targets to public health, what pathogens are we looking at? Are we looking at chemical targets? Why are we looking at these targets? Is this because it's a pathogen that's very prevalent or is this really more specific to a certain community? And what will these data be used for? Is it for hospital resource allocation? And again, how will these data be communicated? So all of these factors, just for different pathogen targets, could be really important for ethical considerations. Archiving samples particularly, future use is always unpredictable, but we have learned in public health history that future use can be fraught with ethical mistakes and violations. And that can really erode trust with communities, especially his communities that have been historically underserved or oppressed. And one of the valuable parts of wastewater surveillance is that we can use it to reach these communities and hope to find health disparities so that we can address them properly. But first we have to build trust in those communities and maintain that trust. So understanding future use and what limitations might be around that. We can't predict what technologies might come in the future, but in at least keeping this in mind, we can try to guide ethical principles around this. And then of course social and political consequences of surveillance data. This is not quite as relevant for SARS-CoV-2, but for future targets, whether that's pathogens or otherwise, we need to better understand and ensure that there's no stigmatization of communities. So far we've actually gotten positive feedback that this wastewater surveillance for SARS-CoV-2 has not led to that, but for certain targets, we do want to be mindful of that, that we're doing this for the good of the public health and the community and that it's not misused in any way. So I mentioned that there are several efforts and ethical guidance on wastewater surveillance. One of them is not particularly on wastewater surveillance. It is general public health surveillance ethics guidance published by the WHO in 2017. And this has 17 different guidelines that are broadly applicable to public health surveillance. In some cases, not quite as applicable to wastewater surveillance. And that is where Canadian Water Network really did a lot of work in 2020 and 2021 to analyze those guidelines and tailor them to wastewater surveillance. A lot of their considerations focus on the use of the data, as well as data quality and how to communicate those data. Currently, there have been some more recent academic group publications about ethical considerations in wastewater surveillance, which is really encouraging to see and always happy to see them sort of leading that and thinking ahead. But again, that's not particularly centralized. And with state and local public health initiatives, those are also incredibly valuable. And on the news team, we want to learn from those. Every community will be different, but what are some central themes that we can find? And then on the legal side of ethics, the legality and sort of ownership of wastewater. So when you think about consent, when it's human waste, who exactly consents to that sampling? And currently it's the utilities, because it is human waste and the sampling point is at the utility. And the EPA's Clean Water Act has some sections that are applicable. And they gave a great presentation at the Water Environment Federation conference a few weeks ago. So, oh, I'm sorry again about this graphic, but there are some major ethical considerations that I've already mentioned, but just sort of major themes that we've seen so far is understanding the shared benefits of surveillance, including how these data are used, but also posing those relative to potential risks. And a lot of these center on privacy, very little in life has zero risk, but this is part of the WHO guidelines on public health surveillance as well, is really communicating the shared benefits of these surveillance programs. The opportunity and responsibility to address health equity issues and not only address them, but learn from the communities that are impacted by health disparities and better tailor systems to serve their needs. Also respect for individuals and balancing that with a need for privacy. One beautiful thing about wastewater surveillance is that it is essentially a large community pooled sample. And so there aren't too many privacy concerns. We don't sample at a household level or even the neighborhood level, but just considering that and really communicating that to the public, that can be really important. Sometimes it's just getting the answer to your question that you need. And so thinking about that in wastewater surveillance ethics will also be really important, especially when you start thinking about facility level sampling. And then of course the responsibility of those performing surveillance, the transparency and proper governance behind those structures, and then justice when it comes to misuse or violations of trust. How will that be handled? And so finally I'd like to end on a positive note that we have an opportunity to build an ethical integrity from the beginning. We have been building at these systems for a little over a year and in some cases even more, but we have learned from history that public health has incredible benefit on or that and acknowledge it. And while future use might be unpredictable, we can't exactly know where technologies are going. If we have regular review and input from served communities, from experts in public health and environmental health, then we can hope to have these ethics evolve alongside the technologies so that we can have a really strong trustworthy system that the public depends on for their data. And with that, I'm happy to answer questions and thank you for your time. Thank you, Rachel. We've got about five minutes for questions. Scott, I see your hand up and go ahead. Sure. So Rachel, there's very nice presentation for it. I'm struck by the fact in Amy's talk, she talked about decentralized systems not really being included in this. Yet it strikes me as basically in the kind of the world south and southeast in particular, a lot of these inequities or really may lie in that division between decentralized and centralized systems. And so what consideration has that been given in your thinking? A good point. I think this is where, of course, we're going to have to learn from our partners in any ethical guidance that we create or that others create in partnership with us because there are so many different systems and as some folks may discuss later on, every community and every utility is different and complex. And so understanding those decentralized systems and their needs and what ethical considerations we may have to give to them and tailoring that, it may just be that our ethical considerations are very extensive and have different applicabilities. But that's something that we should certainly think about in the future. And one thing that I'm particularly interested in on feedback from the committee and from others is some of our local and state jurisdictions who have been engaging with ethical discussions with their community. They have community representatives, a little bit similar to IRBs where there's a member of the community who may be a trusted member who can then present community priorities, questions and needs. Very difficult to do that on a federal level, but I would really be interested to hear how that might, we may incorporate something similar to not only represent utilities and public health interests, but also really community questions and understand what their concerns are. So, but thank you for that question. That's something that we should certainly keep in mind in the future. All right, we've got three questions. Michelle, Rekha, and then Rob. So it would be a little bit delayed going into our next talk, but that's okay. We want to get those questions in there. So, Michelle. Thanks so much for the information that you provided. I just pick you up on the last question. I would like to hear more about whether you are currently hearing concerns from community groups from members of the public, or is this at more at the stage of kind of issues, spotting potential concerns, but not yet needing to be responsive to stakeholder groups that have emerged in the public. So happily it is a ladder. We're thinking we're trying to think more proactively about this. We actually have heard some great feedback from our implementing partners that the public really do value these data and Zach will talk more about our public facing data on COVID data tracker. But we think we've heard that one of the valuable aspects is that it's community level health. So really looking at your entire community and how COVID-19 transmission is going. There were some concerns early on at facility level sampling, and this was primarily with universities. Some jurisdictions had concerns about privacy. And so that's why I mentioned it, facility specific levels, particularly when you think about if it's maybe a correctional facility or something like a dorm, really understanding how that data will be used is very important. So while we haven't received much feedback on ethical concerns from the public at this point, we want to be forward thinking and start trying to incorporate them now so that in the future as we expand wastewater surveillance that we at least have that in mind. And there might be some concerns that come up in the future that we haven't even thought of yet. But yeah, so that's kind of where we are. Sort of proactive thinking. Rekha. So thank you, Rachel. It