Investigation of the Spatial Correlation of Atmospheric Pollution and Meteorological Factors with Levels of COVID-19 Lethality

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The current COVD-19 pandemic is having a catastrophic effect on human health and global economies. In terms of groups most at risk, the elderly and those with underlying medical conditions are disproportionally affected by the virus, with a corresponding higher number of fatalities. So far, environmental factors affecting transmission and infection rates are under-investigated. Given that COVID-19 attacks the respiratory system, environmental factors that also impact respiratory health may leave an individual more prone to infection. To contain the spread of the COVID-19 virus, governments have enforced multi-phase restrictions on outdoor activities or even collective quarantine on the population.

Objectives & Challenges

This proof of concept study aims to gain a better understanding of:

  • Environmental exposures which may lead to increased Severe Acute Respiratory Syndrome infection rates;
  • The impact of lifestyle, health conditions, and vulnerability by age and sex;
  • The impact of lockdowns and physical distancing.

These environmental, statistical, and epidemiological parameters were considered as inputs of a model that aims to predict the contagion and lethality of Covid-19 in Ireland. A whole-system approach has been applied to millions of time series data based on the principal component regression (PCR) model. To develop the PCR model, the weekly contagion and lethality rates were set as the response variables. Forty-one explanatory parameters including, mean values of weekly atmospheric pollutants (i.e. PM10, PM2.5, O3, SO2 and NO2, and Indoor radon levels) metrological parameters (i.e. precipitation amount, air temperature, wet bulb air temperature, dew point air temperature, relative humidity, vapor pressure, mean sea level pressure, predominant hourly wind speed, and sunshine duration), Sociodemographic data (self-perceived health status, population, population density, distribution of sex and age profile) together with lockdown phases were introduced as predictors.

Main Findings

This research project studied the spatial correlations between atmospheric pollution, weather information, and social-demographic data with contagion and lethality rates of Covid-19 in Ireland. Ireland was selected as the case study because the extensive use of a large number of health and non-health technologies which have been employed, including diagnostic testing and the use of medical devices, allowed us to have a better understanding of the parameters that might affect Covid-19 spread. According to the correlation circle of the factors in principal components analysis, two sets of parameters were identified: 

  1. Those that affect the health condition like aging and health status;
  2. Those that affect the spread of the virus such as temperature and humidity.

A principal components regression analysis was carried out to develop a prediction model and to evaluate the contribution of input parameters on virus transmissions and its killing power. It was discovered that atmospheric pollution contributes in two ways: 

  1. By facilitating virus transmission;
  2. To a lesser extent by forcing extra pressure on the respiratory system (e.g. background radon activity).

The metrological parameters (especially air temperature and dew point temperature) were found to significantly affect both increases of contagiousness and killing power. Similar to what was anticipated, the elderly category was found to be the most vulnerable group hit by the virus. The health status as an indicator of the wellbeing of the immune system was found to have a key role in fighting against the virus. We found that the lockdown set by the Irish government significantly prevented the increase of covid-19 cases. Lockdown measures could have a secondary positive effect on the decrease of mortalities (i.e., by limiting the number of infected cases). However, the high occupancy of hospital beds during lockdowns and also the unwillingness of people to go to the hospital due to a fear of contracting the virus could actually increase the mortality rate during the lockdowns.

Main Recommendations

The models we developed were successful in predicting the contagion and lethality rates that were related to environmental and sociodemographic data. It seems that there are additional effective parameters that are not included in this study since the input parameters introduced in the proposed model do not cover the whole variation in the predictions. Therefore we would suggest adding parameters such as the number of deaths in care homes, the percentage of cumulative contagion and mortality among health workers, the degree of compatibility with social distancing, blood groups, and details about the number of tests per capita, when developing future models. The research methodology presented here can be effectively utilised for modeling Covid-19 contagion and lethality in other countries. The results of this project could also be extended and used for modeling other viral infections like the flu, Influenza, and Ebola.