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

IF 2.5 PLOS global public health Pub Date : 2025-03-21 eCollection Date: 2025-01-01 DOI:10.1371/journal.pgph.0003943
Mwandida Kamba Afuleni, Roberto Cahuantzi, Katrina A Lythgoe, Atupele Ngina Mulaga, Ian Hall, Olatunji Johnson, Thomas House
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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.

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马拉维SARS-CoV-2公共数据的流行病学和系统发育分析
COVID-19 大流行在不同地区造成的影响各不相同,因此有必要采取以数据为导向的本地化应对措施。SARS-CoV-2 于 2019 年 12 月首次在中国武汉的一名患者身上发现,并在三个月内扩散到全球。虽然该流行病对各地区的影响有相似之处,但关键的差异促使人们对不同地区的数据进行系统的定量分析,为应对措施提供依据。马拉维于 2020 年 4 月 2 日报告了首例 COVID-19 病例,但与全球北方国家相比,马拉维的应对数据要少得多。在此,我们对 2020 年 4 月 2 日至 2022 年 10 月 19 日期间马拉维的 SARS-CoV-2 流行病学和系统发生学进行了模拟分析。我们使用开源工具和有关确诊病例、死亡病例、地理、人口和病毒基因组学的公开数据进行了分析。R 用于数据可视化,而广义相加模型 (GAM) 用于估计发病趋势、增长率和加倍时间。使用 IQ-TREE、TreeTime 和交互式生命树进行了系统发育分析。该分析确定了马拉维的五个主要 COVID-19 波,分别由不同的品系驱动:(1) 早期变体,(2) Beta,(3) Delta,(4) Omicron BA.1 和 (5) 其他 Omicron。虽然出现了阿尔法变种,但它并没有引起大的波及,这可能是由于来自传染性更强的德尔塔变种的竞争,因为阿尔法在马拉维流行时,贝塔正在逐步淘汰,德尔塔正在兴起。与早期变种相比,Delta 变种的病死率较高,Omicron 变种的病死率较低。系统发育显示,正如预期的那样,病毒树分为几个主要系,而 Omicron 出现较早,这与该变异体的可能起源地很近是一致的。变异体的流行率以及确诊病例和确诊死亡病例的总体比率在地域上存在高度异质性。我们建议马拉维和其他拥有类似计算和数据资源的国家考虑进行实时分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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