was great to hear your talk in detail. I was always working with you to enroll more sites from Virginia and thank you for your support there too. We have lots of sites in our commercial contract and under our community surveillance. And thankfully we got really good positive feedback from our public. We didn't, we never heard any concerns so far, but a lot of interest in this data. My question is that, like I just said, this data is very rich and we need to work on ethical framework and guidance. So, and you just mentioned we are taking a proactive approach than reactive. Can you just explain that a little bit more like where we are and how long will it take to set up a solid framework there? So on the CDC side, we're working on the news team to sort of better understand core ethical principles that we want to prioritize moving forward. But thinking about ethics as a whole, especially release of water surveillance. We are really interested in hearing from the committee and from others, potential solutions for an ethical evaluation or ethic considerations that is not run by us because we are running the national wastewater surveillance system and it would be inherently biased if we created the ethics for wastewater surveillance. And of course we are not the only stakeholder. There are so many other leaders are implementing jurisdictions. We're so glad to have Virginia's participation, but we want to have it as a balanced approach. So we would value having a seat at the table, but we're interested in finding the best possible way of having these ethical principles shaped by as many stakeholders as we can. And we can't of course include literally everyone, but we would value the opportunity and any ideas that people have on how we could best do that, how it may have been done with other systems so that we're not providing biased guidance. Rob. Thanks Rachel for a great presentation there. I was intrigued by your comments on future use, which obviously is very important ethical consideration. Are there any specific future uses that you're concerned about with these samples? So for example, reprocessing them for drugs, reprocessing them to identify individual human subjects. You might be in a particular catchment region or that kind of thing. Or is it more of a precautionary principle where you think there might be some future use that people would complain about, but you haven't figured out what that future use would be yet or is it a mixture of both of those? It's a little bit of both. As you mentioned, so if wastewater samples are archived, one of our considerations and concerns is who will have access to those samples? Who will be able to request access and how will they be used? As you mentioned, things like illicit drugs. In some ways, we're seeing epidemics of drug use throughout the country. And so it is a public health issue, but there are also stakeholders in that on law enforcement side. And we want to be mindful of that in that could these samples be used for something other than public health? And so illicit drugs is something that comes to mind. In certain cases, depending on the granularity, there could be pathogens that may be stigmatizing. That is more of a theoretical concern, because our primary pathogens that we're hoping to target in the future are things like foodborne pathogens and have a very clear public health use. But I think the central concern right now is sort of where that line blurs between public health use and other use, whether that's for law enforcement, whether that's private use of samples for some type of campaign. That is a much more theoretical concern. And of course, in the future, I'm not exactly sure what technologies would be available. So what could you glean from these wastewater samples? How could this be used in other ways? But one of the primary concerns right now is essentially what target would be used and essentially who would have access to the samples to look for those targets. I just want to ensure that any samples would be used for the good of public health and not for any other unintended use that could concern a community. Grace, thank you. And our last question, Lauren. Thank you. So my question is about samples that are not collected by news. We are, I mean, in my experience, we have researchers that would like to have samples. And I'm thinking about the, the, and, and, you know, the idea of having something like an IRB or some kind of committee that looks at the use of samples. And the way we deal with it now is we have, like at least in Houston, we have perhaps an MOU with another with the utility and the researcher that's looking into the sample. But I'm struck by, I think your presentation indicating that we really don't want to put more regulation on the utility. It's a lot of burden on them. People requesting additional samples and how the flow would go. Are we going to try to restrict who gets, you know, the samples that are going forward, not necessarily in the framework of the news system. I didn't know if you thought about that. So that is a wonderful point. I'm really glad you brought that up about avoiding more intensive or additional regulations on utilities. Sometimes when, sometimes when interacting with utilities, of course, when sampling starts, there's concern. Is this leading to another regulation? Is this something else that we need to start preparing for? And our goal at news right now is not to increase regulations on utilities. We value the samples that they provide us and we want to support them, but you are right that we don't want to start over burdening them, whether with requests for samples or with any regulations surrounding that. So I think when I mentioned bringing environmental health partners to the table, that would be important to bring utility partners as part of that, of course. And better understand sort of what would be the limitations. There may be a finite amount of sample and that may just be the case, especially initially. But that is something that would probably have to evolve as the system grows and is interest in wastewater surveillance grows right now. It's primarily focused on SARS-CoV-2, but if it does blossom into foodborne illness surveillance and there's significant interest in that, we would probably need to review the ethics surrounding that and also the capacity of utilities and keep that in mind. So that's a really interesting point. Thank you for bringing that up. That's something we should certainly think about. All right. Thank you very much, Rachel. That was very much appreciated. And the question is very helpful as well. Next, we're going to hear from Zach Marsh. Zach is an epidemiologist at CDC and is going to speak around data analysis and use. Great, thanks all. Let me just get my video started. Okay. So I'm going to keep my video on, but if we start to hear any degradation in audio, just let me know. I'll cut it off. So like I was introduced, my name is Zach Marsh. I'm the data and epidemiology lead at the national wastewater surveillance system. And I just want to give a really quick overview of our data analysis and use. So you've heard a bit more about the implementation of wastewater surveillance, but what happens whenever data gets to us and what do we do with it? Let's see if I can get it. Yeah. So a slide you certainly haven't seen today already, but really what I'm going to focus on here is in the upper right quadrant. So from the data submission components all the way over through data analysis and information sharing. And so what are we doing with these data? So as Amy mentioned, one of the first considerations and really key values that news brings to wastewater surveillance is this national standardization of data. So making sure what's being captured and compiled by CDC is comparable. The methods may be different, but the data that we are collecting is similar. It does go through the same data quality standards. It does get the same exact data quality review. And it really helps to make comparison of these national level data much easier. So what I've highlighted here with the graphic is just the different components that we capture with news data. So we get information on the wastewater treatment plant, where it's located, what its capacities are, what additional inputs there may be into that system. We get information about what is, what are the factors at sample collection. So what the day is, what pH conductivity other factors, what the flow was at that system during that time, the type of sample that was captured. And then we get information after the sample has been submitted to the laboratory on what exactly they did to process that sample to get the SARS-CoV-2 concentrations at the end, which is really the final piece of information. So we get test results and all the associated metadata with that PCR result. So after all these data components are compiled into our submission template, we receive it in our data platform at CDC. Once it's uploaded, it goes through some automated quality control checks that are applied to all data in our system. And so if they don't meet the data standard, they're not going to pass along to our analysis, which is also an automated component. So once we have these error-free data, so we've gone through the quality control check, we have an analysis pipeline that's built within the same platform that data are submitted and visualized. As soon as these data are received, they initiate in the pipeline. This is about as near real-time as any surveillance platform I've worked on. It takes probably from submission to visualization 15 minutes to an hour to be able to see the most recent