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


The novel Coronavirus SARS-CoV-2 was first identified in a person in Wuhan city, China in December 2019, and had spread to all continents in less than three months. While there were many similarities between the resulting COVID-19 pandemic in different regions and countries, there were also important differences, motivating systematic quantitative analysis of available data for as diverse a set of geographical locations as possible to drive generation of insights relevant for response to COVID-19 and other respiratory viral and pandemic threats. Malawi had its first COVID-19 case on 2 April 2020 and, like many countries in the Global South, had access to orders of magnitude less data than countries in the Global North to inform its response. Here, we present modelling analyses of SARS-CoV-2 epidemiology and phylogenetics in Malawi from 2 April 2020 to 19 October 2022. We carried out this analysis using open-source software tools and open data on cases, deaths, geography, demographics, and viral genomics. In particular, we used R to visualise the raw data and results, alongside Generalised Additive Models (GAMs), which were fit to case and mortality data to describe the incidence trends, growth rate and doubling time of SARS-CoV-2. IQTree, TreeTime and interactive Tree of Life were used to perform the phylogenetic analysis. This analysis reveals five major waves of COVID-19 in Malawi, associated with different lineages: (1) Early variants; (2) Beta; (3) Delta; (4) Omicron BA.1; (5) Other Omicron. Some sequences associated with the Alpha variant were present but these did not appear to drive a major wave as they did in some other countries. 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 cases and deaths were highly geographically heterogeneous. We argue that such analyses could have been and could in future be carried out in real time in Malawi and other countries in the Global South with similar computational and data resources.