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Climatic influence on the magnitude of COVID-19 outbreak: a stochastic model-based global analysis

Climatic influence on the magnitude of COVID-19 outbreak: a stochastic model-based global analysis
Climatic influence on the magnitude of COVID-19 outbreak: a stochastic model-based global analysis
We investigate the climatic influence on COVID-19 transmission risks in 228 cities globally across three climatic zones. The results, based on the application of a Boosted Regression Tree algorithm method, show that average temperature and average relative humidity explain significant variations in COVID-19 transmission across temperate and subtropical regions, whereas in the tropical region, the average diurnal temperature range and temperature seasonality significantly predict the infection outbreak. The number of positive cases showed a decrease sharply above an average temperature of 10°C in the cities of France, Turkey, the US, the UK, and Germany. Among the tropical countries, COVID-19 in Indian cities is most affected by mean diurnal temperature, and those in Brazil by temperature seasonality. The findings have implications on public health interventions, and contribute to the ongoing scientific and policy discourse on the complex interplay of climatic factors determining the risks of COVID-19 transmission.
Boosted Regression Tree, COVID-19 transmission, SARS-CoV-2, climatic association, stochastic model
1-16
Pramanik, Malay
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Chowdhury, Koushik
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Rana, Md Juel
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Bisht, Praffulit
bceb5e2f-51df-4a82-a81c-5af1dba99a8f
Pal, Raghunath
2b10a851-1518-4834-9b3f-bbb2d54ff73b
Szabo, Sylvia
d311200a-6d18-4d85-b2eb-80baf360a061
Pal, Indrajit
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Behera, Bhagirath
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Liang, Qiuhua
e6484c41-c689-41de-9d22-35b5a427e658
Padmadas, Sabu S.
64b6ab89-152b-48a3-838b-e9167964b508
Udmale, Parmeshwar
aa315c22-2a4e-4b1e-9502-05cdb3ed96c6
Pramanik, Malay
9521e7c0-1959-4c65-a537-565b7478b885
Chowdhury, Koushik
29f6a14c-74da-47b1-978a-8064eb9375bf
Rana, Md Juel
29b1cf64-4eb3-43e0-bf41-01dd84a2f6a4
Bisht, Praffulit
bceb5e2f-51df-4a82-a81c-5af1dba99a8f
Pal, Raghunath
2b10a851-1518-4834-9b3f-bbb2d54ff73b
Szabo, Sylvia
d311200a-6d18-4d85-b2eb-80baf360a061
Pal, Indrajit
f0760e44-9184-442e-ab9a-0e0f745f5893
Behera, Bhagirath
05a4dd03-3c5d-4eed-b768-1bb3b88ddb04
Liang, Qiuhua
e6484c41-c689-41de-9d22-35b5a427e658
Padmadas, Sabu S.
64b6ab89-152b-48a3-838b-e9167964b508
Udmale, Parmeshwar
aa315c22-2a4e-4b1e-9502-05cdb3ed96c6

Pramanik, Malay, Chowdhury, Koushik, Rana, Md Juel, Bisht, Praffulit, Pal, Raghunath, Szabo, Sylvia, Pal, Indrajit, Behera, Bhagirath, Liang, Qiuhua, Padmadas, Sabu S. and Udmale, Parmeshwar (2020) Climatic influence on the magnitude of COVID-19 outbreak: a stochastic model-based global analysis. International Journal of Environmental Health Research, 1-16. ().

Record type: Article

Abstract

We investigate the climatic influence on COVID-19 transmission risks in 228 cities globally across three climatic zones. The results, based on the application of a Boosted Regression Tree algorithm method, show that average temperature and average relative humidity explain significant variations in COVID-19 transmission across temperate and subtropical regions, whereas in the tropical region, the average diurnal temperature range and temperature seasonality significantly predict the infection outbreak. The number of positive cases showed a decrease sharply above an average temperature of 10°C in the cities of France, Turkey, the US, the UK, and Germany. Among the tropical countries, COVID-19 in Indian cities is most affected by mean diurnal temperature, and those in Brazil by temperature seasonality. The findings have implications on public health interventions, and contribute to the ongoing scientific and policy discourse on the complex interplay of climatic factors determining the risks of COVID-19 transmission.

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COVID-19 and climate IJEHR - Accepted Manuscript
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Supplementary file - Accepted Manuscript
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Table 1: Preliminary and final selected variable for the BRT model - Accepted Manuscript
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Accepted/In Press date: 28 September 2020
e-pub ahead of print date: 22 October 2020
Published date: 22 October 2020
Keywords: Boosted Regression Tree, COVID-19 transmission, SARS-CoV-2, climatic association, stochastic model

Identifiers

Local EPrints ID: 444986
URI: http://eprints.soton.ac.uk/id/eprint/444986
DOI:
PURE UUID: 8346c48a-a95d-4b05-a6fd-f9ce9a145e16
ORCID for Sabu S. Padmadas:

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Date deposited: 16 Nov 2020 17:31
Last modified: 23 Nov 2020 17:32

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Contributors

Author: Malay Pramanik
Author: Koushik Chowdhury
Author: Md Juel Rana
Author: Praffulit Bisht
Author: Raghunath Pal
Author: Sylvia Szabo
Author: Indrajit Pal
Author: Bhagirath Behera
Author: Qiuhua Liang
Author:
Author: Parmeshwar Udmale

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