samples that a utility or not a utility, a jurisdiction is uploading to us. So the first step whenever we start to analyze these data are to take the concentrations and to normalize them. So we have two different approaches there. We do flow and population normalization, and that really helps to account for the number of people contributing waste and also the flow rate. As many of you may know, there can be different contributions beyond just household waste. There can be industrial contributions. There can also be stormwater combined systems with sewage. And so understanding the flow is really important to know what dilution factors may be present within the wastewater system. We also can look at factors that we call human endogenous control. So examples being pepper mild model virus. And that allows us to have a bit of a control that is inherent in the feces that are excreted within the system. And so this helps us to get a bit more to a per person contribution of the SARS-CoV-2. So after we've done this normalization, we then go through and process some metrics that we have within our system. These metrics are calculated on the per site and target level. So we can receive PCR results for total SARS-CoV-2, which is what we often are talking about when we're discussing wastewater. But we can also look at specific mutations within the genome that are very specific to variants of concern. So Delta and Omicron. So the three primary metrics that we have, and you'll get to see some visualizations of those on subsequent slides. First and foremost, we have a percent change. So our wastewater level is going up or down within a given sampling location. This is performed by a time-based linear regression. So we take all the samples within the given time period. And we run a regression on them over the number of days to see what the trajectory of those concentrations are at that location. And then just to back calculate that from the linear regression, we multiply the slope by the number of days included within the time period just to oversimplify that. The next metric we have is a relative level. This is the most recent metric we've released publicly. It takes all the wastewater concentrations for a given sampling site since they started collecting. It orders them, and it gives a sense of how high or low are levels at a sampling location compared to how they've been historically. Looking at when sampling started is a really important consideration that I'll talk about in some of the challenges later on. But this is really helpful to understand where a given location is compared to where it's been throughout the pandemic. And then the final metric that we have is a detection proportion, so really simple. It's the number of samples that were tested. And then you divide how many of those samples had detectable virus. So among all the samples that have been conducted in the last 15 days, what proportion of those found SARS-CoV-2. And so this is not so helpful when you're in a higher incident state, but as we're getting to lower incident states as we are now, it really helps to drive home where and where you are not detecting coronavirus within communities. So this is one of the dashboards that we have available. This is our internal decipher dashboard, decipher being the data platform we operate news within. So I'm going to walk through a few of the key visuals that we have available to health departments to help drive those public health decisions that they have that they have to make based on these wastewater data. So in the top left, when you load up the visualization dashboard, we have your map with a national level map and each sampling site is represented by a point. And the point currently as shown in this figure is colored based on the percent change. And so our wastewater levels going up, which are the reds, the orange and yellows, or are they going down to represented by the blue colors. And so this gives a national perspective of what's going on in the community. You can zoom in to see smaller geographies. You can select points. And you can also change the metrics. So we can view all three of those metrics I highlighted on the previous page on this national level map. If you then zoom in, I'm going to use North Carolina as an example here. You can apply some spatial layers that we really think help to provide that context to what's going on in the community. So we've ingested some spatial data on things like vaccination, testing, cases, hospitalization, and we are displaying that at the county level. What you're seeing here is the proportion of the total population that has at least one vaccine dose. And so you can have these data underlaid with the wastewater testing data on top. So that may provide that additional context to understand why some communities are seeing increases whereas others are seeing decreases, or if there's anything at the county level that may explain why you're seeing clustering of cases or clustering of increasing wastewater levels that you might not be seeing in other areas of your jurisdiction. So if you click on an individual point, there's an entire charts pane that appears in the dashboard. And so that's showing a couple of the visuals in three and four. So the visual three is probably one of our most popular. There's a few different data components that are displayed here. So for this individual site, if you've selected one of these points on the map, you get to see the wastewater concentration data that's shown by the dark gray point. And the dark gray line, which is just a smooth line of the concentration in the back, the lighter gray color and bars. That is the sewer shed level case data. So that's something that Amy had mentioned in one of her answers. So some of our jurisdictions have taken their sewer shed level spatial data. So the shapefiles that represent those service boundaries and they geocode cases to those geographies. And what you get is you can see over time what the cases in that specific catchment area that we're sampling wastewater look like for cases. And so as you can see here, we're seeing that really nice leading indication with the Omer Compique in December and January at this site. Just before you start to see the respective increase of those cases in this sampling location. So, and then in number four here at the very bottom, this is displaying a heat map that we show within decipher for a given jurisdiction. So these are all of the North Carolina sampling sites. And what this is showing is a time series. So each line on this chart represents all of the samples over time for that location with the location you've selected denoted with this rhombus or diamond shape. And so this is a really helpful visual to see how wastewater levels presently, which is represented on the far right compared to what's being sampled over time since sampling started. And so this both gives you how things look presently within a site. But you can also compare sites that started sampling around the same time periods to each other to see if levels in some locations are higher than in others. So in addition to this deciphering dashboard that I just walked you through, we have three different data products that are used by different audiences and they were designed as slightly differently for that very reason. So the decipher dashboard provides much more granular data. You can really dive in to super specific location information. You can see the sewer shed shape files that represent where these locations are geographically. You can see full time histories. You can download data from our dashboard if you want to do additional analysis of jurisdictions to use it on their own internal dashboard. We also have a COVID data tracker. So this is our public facing wastewater dashboard. It's just went through its second revision. So we now show percent change, detection proportion and the most recent additions we had. We're showing the current level. So how high or low or wastewater level compared to how high they've been since December 1. That's shown in the top right screenshot there. That's a screenshot of our COVID data tracker site. And then we also have a couple of different weekly reports that we put together for agency and White House level leadership. So there's a weekly report that we run that's been generated and are and that's really used to keep national level awareness of what's going on. And then we have a bi-weekly slide we put together for the White House that highlights some areas that we're keeping a close watch on based on their current wastewater trends. So these different products have different audiences. And as a result, the intended use for each of these are different. So the internal dashboard really for health departments to see what's going on in their data and use it to drive that public health action, which is why we provide many different data sources together in the same location. COVID data tracker is really for public to use this to inform their own decisions to see what's going on in their communities or the neighboring communities to make the decisions that are best for themselves and for their families. And then these weekly reports really are meant to just provide high level awareness to agency leadership and White House COVID leadership to make sure they know what wastewater data are showing, where areas might be emerging as far as disease goes, and then use that to inform decisions and other recommendations that they might be putting out. These data as great as they can be with all the benefits Amy has highlighted, such as the healthcare agnostic nature being very quick to get actionable data. They're