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Community venue exposure risk estimator for the COVID-19 pandemic

Complexities of virus genotypes and the stochastic contacts in human society create a big challenge for estimating the potential risks of exposure to a widely spreading virus such as COVID-19. To increase public awareness of exposure risks in daily activities, we propose a birthday-paradox-based pro...

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Detalles Bibliográficos
Autores principales: Sun, Ziheng, Di, Liping, Sprigg, William, Tong, Daniel, Casal, Mariana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7522786/
https://www.ncbi.nlm.nih.gov/pubmed/33010661
http://dx.doi.org/10.1016/j.healthplace.2020.102450
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author Sun, Ziheng
Di, Liping
Sprigg, William
Tong, Daniel
Casal, Mariana
author_facet Sun, Ziheng
Di, Liping
Sprigg, William
Tong, Daniel
Casal, Mariana
author_sort Sun, Ziheng
collection PubMed
description Complexities of virus genotypes and the stochastic contacts in human society create a big challenge for estimating the potential risks of exposure to a widely spreading virus such as COVID-19. To increase public awareness of exposure risks in daily activities, we propose a birthday-paradox-based probability model to implement in a web-based system, named COSRE (community social risk estimator) and make in-time community exposure risk estimation during the ongoing COVID-19 pandemic. We define exposure risk to mean the probability of people meeting potential cases in public places such as grocery stores, gyms, libraries, restaurants, coffee shops, offices, etc. Our model has three inputs: the real-time number of active and asymptomatic cases, the population in local communities, and the customer counts in the room. With COSRE, possible impacts of the pandemic can be explored through spatiotemporal analysis, e.g., a variable number of people may be projected into public places through time to assess changes of risk as the pandemic unfolds. The system has potential to advance understanding of the true exposure risks in various communities. It introduces an objective element to plan, prepare and respond during a pandemic. Spatial analysis tools are used to draw county-level exposure risks of the United States from April 1 to July 15, 2020. The correlation experiment with the new cases in the next two weeks shows that the risk estimation model offers promise in assisting people to be more precise about their personal safety and control of daily routine and social interaction. It can inform business and municipal COVID-19 policy to accelerate recovery.
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spelling pubmed-75227862020-09-29 Community venue exposure risk estimator for the COVID-19 pandemic Sun, Ziheng Di, Liping Sprigg, William Tong, Daniel Casal, Mariana Health Place Article Complexities of virus genotypes and the stochastic contacts in human society create a big challenge for estimating the potential risks of exposure to a widely spreading virus such as COVID-19. To increase public awareness of exposure risks in daily activities, we propose a birthday-paradox-based probability model to implement in a web-based system, named COSRE (community social risk estimator) and make in-time community exposure risk estimation during the ongoing COVID-19 pandemic. We define exposure risk to mean the probability of people meeting potential cases in public places such as grocery stores, gyms, libraries, restaurants, coffee shops, offices, etc. Our model has three inputs: the real-time number of active and asymptomatic cases, the population in local communities, and the customer counts in the room. With COSRE, possible impacts of the pandemic can be explored through spatiotemporal analysis, e.g., a variable number of people may be projected into public places through time to assess changes of risk as the pandemic unfolds. The system has potential to advance understanding of the true exposure risks in various communities. It introduces an objective element to plan, prepare and respond during a pandemic. Spatial analysis tools are used to draw county-level exposure risks of the United States from April 1 to July 15, 2020. The correlation experiment with the new cases in the next two weeks shows that the risk estimation model offers promise in assisting people to be more precise about their personal safety and control of daily routine and social interaction. It can inform business and municipal COVID-19 policy to accelerate recovery. Elsevier Ltd. 2020-11 2020-09-29 /pmc/articles/PMC7522786/ /pubmed/33010661 http://dx.doi.org/10.1016/j.healthplace.2020.102450 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Sun, Ziheng
Di, Liping
Sprigg, William
Tong, Daniel
Casal, Mariana
Community venue exposure risk estimator for the COVID-19 pandemic
title Community venue exposure risk estimator for the COVID-19 pandemic
title_full Community venue exposure risk estimator for the COVID-19 pandemic
title_fullStr Community venue exposure risk estimator for the COVID-19 pandemic
title_full_unstemmed Community venue exposure risk estimator for the COVID-19 pandemic
title_short Community venue exposure risk estimator for the COVID-19 pandemic
title_sort community venue exposure risk estimator for the covid-19 pandemic
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7522786/
https://www.ncbi.nlm.nih.gov/pubmed/33010661
http://dx.doi.org/10.1016/j.healthplace.2020.102450
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