Evidence map›Paper›PMID 40117234›Full record

ArticlePLOS global public health2025

Epidemiological and phylogenetic analyses of public SARS-CoV-2 data from Malawi.

Mwandida Kamba Afuleni, Roberto Cahuantzi, Katrina A Lythgoe, Atupele Ngina Mulaga, Ian Hall, Olatunji Johnson, Thomas House

Abstract read
In one paragraph

Article in PLOS global public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Mwandida Kamba AfuleniDepartment of Mathematics, University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0009-0005-2389-8217
Roberto CahuantziDepartment of Mathematics, University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0002-0212-6825
Katrina A LythgoeBig Data Institute, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-7089-7680
Atupele Ngina MulagaDepartment of Mathematical Sciences, School of Science and Technology, Malawi University of Business and Applied Sciences, Blantyre, Malawi.ORCID https://orcid.org/0000-0002-9172-0366
Ian HallDepartment of Mathematics, University of Manchester, Manchester, United Kingdom.
Olatunji JohnsonDepartment of Mathematics, University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0002-4080-0999
Thomas HouseDepartment of Mathematics, University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0001-5835-8062

Funding

Wellcome Trust
6 · The paper itself

Abstract

The COVID-19 pandemic has had varying impacts across different regions, necessitating localised data-driven responses. SARS-CoV-2 was first identified in a person in Wuhan, China, in December 2019 and spread globally within three months. While there were similarities in the pandemic's impact across regions, key differences motivated systematic quantitative analysis of diverse geographical data to inform responses. Malawi reported its first COVID-19 case on 2 April 2020 but had significantly less data than Global North countries to inform its response. Here, we present a modelling analysis of SARS-CoV-2 epidemiology and phylogenetics in Malawi between 2 April 2020 and 19 October 2022. We carried out this analysis using open-source tools and open data on confirmed cases, deaths, geography, demographics, and viral genomics. R was used for data visualisation, while Generalised Additive Models (GAMs) estimated incidence trends, growth rates, and doubling times. Phylogenetic analysis was conducted using IQ-TREE, TreeTime, and interactive tree of life. This analysis identifies five major COVID-19 waves in Malawi, driven by different lineages: (1) Early variants, (2) Beta, (3) Delta, (4) Omicron BA.1, and (5) Other Omicron. While the Alpha variant was present, it did not cause a major wave, likely due to competition from the more infectious Delta variant, since Alpha circulated in Malawi when Beta was phasing out and Delta emerging. Case Fatality Ratios were higher for Delta, and lower for Omicron, than for earlier lineages. Phylogeny reveals separation of the tree into major lineages as would be expected, and early emergence of Omicron, as is consistent with proximity to the likely origin of this variant. Both variant prevalence and overall rates of confirmed cases and confirmed deaths were highly geographically heterogeneous. We suggest that real-time analyses should be considered in Malawi and other countries, where similar computational and data resources are available.

Identifiers

PMID40117234
PMCPMC11927878

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.