not without challenges and issues that we have been working through trying to solve. The big one is the data comparability. So in addition to just variability with testing methods, there's also variability in wastewater infrastructure. So the systems themselves have different times of residence. So the time it takes to go from a household to a treatment plant may differ. There's different inputs like I highlighted, such as stormwater or industrial waste that may have chemicals that degrade the viral RNA. And then wastewater is a community sample. It's a pooled sample. And it's based on the input from that community. That includes people who may be traveling to a community that don't live there. So tourism does impact wastewater surveillance. And so it's important to keep that in mind. As I showed on the relative level heat map, you can see that within a given jurisdiction, when a sample was started or when a site started collecting wastewater, it impacts what the current picture is. So if you look at these top sites, they started sampling much later than some of the lower locations. And so they didn't necessarily sample during these peak periods of Omicron. So what they're seeing right now as relatively high might be different from those who sampled during the peak of Delta or Omicron within the pandemic. And so they don't have that pandemic memory is what we've coined that other sites may have that have been sampling for longer. And that impacts interpretation. And then it's important to keep in mind when looking at the data coverage. And then the last challenge is really just data coverage. We've highlighted and talked about the variability and geographic coverage. But there's also sampling frequencies that can be challenging to some sites do as high as five or seven samples per week. And others do one sample that we get per week. And when you do these analyses, sometimes that's not apparent. And so it's important to keep in mind that these coverage and also the temporal coverage may vary. Amy highlighted the septic challenge that we have where these people may not be able to be covered. There are also challenges with aggregating to higher geographies that many in public health often like to do of giving county level summaries, state level summaries, national level summaries of what's going on. And because of the variability in spatial and temporal coverage, that can sometimes be a challenge. That's why we often look at wastewater data on a site basis because this is a hyper local metric. And it's really important to keep that in mind. There are ways that we're exploring to aggregate, but it does present challenges on how to make sure it's representative of the number of sampling locations within a given geography. And I think that one more thing. So obviously there's been a lot as Amy's highlighted that we've done as a system, but there's still much to be done. And much we can do. And so some of the future things we have ongoing or we plan to incorporate in the future. We're looking at ways to evaluate different percent change calculations to account for sampling, frequency, factors that are intrinsic to the wastewater systems to make these percent change calculations a bit more comparable, maybe less sensitive and lower incident settings. We also are looking at ways to use lessons learned from other systems that have developed these alert algorithms to identify meaningful increases. So we're working closely with our syndromic surveillance colleagues who have some really great algorithms they've developed to identify what meaningful increases may be occurring in wastewater and to be able to parse out that noise that sometimes it's inherent in an environmental signal at a community level like wastewater. To address some of the tourism challenges or population movement challenges, CDC is acquiring some mobility data sets. And so we might be able to use that in the future to observe what's going on in these communities to see if mobility and increased tourism might be accounting for increasing levels or to discount that is happening and maybe help us to confirm that what we're seeing is based on the residents of those communities. And then the last thing that we're actively working on right now is the ingestion and analysis and visualization of the wastewater sequence data that many jurisdictions in our commercial contractor are collecting and then pairing that on the same internal wastewater dashboard that we have with the PCR testing data. So it provides that complete picture of variance in addition to just levels and trajectories so that people understand what might be driving the different levels and concentrations that they're seeing. So I think that's all I have and happy to take any questions given the time. Thank you, Zach. Very helpful and and certainly our committee members have learned how to game the system by raising their hands before the presentation is over, which is the evidence that we have a very intelligent committee. Scott. I jump in a chomping at the bit here on this one. I've got several questions for you. I'll try to streamline them into two lines of thinking here. One is the normalization of data and I'm curious if you can provide us some information around this. Why you're using a biological indicator. Like mild model pepper virus, which has natural variability of a couple orders of magnitude in addition and how you're factoring that variability into your normalization. That's my first question. The second question comes down to the matrix effect really in the individual samples and you said there's a significant amount of variability within the individual infrastructures, but the actual can be a significant amount of variability between samples as well with precipitation and just overall wastewater strength. And that can lead to single samples that have significant signal increases, particularly if you're normalizing with with indicators. How is that have an effect on your, your, you know, initially people talked about trends analysis, but single samples that are three orders of magnitude higher may drive that trend for a very long time, even though it is a spurious result potentially. And then I also have this question that the metrics piece. A lot of the utilities have changed methods since they began as methods have evolved. And so how is that accounted for in that overall historical metric that you're using? So those three questions, the historical metric, the normalization data and the matrix effect on single samples. Yeah, those are, those are really great questions and they're certainly the things that we've been trying to tackle. To I'll answer them in order in which they were asked if I, if I can recall them. So getting back to the human endogenous factors. We actually don't restrict it just to one. We allow a variety of different factors to be reported. So anywhere from chemicals to microbial indicators. And so we haven't really done an in depth analysis as to the the variability within those. That's certainly something that we're, we're wanting to explore in the future. It, it certainly does have an impact. I think some are looking at things like caffeine or, or other factors that maybe have a more stable lifespan or stable coverage and are not dependent on diet as heavily on those. Certainly caffeine does have a diet dependency, but it's something we'd like to look into more. Regarding the variability of the matrix. Yeah, the trends factors actually something we're looking at now with some of our statisticians to see if they're, if we incorporate these additional infrastructure level factors and in different contributions to the wastewater network does that help to smooth out and, and eliminate or at least dampen some of the noise that are inherent. If you look at the point level data on that figure I showed, you do see spikes here and there that sometimes we're not clear if it's a factor of the system, if it's an actual signal or if it has to do with population changes. And so something else I'm really curious about is if we can use mobility data to get a better grasp on what the true quote unquote population served for the system is rather than just relying on what utilities report to be their population serve because having that dynamic value might help us to better account for large changes in swings in population. And then the last one regarding the methods. We do actually account for changes in major lab methods. So a jurisdiction will report if they've changed an assay or they're using a different supplier or whatever it may be that they feel has deemed their results in comparable now. And so we use this field called major lab methods within our analysis and we don't analyze samples that were captured using different methods. And I didn't show an example of a site that has changed, but we do have some states that are starting to consolidate their testing within their own state lab. And so we're not going to be showing compared metrics or other trends for those sites that have since changed the method. We only compare data since the new method has begun. And you'll notice on our COVID data tracker site, if you click on sites that have since changed, the concentration time series won't be available for those locations just so you avoid that direct comparison of locations. All right. Thank you. Just so those of you joining, we're going to go till about 15 after because we do want to get all these questions in as well. Christine. Thank you, Zach. That was that was really, really exciting. And I think the dashboard work that you've done is incredibly impressive, especially over such a short period of time. I