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Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020

The effect of weather on COVID-19 spread is poorly understood. Recently, few studies have claimed that warm weather can possibly slowdown the global pandemic, which has already affected over 1.6 million people worldwide. Clarification of such relationships in the worst affected country, the US, can...

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Detalles Bibliográficos
Autores principales: Gupta, Sonal, Raghuwanshi, Gourav Singh, Chanda, Arnab
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7194548/
https://www.ncbi.nlm.nih.gov/pubmed/32334160
http://dx.doi.org/10.1016/j.scitotenv.2020.138860
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author Gupta, Sonal
Raghuwanshi, Gourav Singh
Chanda, Arnab
author_facet Gupta, Sonal
Raghuwanshi, Gourav Singh
Chanda, Arnab
author_sort Gupta, Sonal
collection PubMed
description The effect of weather on COVID-19 spread is poorly understood. Recently, few studies have claimed that warm weather can possibly slowdown the global pandemic, which has already affected over 1.6 million people worldwide. Clarification of such relationships in the worst affected country, the US, can be immensely beneficial to understand the role of weather in transmission of the disease in the highly populated countries, such as India. We collected the daily data of new cases in 50 US states between Jan 1–Apr 9, 2020 and also the corresponding weather information (i.e., temperature (T) and absolute humidity (AH)). Distribution modeling of new cases across AH and T, helped identify the narrow and vulnerable AH range. We validated the results for 10-day intervals against monthly observations, and also worldwide trends. The results were used to predict Indian regions which would be vulnerable to weather based spread in upcoming months of 2020. COVID-19 spread in the US is significant for states with 4 < AH < 6 g/m(3) and number of new cases > 10,000, irrespective of the chosen time intervals for study parameters. These trends are consistent with worldwide observations, but do not correlate well with India so far possibly due the total cases reported per interval < 10,000. The results clarify the relationship between weather parameters and COVID-19 spread. The vulnerable weather parameters will help classify the risky geographic areas in different countries. Specifically, with further reporting of new cases in India, prediction of states with high risk of weather based spread will be apparent.
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spelling pubmed-71945482020-05-02 Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020 Gupta, Sonal Raghuwanshi, Gourav Singh Chanda, Arnab Sci Total Environ Article The effect of weather on COVID-19 spread is poorly understood. Recently, few studies have claimed that warm weather can possibly slowdown the global pandemic, which has already affected over 1.6 million people worldwide. Clarification of such relationships in the worst affected country, the US, can be immensely beneficial to understand the role of weather in transmission of the disease in the highly populated countries, such as India. We collected the daily data of new cases in 50 US states between Jan 1–Apr 9, 2020 and also the corresponding weather information (i.e., temperature (T) and absolute humidity (AH)). Distribution modeling of new cases across AH and T, helped identify the narrow and vulnerable AH range. We validated the results for 10-day intervals against monthly observations, and also worldwide trends. The results were used to predict Indian regions which would be vulnerable to weather based spread in upcoming months of 2020. COVID-19 spread in the US is significant for states with 4 < AH < 6 g/m(3) and number of new cases > 10,000, irrespective of the chosen time intervals for study parameters. These trends are consistent with worldwide observations, but do not correlate well with India so far possibly due the total cases reported per interval < 10,000. The results clarify the relationship between weather parameters and COVID-19 spread. The vulnerable weather parameters will help classify the risky geographic areas in different countries. Specifically, with further reporting of new cases in India, prediction of states with high risk of weather based spread will be apparent. Elsevier B.V. 2020-08-01 2020-04-21 /pmc/articles/PMC7194548/ /pubmed/32334160 http://dx.doi.org/10.1016/j.scitotenv.2020.138860 Text en © 2020 Elsevier B.V. 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
Gupta, Sonal
Raghuwanshi, Gourav Singh
Chanda, Arnab
Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020
title Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020
title_full Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020
title_fullStr Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020
title_full_unstemmed Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020
title_short Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020
title_sort effect of weather on covid-19 spread in the us: a prediction model for india in 2020
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7194548/
https://www.ncbi.nlm.nih.gov/pubmed/32334160
http://dx.doi.org/10.1016/j.scitotenv.2020.138860
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