Universal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts.

The spread of a communicable disease is a complex spatio-temporal process shaped by the specific transmission mechanism, and diverse factors including the behavior, socio-economic and demographic properties of the host population. While the key factors shaping transmission of influenza and COVID-19...

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Autores principales:	Yi Huang, Ishanu Chattopadhyay
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/9c16d47306da464e9f5aedac5531d64b
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spelling	oai:doaj.org-article:9c16d47306da464e9f5aedac5531d64b2021-11-25T05:40:32ZUniversal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts.1553-734X1553-735810.1371/journal.pcbi.1009363https://doaj.org/article/9c16d47306da464e9f5aedac5531d64b2021-10-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009363https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The spread of a communicable disease is a complex spatio-temporal process shaped by the specific transmission mechanism, and diverse factors including the behavior, socio-economic and demographic properties of the host population. While the key factors shaping transmission of influenza and COVID-19 are beginning to be broadly understood, making precise forecasts on case count and mortality is still difficult. In this study we introduce the concept of a universal geospatial risk phenotype of individual US counties facilitating flu-like transmission mechanisms. We call this the Universal Influenza-like Transmission (UnIT) score, which is computed as an information-theoretic divergence of the local incidence time series from an high-risk process of epidemic initiation, inferred from almost a decade of flu season incidence data gleaned from the diagnostic history of nearly a third of the US population. Despite being computed from the past seasonal flu incidence records, the UnIT score emerges as the dominant factor explaining incidence trends for the COVID-19 pandemic over putative demographic and socio-economic factors. The predictive ability of the UnIT score is further demonstrated via county-specific weekly case count forecasts which consistently outperform the state of the art models throughout the time-line of the COVID-19 pandemic. This study demonstrates that knowledge of past epidemics may be used to chart the course of future ones, if transmission mechanisms are broadly similar, despite distinct disease processes and causative pathogens.Yi HuangIshanu ChattopadhyayPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 10, p e1009363 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Yi Huang Ishanu Chattopadhyay Universal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts.
description	The spread of a communicable disease is a complex spatio-temporal process shaped by the specific transmission mechanism, and diverse factors including the behavior, socio-economic and demographic properties of the host population. While the key factors shaping transmission of influenza and COVID-19 are beginning to be broadly understood, making precise forecasts on case count and mortality is still difficult. In this study we introduce the concept of a universal geospatial risk phenotype of individual US counties facilitating flu-like transmission mechanisms. We call this the Universal Influenza-like Transmission (UnIT) score, which is computed as an information-theoretic divergence of the local incidence time series from an high-risk process of epidemic initiation, inferred from almost a decade of flu season incidence data gleaned from the diagnostic history of nearly a third of the US population. Despite being computed from the past seasonal flu incidence records, the UnIT score emerges as the dominant factor explaining incidence trends for the COVID-19 pandemic over putative demographic and socio-economic factors. The predictive ability of the UnIT score is further demonstrated via county-specific weekly case count forecasts which consistently outperform the state of the art models throughout the time-line of the COVID-19 pandemic. This study demonstrates that knowledge of past epidemics may be used to chart the course of future ones, if transmission mechanisms are broadly similar, despite distinct disease processes and causative pathogens.
format	article
author	Yi Huang Ishanu Chattopadhyay
author_facet	Yi Huang Ishanu Chattopadhyay
author_sort	Yi Huang
title	Universal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts.
title_short	Universal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts.
title_full	Universal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts.
title_fullStr	Universal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts.
title_full_unstemmed	Universal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts.
title_sort	universal risk phenotype of us counties for flu-like transmission to improve county-specific covid-19 incidence forecasts.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9c16d47306da464e9f5aedac5531d64b
work_keys_str_mv	AT yihuang universalriskphenotypeofuscountiesforfluliketransmissiontoimprovecountyspecificcovid19incidenceforecasts AT ishanuchattopadhyay universalriskphenotypeofuscountiesforfluliketransmissiontoimprovecountyspecificcovid19incidenceforecasts
_version_	1718414533697994752

Universal risk phenotype of US counties for flu-like transmission to improve county-specific COVID-19 incidence forecasts.

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