think we're going to be really interested in the data that you were showing around the lead time between the detection and wastewater in cases reported in the community within sort of that sewer shed as you're describing it. And I'm wondering if you have any data on sort of the, the number of days, if there's variation in the number of days by state or utility and just sort of what that, what that metric is looking like. Thank you. Yeah, that's a, that's a good question. We at the very early stages of the system, probably back in 2020. So before we had all the variability and we, we had new variants and everything, we had looked at the timing of wastewater in cases. And it was found that around four to six days is the typical lead time you would see with wastewater relative to the case data. We think there's been some change, obviously with different surveillance biases that are inherent in the systems. Different states have different case reporting capacities. Their clinical testing may be very robust. And so they may get results very soon. And obviously that's going to shorten the time that wastewater has on clinical testing. If they've established near real time data transfers, if they have really quick, rapid antigen testing or other mechanisms to get these data, that that's going to make wastewater less of a leading indicator. But I think on, on the average, especially now that we're seeing less clinical testing, wastewater is certainly being viewed much more I guess closely because the, the data that are available out there for clinical testing are becoming less and less reliable. The reporting frequency over just the past few weeks we've heard has decreased in most places to weekly. And so that certainly means we need to reevaluate how wastewater leads other indicators. Michelle. Thank you so much for your talk. I was interested to learn more about the data that are currently accessible to any member of the public. What can they download? And at what level of geographic specificity? Yeah, that's a good question. So we, we have a few mechanisms for people to access data. The easiest that doesn't require them interacting with us at all. Is they can download the current data that are shown on the COVID data tracker site. So all of those metrics at the sampling location level. We're not providing the names of the wastewater treatment plants themselves. We anonymize that. But it is whatever data is shown on that, on that dashboard is available for download. We also have two data repositories on a public data sharing site called data dot CDC dot gov. So the full history of what's being shown on COVID data tracker, including the smooth wastewater concentrations at the sampling site level are available for download there. And then we also have a public data request process where people can request much more detailed information. We still anonymize the wastewater treatment plant, but they can request the full, I think it's around 200 analytics variables that we have available, including the laboratory metadata, the wastewater infrastructure metadata. And I think we filled, fulfilled around 10 or 12 data requests to the state. Most of them have been academic researchers, but anybody in the public just has to sign a daily use agreement. And then we released that to them. All right, Rekha. Thank you, Joan, for a wonderful presentation. My question is kind of, I think you touched a little bit on that in your last slide when you were talking about future plans for a data trend. So on COVID data tracker, you are right now showing 15 day percent change trends. And some of the jurisdictions, they are not getting samples bi-weekly. They are still collecting once a week sample. So for those jurisdictions, we cannot see any data on COVID data tracker. There are gray bars, gray dots, and we get lots of questions from our community partners, our stakeholders, curious Virginians. They always ask why we don't have any data, you know, reporting or something like that. So are you planning to change that to something different so that you can accommodate all the jurisdictions? Yeah, we talked a lot about the time period that we wanted to represent data on. And with that percent change metric, we were really, we really wanted to make sure data were represented on a timely basis. So what's the most recent data we have? We, I think the biggest concern we have is if we show longer time periods, then what does someone do with that? If you're showing data from a month ago, like how informative is that, especially in a pandemic that can change so quickly? We are talking about in the third iteration to allow some date modification so you can select different time periods to look back over time, which would increase the amount of recent data available and so current metrics shown. But I really think part of why we focused on such a recent time period is really just to make sure that people are seeing up-to-date data, knowing what's going on in near real time in their communities and not showing data that are old that may be confusing, especially if we allowed the most recent sample for a location to be shown, there's a lot of confusion that could result there if that's a month and a half old and they're looking at data that are three days old. And so knowing what the most recent results are, we really just had to be pretty strict in enforcing the more recent data only to be shown. Hey, Zach, I'm just struggling back to the limit of detection problem or challenge that Amy brought up in her talk and I noticed in your talk how the LOD can affect some of this data interpretation, would you guys ever have interest in recommending a target LOD or LQ? I don't know if we'd make recommendations that probably I'm going to start talking over my skis here and probably best to have Rory or someone on our lab side answer that, but we do capture the limit of detection for the assays within our data and we consider that when we're doing analyses if something's below the limit of detection, which as you're alluding to can't be quite variable depending on the assay that's being used. I think once we are moving toward this digital platform, I think we're going to get to a better state where we can start putting those frameworks in place that do have minimal requirements, particularly as we're hoping to be able to have more comparable lab data in the future. There comes with the ability to have a lot more specifications, but I don't know if we're at that point now with the wide array of methods that some assays and some instruments just may not be able to achieve an ideal LOD or LQ that we would want to have. Neroj? Zach, I had a question kind of similar to what Christine asked, which is if these data were say a leading indicator of hospitalizations or debts rather than say just some cases, do you have a sense of like how sensitive or specific they are? So for example, if in an area I find debt spike, can I go back and see a red bar three weeks before that spike in debt and what is the fraction of times I would see a red bar or the other way around that I see a red bar, what is the chance that I'm going to see a spike in debts or hospitalizations three weeks from now? Yeah, the severe disease question is always the big million dollar one. I think the challenge with wastewater data are because it is healthcare agnostic and we can pick up symptomatic and asymptomatic cases. The challenge becomes parsing out what proportion of what you're detecting is from those who don't have symptoms, that they're less likely to seek care, that they're less likely to be hospitalized and die. So I think when we want to start thinking about can we project out severe disease, looking at variants or mutations of high consequence, so there's lots of talk about being able to look at mutations that may be able to escape treatments or may in the future have like vaccine escape. I think that's where we can start to look into severe disease, but I think when we just look at this pooled sample without understanding more about what the proportion of symptomatic versus asymptomatic cases, we can maybe use vaccine and vaccine coverage to serve as a bit of a proxy for protection, but even that sometimes is difficult to extrapolate out. It's certainly something we're talking about beginning to explore particularly as we're getting into sequencing as to whether we can use sequence data to inform the likelihood of what we're picking up to result in the severe disease that you're talking about. But just to clarify, is there any current analysis that shows that these data are good for predicting severe disease? Not a strong analysis that I've seen. We know that the time period is roughly a couple weeks to three weeks from spikes in wastewater to the representative spikes in cases. Most of that is just the correlation to case spikes and then it takes a little bit longer, typically about a week or so for those people to start showing up in the hospital. But I know I haven't, but I'm not sure if others have begun to investigate whether there's that representative spike in that severe disease component of hospitalization and death that we're seeing in wastewater. It's certainly one of the things that we're starting to look at as we're identifying these meaningful thresholds and as we look back, I think we're going to start to evaluate things like that to see if there are those correlates with these severe disease. Thank you. Lauren. Hey, Zach. I wanted to talk about specifically one of our tasks is to discuss broad approaches to increase the public health impact of wastewater surveillance. And when I was thinking about your talk, you