ArticlePeerJ2021
Spatial temporal distribution of COVID-19 risk during the early phase of the pandemic in Malawi.
Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.
What it found
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The trial behind it
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Who cites it
9 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Pooled it
- Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Respiratory viral infections: when and where? A scoping review of spatiotemporal methods.Journal of global health · 2025Article
- Epidemiological and phylogenetic analyses of public SARS-CoV-2 data from Malawi.PLOS global public health · 2025Article
- Article
- Spatial and spatio-temporal epidemiological approaches to inform COVID-19 surveillance and control: a review protocol.Systematic reviews · 2022Article
- Spatial Dynamics and Multiscale Regression Modelling of Population Level Indicators for COVID-19 Spread in Malaysia.International journal of environmental research and public health · 2022Article
- The Epidemiological and Spatiotemporal Characteristics of the 2019 Novel Coronavirus Disease (COVID-19) in Libya.Frontiers in public health · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCOVID-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.
methodsThe 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.
resultsThe 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]).
conclusionFuture 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.
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