About: Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity

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About: Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity
Creator	Almquist, Zack Butts, Carter Hipp, John Huang, Peng Luo, Xiaoshuang »more»
source	ArXiv
abstract	Standard epidemiological models for COVID-19 employ variants of compartment (SIR) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 U.S cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly non-uniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform healthcare planning, predict community outcomes, or identify potential disparities.
has issue date	2020-05-20(xsd:dateTime)
has license	arxiv
sha1sum (hex)	0873b49a9de223b8e20ed3b19f054e80f81f2a9b
resource representing a document's title	Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity
resource representing a document's body	covid:0873b49a9de223b8e20ed3b19f054e80f81f2a9b#body_text
is schema:about of	named entity 'areas' named entity 'models' named entity 'pattern' named entity 'epidemiological' named entity 'SIR' »more»

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