mentioned briefly about sampling. And I wanted to, I guess, get your thoughts on both the temporal frequency and variation in sampling by space. We've seen papers out there that indicate, you know, if it's a vulnerable community and we don't have really good indicator data for them, maybe those are places that we should sample more frequently than, say, a community that has, you know, well established, either the vaccine race, like you just mentioned, or our good syndromic surveillance or essence data. We really kind of know what's going on in a community. Maybe we could take our resources because, you know, we have only so much funding. And step back on the communities that we really understand and sample them at some frequency. And if we see something happening in one of these vulnerable communities, we step it up, you know, maybe trigger something. Is that something that you're thinking about? I don't know if it's something we've explicitly thought about scaling based on need. I know we certainly are being very intentional with this commercial contract to talk, to try and focus on more vulnerable communities. So two of the factors we considered when we were identifying priority sites, we're looking at counties with lower vaccination coverage. And then we also looked at CDC's social vulnerability index to see if we could make sure we had really good representative coverage of those communities, like you're saying, that are more likely to be impacted by increasing COVID transmission. I think it's certainly worth considering, particularly with upstream sampling, when maybe a community is looking at doing central testing more frequently at the plant, and then as pockets of disease or maybe being identified or there's concern that pockets of disease may not be captured as well with other indicators, we can think about upstream sampling. I know you all in Houston do lots of smaller scale sampling throughout your city to make sure that you have adequate coverage. And so it seems like a combination of the two might be helpful, looking at both just traditional surveillance coverage, access to healthcare, and then what the vulnerabilities may be in those communities to help prioritize where the sampling occurs. I think what's really important though is just the duration and consistency of sampling. If you don't have that historic perspective, it can be challenging sometimes to know if what you're seeing is a meaningful trend or if it's just an artifact because you just started sampling there and you don't really have a good grasp on what the baseline levels are, especially as we start thinking about what COVID gonna become just an endemic disease and we have a better handling on what typical expected levels might be. Right, so I just had this really quick follow up on sneaking in. Have you, when you, so it sounds like you have a lot of data where there are people reporting on the weekly basis and then you have ones that are reporting twice a week and have you done a cross-validation where you can see like, was there like a great impact of the two times a week versus the one? I think we landed on the twice weekly because we found it has, there's better responsiveness to the data and that's why we've encouraged twice weekly sampling just because with most of our measurements they're either time or measurement based. So obviously the more frequently you're sampling, the more frequently you can have new trends that also can introduce additional noise. So we felt really honing in on twice weekly sampling gave a good balance of not only sensitivity of the assays and the system but also it was not overwhelming for the utility operators. They're completely new to public health surveillance and so keeping in mind their capacities and willingness to participate not burn them out was also a consideration. So we don't have any great data on within a site if they've changed different sampling frequencies of whether or not it improves resolution of what's going on. But I think that's a really interesting evaluation that maybe some of our centers of excellence or other groups that we're looking to fund in the next cycle can start to investigate. All right, Christa, last question. Okay, hi, thank you so much for talking and I'm just going to build on this resolution question on the public facing website. You know, is there, does it currently show how many data points have gone into those trends? Because I'm thinking, you know, the people who look at the website might trust a trend more if it's based on daily samples as opposed to weekly or bi-weekly. Is there a way to, do you show that and if not, why not? We don't yet. It's something that we need to, particularly as we're showing percent change because sometimes a location represents two samples over that 15-day period and others represent as many as like 10. And so that's something that we're going to probably be adding in the next round is that and also showing the most recent sample date just so it's clear of when in that 15-day period the most recent sample was collected. All right, thank you. And thank you again, Zach. We're now going to break into one o'clock Eastern time and then we'll reconvene. So thank you all of the presenters this morning. It's very, very, very helpful. Right. Welcome back everyone. We're next going to hear from several of our speakers around some of the challenges and concerns regarding implementing a national wastewater surveillance system. Before I introduce my first speaker, our first speaker, I also want to give Marissa the chance to introduce herself. She's just joined us. She's a committee member, Marissa. Hi. There you go. Okay. There we go. Better. All right. So yeah, hi, I just wanted to say hello. I'm a faculty at the University of Michigan in epidemiology and complex systems. And I don't know how much introduction I'm just to get. Is that the whole deal or more? I do a lot of work on mathematical modeling, including of infectious diseases, but also of wastewater surveillance and that kind of thing. And I've, I've worked on polio and COVID with wastewater. And yeah, that's, that's the, there we go. Perfect. Perfect. All right. Thank you very much. Yes. I want to introduce Nathan lacrosse. He's with the Utah department of health. And he's going to speak to us about the opportunities and limitations of syndromic versus wastewater disease surveillance data. So Nathan. Hello. Thank you. Hopefully everybody can hear me. Appreciate the invitation to speak. So I'm going to be talking a bit, as Guy mentioned about wastewater surveillance, a little bit about syndromic surveillance. I manage our wastewater surveillance program. I'm not an expert in syndromic surveillance, although we do have some very, I think, fruitful and encouraging collaborations with our syndromic surveillance program on going that I can speak to. So I'll get. Let's see. Too far. There we go. Okay. Just a little lag. So very briefly, I want to describe what we're actually talking about with wastewater surveillance and syndromic surveillance, wastewater surveillance. Amy Rachel and Zach did a great job of introducing a lot of the concepts. So I won't spend too much time on that. And I do want to mention that the points I'm going to be talking about are going to be related to the systems and data we have in place in Utah. They're going to be different in other areas, of course, naturally speaking. So with wastewater surveillance, as has already been mentioned, we're largely talking about community level surveillance, although it certainly can be done at different levels down to single buildings, potentially, really anywhere you can sample. In Utah, currently it's all community level surveillance of quite a wide ranges of community sizes. The data and samples typically they're collected new. You're not making use of existing data, although with a caveat that whenever possible, it's good in my opinion to try to piggyback on existing sampling schedules that the partner facilities are already conducting just to ease the burden, streamline things, make things even more efficient. But typically this is new data. They're new samples. It's not something that already exists. You're collecting it for this purpose. And the Turner on time varies. You know, Zach gave some national perspectives in Utah for us. It's typically in the neighborhood of two to three business days between the end of the sample collection period. And when we actually have the data in our hands, which is pretty good. I think there's not much we can do to really reduce that down too much. So drama surveillance, rather different, of course, this is typically something that is hospital health care related. What specifically it is can vary quite widely in Utah. We're specifically talking about emergency department visit related data. So just emergency departments. These data are already being collected typically for at the very least the hospital health care facilities own purposes for their own internal reporting purposes, building purposes, that sort of thing. And you can make use of quite a different variety of data that comes out of those facilities. Again, in Utah, we're specifically talking about typically some combination of chief complaint and initial discharge diagnosis data. Most of our systems for syndrome surveillance make use of both of those for COVID specifically. It's the initial discharge diagnosis just because chief complaint. There's too much crossover between influenza and RSP that to be terribly useful. And in Utah, again, this is what I've experienced with here. There's typically a lag of around three days for most data to enter our system, although