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

TL;DR

This paper shows how urban factors like humidity and population density affect the severity of diseases like COVID-19, using a model that works across cities and can predict case fatality rates.

Contribution

A novel probabilistic model that predicts disease severity based on urban environmental and population factors, validated across global cities.

Findings

A probabilistic model accurately predicts the case fatality ratio of COVID-19 using urban descriptors like humidity and population density.

The model's validity extends to other diseases like flu and pneumonia, showing consistency across pandemic waves.

Relative humidity and urban form are key outdoor factors, while housing density and elderly population are key indoor and personal factors.

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.

The Sars-Cov2 virus has caused significant disease in humans inducing the COVID19 crisis. Sars-Cov2 virus is airborne transmitted among humans through mucus laden expectorated droplets. Currently, the amount of virus per droplet is unknown but the role of relative humidity is documented as well as that regarding urban population concentration. Here, we propose a multiscale statistical model that can predict the COVID19 case fatality ratio that characterizes the disease intensity in urban environment from the zip-code level up to the global city scale. We found a simple equation based on 5 urban descriptors and a single constant factor called CFR0 that is the disease CFR corrected for the urban environment and urban population profile. Hence CFR0 is location independent. The statistical weight for each urban and population descriptor is adjusted once on COVID19 CFR borough data for 4 major US cities and then used for all other scales and other cities around the globe with no further adjustment with a very good level of accuracy showing large variations within a given city and from city to city. The urban descriptors were ranked in 3 categories: outdoors, indoors and personal. The dominating “outdoors” one is the relative humidity with a smaller influence of the urban form while “indoors” descriptors (number of housing units per building and number of dwellers per unit) are nearly equal to 50%; the only important “personal” parameter is the population older than 65 and not income level. We found that the COVID29 CFR0 is 10 times larger than that for the flu and comparable to that of pneumonia. Our study provides new insights into the deployment and intensity of the COVID19 disease in different urban environments.

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