Urban environmental and population factors as determinants of COVID-19 severity: A spatially-resolved probabilistic modeling approach.

Abstract

COVID-19 is caused by a severe acute respiratory syndrome due to the SARS-CoV-2 coronavirus. It has reshaped the world with the way our communities interact, people work, commute, and spend their leisure time. While different mitigation solutions for controlling COVID-19 virus transmission have already been established, global models that would explain and predict the impact of urban environments on the case fatality ratio CFR of COVID-19 (defined as the number of deaths divided by the number of cases over a time window) are missing. Here, with readily available data from public sources, we study the CFR of the coronavirus for 118 locations (city zip-codes, city boroughs, and cities) worldwide to identify the links between the CFR and outdoor, indoor and personal urban factors. We show that a probabilistic model, optimized on the sample of 20 districts from 4 major US cities, provides an accurate predictive tool for the CFR of COVID-19 regardless of the geographical location. Furthermore, we show that the validity of the model extends to other infectious diseases such as flu and pneumonia with pre-COVID-19 pandemic data for 3 US cities indicating that the first COVID-19 wave severity corresponds to that of pneumonia while other COVID-19 waves have the severity of influenza.When adjusted for the population, our model can be used to evaluate risk and severity of the disease within different parts of the city for different waves of the pandemic. Our results suggest that although disease screening and vaccination policies to containment and lockdowns remain critical in controlling the spread of airborne diseases, urban factors such as population density, humidity, or order of buildings, should all be taken into consideration when identifying resources and planning targeted responses to mitigate the impact and severity of the viruses transmitted through air.