some of it does trickle in as time goes on. However, most of the analyses make use of. Averages over a period, often a week or seven day averages. And so the time between. Some event occurring, and then you being able to see that change in these averages is more along the lines of somewhere around a week to a week and a half thereabouts. And then the maps are just on the left of map of our currently sampled wastewater facilities with 32. And a map of the emergency departments that are part of their syndrome surreal system, which is 49, which is all of them, which is great. Then. So want to advance. There we go. So briefly strengths and limitations like all data systems, surveillance systems. Each has their own set of strengths and limitations. I think the takeaway to put that first is that in a lot of situations, these are really complimentary systems and that they can be used to strengthen each other there. One strength can help overcome other's weaknesses and vice versa. I don't really see much conflict between the two. I don't see much conflict between the two. I don't see much conflict between the two. I don't see much conflict between the two. And again, these have really been covered. Very, very well by our wonderful CDC presenters earlier. So that move pretty quick here. They are very independent of traditional public health data sources, things like access to testing and healthcare seeking behavior. I know Amy mentioned this as a really important point. I completely agree. I don't. I think this is something we knew going into this. And it's something that has become. It's really important to include, to include the changes in individual level testing in recent weeks. It also captures all shedders, including asymptomatic cases. Now, not all people will shed necessarily, but everybody who does, we're going to get data from them, which is really important. So it's not dependent on people being symptomatic or their severity of their symptoms. It's also very efficient. We are currently sampling. At these 32 facilities statewide twice per week. twice per week, and that covers about 88% of our state's population. And that's just not something that's feasible with individual level testing. It's just not something that could be done, but we're able to do it sustainably and consistently. And as also been mentioned, there are inherently pooled samples. And I kind of put this in the middle of a little bit of extra gap, just because I think it's, it can be both a strength and a limitation, depending on what you're trying to do. So if what you want is to get data about a given community in your sewer shed, your wastewater service area, reasonably covers that community, then great, your sample's already there. You don't, there's nothing else you need to do. That being said, if you want to get more granular, you can't do that with that type of sample. You need to get a different sample, typically from a different sampling point or a different sampling area, because you can't unpool it. It's inherently pooled. Some of the limitations, there are a lot of unknowns. There's a lot of things we don't know, a lot of very open questions, especially the research side, I think. And the data can be quite volatile, but you can see quite a bit of variability in the data. And it's not wholly clear all the time whether these are reflecting actual changes in the community that we might want to know about and pay attention to, or whether it just represents changes in the wastewater stream, changes in temperature, weather, storm events, something like that, that changes our data, but it's not something that is of public health interest. And that could be difficult to deal with. And as Zach mentioned, and this is also very, very true, there's a need to establish a certain amount of baseline data, so you know what data from at its site looks like, and you can start determining what it might mean, because each site has enough unique factors that you can't necessarily take conclusions and thresholds and metrics directly from another site and apply them to a new one. A lot of times, it's just not going to be valid. And so you need to collect data over enough period of time, so that you start having an idea of what you're actually looking at and talking about. It could be quite difficult to aggregate and compare data between sites because of all those differences. And in a lot of situations, best practices haven't really fully emerged yet. I think this is changing over time, thankfully, as people get more experience and we try to do methods, we start figuring out which ones are working even better, which offer more advantages and other methods. But it's a new area, it's a new science, and so we're still figuring things out. And as also been mentioned, septic use can be an issue more so in some areas of the country than others in Utah, a lot of the infrastructure is relatively new. So our septic use is on the lower end in other areas with older infrastructure, especially on the east coast or the Midwest, that can be a significant problem. With syndromic surveillance, it's also independent of testing, which again, extremely, extremely valuable, especially in times like now. The status of individual testing really doesn't bear much influence on the data you get out of this system. And it's also a mature system with really well established methods. And this is very valuable, especially on the wastewater side, as we're trying to figure out some of these best practices. And we're hoping to be able to take some of the experiences and methods and lessons learned from syndromic surveillance and apply them to our systems. And it's already, it's very efficient, very similar in many ways to wastewater surveillance, because especially because the data are already being collected for the most part. So it's just getting that data and getting it into the system and databases where you can make use of it for public health purposes. But typically it doesn't involve collecting any new data, it's just getting the data to where it needs to be. And it's really individual level data still. So aggregation is relatively easy. You can do it like you would aggregate most other individual local data. So if you want countywide, statewide, what have you, that's really typically not too much of a problem. And in fact, like most individual level data sources, the greater the geographic scale you aggregate on, typically the cleaner and a little bit easier to deal with the data becomes as well. Very different from wastewater surveillance in that regard. Some limitations, it is inherently, of course, extremely dependent on things like access to healthcare and healthcare seeking behavior. And these things are not evenly distributed and access and seeking behavior are not equal across all populations, all communities and all groups. And this can be a real problem. And this is one that's tough to solve too, because it gets down to the root of issues with our healthcare system. And it can be very dependent on symptom severity. So these are people who had symptoms severe enough or some other issue that was severe enough that they actually presented to an emergency department, depending on which disease we're talking about, which variant we're talking about, that may not be most of the people. That doesn't mean it can't be useful. Oftentimes the trends will still remain the same. But it's important to keep that distinction in mind. And there's a trade-off between timeliness and accuracy when we're using things like the initial discharge diagnosis. You can wait for more accurate and refined data to come in. But again, you've lost that timeless, you're often waiting weeks, if not months to get that more refined data, which is great retrospectively. But when you need to make a decision now, it's not so helpful. So as I mentioned, I really ultimately, I think that these two surveillance systems are very complementary. They help strengthen each other. I don't, thus far we haven't had any conflicts between the two. And I don't necessarily foresee that in the future. I think if, even if the two systems are to show very different things, that's more likely to reflect actual differences in what they're measuring because they're measuring different points in the progress of an infection between shedding and having severe enough symptoms that you're going to the emergency department. They're measuring different things. I think it's likely to reflect real things that we want to pay attention to and would be of interest and not just artifacts of the type of system we're talking about. But we'll see as time goes on. And last slide, I want to talk a little bit about some of this very preliminary, very rough to put it mildly data we've been working on and systems we've been working on integrating these two data sources. So we have an ongoing collaboration, as I mentioned with CDC and with our syndromic surveillance folks here at state to try to find ways, commonalities, ways to bridge some of these gaps and to make use of a lot of the knowledge and methods that syndromic surveillance has developed over the years and apply them to our data and coming up with ways that we can combine these data to make them both even stronger. And so this is some work we've been also doing as a side project of that to help develop some readiness metrics for hospitals, so statewide metrics, hence the need to aggregate the data to statewide level. And it can be quite complicated wastewater has an inherent geographic scale is that covered us I think quite well where each site is unique enough that it becomes quite difficult to compare and aggregate data between