<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Curbing Pandemic Through Evolutionary Algorithm-Based Priority Aware Mobility Scheduling."
Abstract:"COVID-19 is a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2. While swift vaccine development and distribution have arrested the infection spread rate, it is necessary to design public policies that inform human mobility to curb outbreaks from future strains of the virus. While existing non-pharmaceutical approaches employing network science and machine learning offer promising travel policy solutions, they are guided by epidemiological and economic considerations alone and not human itineraries. We introduce an evolutionary algorithm (EA) based mobility scheduler that incorporates the personalized itineraries of individuals to determine the ideal timing of their mobility. We mathematically analyze the computational efficiency versus the optimality trade-off of the mobility scheduler. Through extensive simulations, we demonstrate that the EA-based mobility scheduler can balance the trade-off between (1) optimality and computational cost and (2) fair and preferential human mobility while reducing contagion under lockdown and no-lockdown as well as even and uneven human mobility traffic scenarios. We show that for two human mobility models, the scheduler exhibits lower infection numbers than a baseline trip-planning approach that directs human traffic along the least congested route to minimize contagion. We discuss that the EA scheduler lends itself to intricate mobility schedules of multiple destination choices with varying priorities and socioeconomic and demographic considerations.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:

Curbing Pandemic Through Evolutionary Algorithm-Based Priority Aware Mobility Scheduling
COVID-19 is a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2. While swift vaccine development and distribution have arrested the infection spread rate, it is necessary to design public policies that inform human mobility to curb outbreaks from future strains of the virus. While existing non-pharmaceutical approaches employing network science and machine learning offer promising travel policy solutions, they are guided by epidemiological and economic considerations alone and not human itineraries. We introduce an evolutionary algorithm (EA) based mobility scheduler that incorporates the personalized itineraries of individuals to determine the ideal timing of their mobility. We mathematically analyze the computational efficiency versus the optimality trade-off of the mobility scheduler. Through extensive simulations, we demonstrate that the EA-based mobility scheduler can balance the trade-off between (1) optimality and computational cost and (2) fair and preferential human mobility while reducing contagion under lockdown and no-lockdown as well as even and uneven human mobility traffic scenarios. We show that for two human mobility models, the scheduler exhibits lower infection numbers than a baseline trip-planning approach that directs human traffic along the least congested route to minimize contagion. We discuss that the EA scheduler lends itself to intricate mobility schedules of multiple destination choices with varying priorities and socioeconomic and demographic considerations.