Spatiotemporal tracing of pandemic spread from infection data

Abstract COVID-19, a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus, has claimed millions of lives worldwide. Amid soaring contagion due to newer strains of the virus, it is imperative to design dynamic, spatiotemporal models to contain the spread of infection du...

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Autores principales:	Satyaki Roy, Preetom Biswas, Preetam Ghosh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2bdc85663e6a40b5b67ece41f7ab6048
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spelling	oai:doaj.org-article:2bdc85663e6a40b5b67ece41f7ab60482021-12-02T15:29:02ZSpatiotemporal tracing of pandemic spread from infection data10.1038/s41598-021-97207-52045-2322https://doaj.org/article/2bdc85663e6a40b5b67ece41f7ab60482021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97207-5https://doaj.org/toc/2045-2322Abstract COVID-19, a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus, has claimed millions of lives worldwide. Amid soaring contagion due to newer strains of the virus, it is imperative to design dynamic, spatiotemporal models to contain the spread of infection during future outbreaks of the same or variants of the virus. The reliance on existing prediction and contact tracing approaches on prior knowledge of inter- or intra-zone mobility renders them impracticable. We present a spatiotemporal approach that employs a network inference approach with sliding time windows solely on the date and number of daily infection numbers of zones within a geographical region to generate temporal networks capturing the influence of each zone on another. It helps analyze the spatial interaction among the hotspot or spreader zones and highly affected zones based on the flow of network contagion traffic. We apply the proposed approach to the daily infection counts of New York State as well as the states of USA to show that it effectively measures the phase shifts in the pandemic timeline. It identifies the spreaders and affected zones at different time points and helps infer the trajectory of the pandemic spread across the country. A small set of zones periodically exhibit a very high outflow of contagion traffic over time, suggesting that they act as the key spreaders of infection. Moreover, the strong influence between the majority of non-neighbor regions suggests that the overall spread of infection is a result of the unavoidable long-distance trips by a large number of people as opposed to the shorter trips at a county level, thereby informing future mitigation measures and public policies.Satyaki RoyPreetom BiswasPreetam GhoshNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Satyaki Roy Preetom Biswas Preetam Ghosh Spatiotemporal tracing of pandemic spread from infection data
description	Abstract COVID-19, a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus, has claimed millions of lives worldwide. Amid soaring contagion due to newer strains of the virus, it is imperative to design dynamic, spatiotemporal models to contain the spread of infection during future outbreaks of the same or variants of the virus. The reliance on existing prediction and contact tracing approaches on prior knowledge of inter- or intra-zone mobility renders them impracticable. We present a spatiotemporal approach that employs a network inference approach with sliding time windows solely on the date and number of daily infection numbers of zones within a geographical region to generate temporal networks capturing the influence of each zone on another. It helps analyze the spatial interaction among the hotspot or spreader zones and highly affected zones based on the flow of network contagion traffic. We apply the proposed approach to the daily infection counts of New York State as well as the states of USA to show that it effectively measures the phase shifts in the pandemic timeline. It identifies the spreaders and affected zones at different time points and helps infer the trajectory of the pandemic spread across the country. A small set of zones periodically exhibit a very high outflow of contagion traffic over time, suggesting that they act as the key spreaders of infection. Moreover, the strong influence between the majority of non-neighbor regions suggests that the overall spread of infection is a result of the unavoidable long-distance trips by a large number of people as opposed to the shorter trips at a county level, thereby informing future mitigation measures and public policies.
format	article
author	Satyaki Roy Preetom Biswas Preetam Ghosh
author_facet	Satyaki Roy Preetom Biswas Preetam Ghosh
author_sort	Satyaki Roy
title	Spatiotemporal tracing of pandemic spread from infection data
title_short	Spatiotemporal tracing of pandemic spread from infection data
title_full	Spatiotemporal tracing of pandemic spread from infection data
title_fullStr	Spatiotemporal tracing of pandemic spread from infection data
title_full_unstemmed	Spatiotemporal tracing of pandemic spread from infection data
title_sort	spatiotemporal tracing of pandemic spread from infection data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2bdc85663e6a40b5b67ece41f7ab6048
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Spatiotemporal tracing of pandemic spread from infection data

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