them. So at that community level, it's great broader, it becomes more difficult, syndromic it's it's there's a different story it's a little bit easier there. And that's what I mean by each word best at different aggregation levels really thus far wastewater level works best quote unquote at no aggregation but a lot of times aggregation is necessary. So what we have here are two charts the top one is wastewater data the bottom one is syndromic surveillance data so emergency apartment visit data the on the top chart the blue and orange bar chart of the statewide daily case counts and then the or the red line are the number of sites with levels above a certain quantile threshold. So this is very much like those heat maps that Zach was showing the fact based on the same methods where we got I got the idea and just taking all the data from a site dividing it into essentially straight quintile thresholds bins and then finding which ones are at that 60% plus bin and counting the number of sites per day and it actually works surprisingly well as an indicator and even predictor of when we are going to see these surges in covid cases as you can see and in the bottom chart these are roughly lined up on the time scale on the x axis by the way is the syndromic surveillance data over a similar timeframe showing the percent of emergency department visits with thresholds at blue being less than 4% or relatively low for one definition of cases due to or severe cases due to covid that yellow is between 4 to 6% so sort of a middle ground and then the orangish pinkish reddish is greater than 6% or high community levels there and you can see very similar things so they can both work really well as indicators and predictors of changes in what we're what we would see in individual level cases even when we wouldn't have that data like now potentially and I think they can work really well in tandem too and provide more strength because if there's great value in having multiple independent data sources and if they're both showing you the same thing that's I think much stronger evidence and more stronger rationale for taking public health action than just relying on a single data source and that is what I have I would be happy to take your questions. I think we're gonna hold the questions until the presenters because I think there's gonna be some crossover and I think the questions then can be directed best at that point but thank you very much Nathan that was an excellent presentation. Next I'd like to introduce Jose Romero and Mike Sema from the Arkansas Department of Health bring their slides up. We don't have any slides we're going to just present our limited information. Thank you very much. I'm Jose Romero I'm the Arkansas Secretary of Health and the Director of the Health Department. I'm also a professor of Pediatrics, Pediatric Infectious Disease at the Medical Center and we're this is really our presentation is more about I think the adage bringing coal to Newcastle fits with this presentation because a lot of what we're telling you you already know so we are a rural state our population is about three million people and we have three wastewater sites currently functioning these are in small communities one of 1600 people the other one of 16,000 people one of these sites actually has two wastewater sites that are contributing to CDC data we submitted 13 proposals and only have those three on boarded at this time so 10 are not on boarded so the speed with which was talked about and again was talked about about getting these up these sites up did not really apply to Arkansas and let me just also preface with this I'm a big proponent of wastewater surveillance and I think it has a utility in a number of diseases not just what we're talking about which is COVID but I'll talk about that in a minute so my comments are not to detract from that they're simply to bring to light certain issues that we view here as a rural state so it's important to note that for our systems our wastewater systems many of these are very old and they're open systems they're not closed systems and they are diluted by rainwater or runoff during certain times of the year and that is going to be an issue that I think we will encounter as you begin to move into rural states and states that do not have these well-defined municipal water treatment systems it's also important to keep in mind that about 1.1 million of our population so about one third of our population is not linked to a municipal wastewater site they have septic tanks and they would not be captured in a surveillance system now the argument could be made that well some of those individuals will be working in town for example and therefore you'll catch you know their their their refuse that way but but but bear that in mind also note that in my state that is in Arkansas the site that has or the region of the state that has the lowest number of these wastewater treatment facilities and and the highest number of of of these home sites septic tanks is located in the southwest corner of the state that is sorry the southeast corner of the state the delta and that is the poorest portion of our state and therefore as you've already talked about this issue of disparity and and equity in in surveillance so that group of individuals may be disenfranchised by focusing on these large wastewater systems for surveillance and we and I bring that to your attention only um we also view other things such as cost so the cost to get this started um is significant now we invested significant amounts of money for nucleic acid amplification testing for COVID and we can do genomic sequencing now within our system that's our public health lab we agree fully that the public health lab should be the lab to to to perform these uh in in the state but there will be an outlay for concentrating the specimen for for nucleic acid extraction from these from these uh from these samples further COVID has really taxed our public health lab we are limited in space we had to expand dramatically in order to put into place systems to make to diagnose uh uh COVID rapidly within our state and so our health department lab literally has no space where to put these this this project now we're lucky um we've thought of a way of doing this and and we're going to employ a modular system that we currently have on site so um we we we think we can get this started I'll talk about that in a minute and lastly the issue of distrust may be very significant so in states like mine there is a significant distrust and loss of loss of confidence uh with the public health system and in particular with the CDC something that I hear continuously from our legislators and the legislators have taken an active role in in in the um in the regulation of public health in our state um you know we have we have mass mandates uh or really against mass mass use against use of vaccines so in some states it if there is a perceived um issue with security of personal data from this you could see uh in my opinion um a movement from the legislators to limit the ability of this use of this data that being said I think that this has a great future and let me let me give you two examples that I think are very important firstly an act a problem that we now have in the United States we have resurgence of tuberculosis we have had an a cluster of TB arising from arising from the COVID pandemic where they were not seeking care and that cluster was significant if we were able to surveil wastewater and find these genomes in the wastewater we could implement a contact tracing or active surveillance for TB in those sites another area that I think uh is going to be very important is in polio virus surveillance I'm I'm the chair of the national certification committee for uh for polio eradication we report to the to pacho who in turn reports to world health organization as you know or you may know there have been now outbreaks of wild type polio in Malawi and circulating polio virus in other countries so this is a threat to the US and it we do not have an active surveillance system for this at this time all this to say that I think that there is a great future for this we just need to figure out how to roll it out and I will turn this over to Mike Sima for his for his comments Mike I think you're muted can you all hear me okay sorry about that I have no idea between this morning and today but I will keep my comments very brief I I do want to thank you all for the opportunity to participate today I think first and foremost you know I've learned more today about the national wastewater surveillance efforts than I have in the past year or two combined which you know I appreciate but it's simultaneously a little bit concerning you know it's clear that there's much more information out there that just simply hasn't diffused down to states like ours and I think that we're missing an opportunity to use a powerful public health tool and I think if you know wastewater is going to be a successful endeavor at a national level that the connection with rural states localities like that in Arkansas needs to be much stronger and there's obvious avenues for those types of connections namely through ELC which we are going to be participating in but it does seem that we have you know we're jumping in at square one whereas others may have had you know early opportunities to be early adopters and I like most of my colleagues like to have the opportunity to be pioneers in the public health practice in science arenas. Other than that I thought Jack did a great job clearing up a lot of questions that I personally had about the data analysis component and you