Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi

Background COVID-19 has been one of the greatest challenges the world has faced since the second world war. This study aimed at investigating the distribution of COVID-19 in both space and time in Malawi. Methods The study used publicly available data of COVID-19 cases for the period from 2 April 20...

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Main Authors: Alfred Ngwira, Felix Kumwenda, Eddons C.S. Munthali, Duncan Nkolokosa
Format: Article
Language:English
Published: PeerJ Inc. 2021-02-01
Series:PeerJ
Subjects:
Online Access:https://peerj.com/articles/11003.pdf
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spelling doaj-bbd2403c7aad4d7b8dffbc7a75e08ecc2021-02-26T15:05:19ZengPeerJ Inc.PeerJ2167-83592021-02-019e1100310.7717/peerj.11003Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in MalawiAlfred NgwiraFelix KumwendaEddons C.S. MunthaliDuncan NkolokosaBackground COVID-19 has been one of the greatest challenges the world has faced since the second world war. This study aimed at investigating the distribution of COVID-19 in both space and time in Malawi. Methods The study used publicly available data of COVID-19 cases for the period from 2 April 2020 to 28 October 2020. Semiparametric spatial temporal models were fitted to the number of monthly confirmed cases as an outcome data, with time and district as independent variables, where district was the spatial unit, while accounting for sociodemographic factors. Results The study found significant effects of location and time, with the two interacting. The spatial distribution of COVID-19 risk showed major cities being at greater risk than rural areas. Over time, the COVID-19 risk was increasing then decreasing in most districts with the rural districts being consistently at lower risk. High proportion of elderly people was positively associated with COVID-19 risk (β = 1.272, 95% CI [0.171, 2.370]) than low proportion of elderly people. There was negative association between poverty incidence and COVID-19 risk (β = −0.100, 95% CI [−0.136, −0.065]). Conclusion Future or present strategies to limit the spread of COVID-19 should target major cities and the focus should be on time periods that had shown high risk. Furthermore, the focus should be on elderly and rich people.https://peerj.com/articles/11003.pdfSpatial epidemiologySpatial riskCOVID-19 spreadSpatiotemporal modeling
collection DOAJ
language English
format Article
sources DOAJ
author Alfred Ngwira
Felix Kumwenda
Eddons C.S. Munthali
Duncan Nkolokosa
spellingShingle Alfred Ngwira
Felix Kumwenda
Eddons C.S. Munthali
Duncan Nkolokosa
Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi
PeerJ
Spatial epidemiology
Spatial risk
COVID-19 spread
Spatiotemporal modeling
author_facet Alfred Ngwira
Felix Kumwenda
Eddons C.S. Munthali
Duncan Nkolokosa
author_sort Alfred Ngwira
title Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi
title_short Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi
title_full Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi
title_fullStr Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi
title_full_unstemmed Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi
title_sort spatial temporal distribution of covid-19 risk during the early phase of the pandemic in malawi
publisher PeerJ Inc.
series PeerJ
issn 2167-8359
publishDate 2021-02-01
description Background COVID-19 has been one of the greatest challenges the world has faced since the second world war. This study aimed at investigating the distribution of COVID-19 in both space and time in Malawi. Methods The study used publicly available data of COVID-19 cases for the period from 2 April 2020 to 28 October 2020. Semiparametric spatial temporal models were fitted to the number of monthly confirmed cases as an outcome data, with time and district as independent variables, where district was the spatial unit, while accounting for sociodemographic factors. Results The study found significant effects of location and time, with the two interacting. The spatial distribution of COVID-19 risk showed major cities being at greater risk than rural areas. Over time, the COVID-19 risk was increasing then decreasing in most districts with the rural districts being consistently at lower risk. High proportion of elderly people was positively associated with COVID-19 risk (β = 1.272, 95% CI [0.171, 2.370]) than low proportion of elderly people. There was negative association between poverty incidence and COVID-19 risk (β = −0.100, 95% CI [−0.136, −0.065]). Conclusion Future or present strategies to limit the spread of COVID-19 should target major cities and the focus should be on time periods that had shown high risk. Furthermore, the focus should be on elderly and rich people.
topic Spatial epidemiology
Spatial risk
COVID-19 spread
Spatiotemporal modeling
url https://peerj.com/articles/11003.pdf
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