The effect of extreme rainfall on COVID-19 surveillance, the case of New York State

This thesis examines the impact of extreme weather on COVID-19 testing rates in New York State. Weather anomalies in temperature or precipitation were identified and their effects on daily testing analyzed. Initial findings showed a reduction in tests on anomalous weather days, which were challenging to quantify. To quantify this impact, regression models were used, considering as main ingredients the weather conditions, day of the week, and the underlying trend of tests. The findings highlight the effect of precipitation on testing rates. A Generalized Linear Mixed Model (GLMM) revealed heterogeneous county responses to heavy precipitation, with test variations ranging from 1.8% to -22.6% with respect to the testing trend. Additionally, counties with higher prevalence of diabetes and obesity in general population correlated with greater reductions. This study underscores the sensitivity of disease surveillance to extreme weather, providing insights for public health improvements. Future research should explore other regions and understand all the factors that explain this sensitivity to enhance global public health strategies.