Abstract View
Infection vs Fatality of COVID-19 in New York State: Effects of Demographics and Poor Air Quality
VIJAY KUMAR, Bridget Wangler, Chaya Chaipitakporn, Shantanu Sur, Supraja Gurajala, Suresh Dhaniyala, Sumona Mondal, Clarkson University, Potsdam
Abstract Number: 391
Working Group: Satellite-Data and Environmental Health Applications
Abstract
The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected the world at an unprecedented scale, however, its impact on different countries and even on different regions within a country varies widely. A multitude of COVID-19 related risk factors have been identified including demographic factors and chronic exposure to air pollution but the specific contribution of these risk factors to infection and fatality is yet to be understood. In this work, we compared the incidence of COVID-19 in New York State (NYS) counties, and analyzed how various risk factor variables associate with the variation observed across different counties. Using publicly available data, we found that the rate of COVID-19 infection correlates well with the death rate, both being high in counties located near the New York City, the epicenter for the infection, and drops prominently in counties located farther away from the epicenter. However, in terms of death among the infected population, several other counties take up the topmost positions even having a low infection rate. To investigate this apparent discrepancy, we divided the counties into three clusters based on the infection rate, death rate, and death per infection, and compared the contribution of various potential risk factors such as ethnicity, age, population density, poverty, and air quality PM2.5 in each of these clusters. Furthermore, a regression model built on this data reveals ethnicity (African-American and Hispanic) and poverty are the major risk factors behind high infection rate, while disease fatality has a strong association with age and PM2.5. Our results show distinct contributions by various risk factors to the COVID-19 burden, and this information could be useful in designing control and mitigation strategies.