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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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 |
work_keys_str_mv |
AT alfredngwira spatialtemporaldistributionofcovid19riskduringtheearlyphaseofthepandemicinmalawi AT felixkumwenda spatialtemporaldistributionofcovid19riskduringtheearlyphaseofthepandemicinmalawi AT eddonscsmunthali spatialtemporaldistributionofcovid19riskduringtheearlyphaseofthepandemicinmalawi AT duncannkolokosa spatialtemporaldistributionofcovid19riskduringtheearlyphaseofthepandemicinmalawi |
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