SARS-CoV-2 Evolution in an HIV-Endemic Setting: A Genomic Epidemiology Study from Botswana

Abstract

Background: HIV infection can affect SARS-CoV-2 infection dynamics through prolonged viral replication and altered immune responses, but population-level genomic evidence from HIV-endemic settings is limited. Botswana, a country with high HIV prevalence and an established genomic surveillance infrastructure, provides an important setting to examine how host immune variation influences viral evolution during the Omicron period.

Methods: We conducted a population-based genomic epidemiology study in the Greater Gaborone District between June 2022 and November 2024. Among 404 SARS-CoV-2 positive samples, 387 yielded high-quality genomes (Ct < 35). Phylogenetic trees were reconstructed using IQ-TREE, time-calibrated with TreeTime, and visualized in Nextstrain to illustrate lineage turnover and clustering patterns. Participant metadata, including HIV status and vaccination history, were summarized descriptively and incorporated into phylogenetic visualizations to annotate sequences by HIV status.

Results: Six successive Omicron sublineage waves (BA.4/BA.5, BQ/FN, XBB, BA.2.86, KP, and XEC.9) were detected, consistent with repeated viral introductions rather than sustained local diversification. Sequences from HIV-positive and HIV-negative individuals were broadly interspersed throughout the phylogeny, indicating no population-level separation by HIV status. However, small dyads of HIV-positive hosts appeared within BA, JN, and KP sublineages.

Interpretation: In a highly treated and vaccinated population, HIV infection did not fundamentally alter SARS-CoV-2 evolutionary trajectories at the population level. However, subtle clustering among HIV-positive hosts suggests possible host-specific effects, underscoring the importance of continued genomic surveillance integrating host immunologic and clinical data to detect early signals of viral adaptation.

Keywords: Botswana; HIV infection; Omicron; SARS-CoV-2; genomic epidemiology; phylodynamics.

Figure 1:

Temporal distribution of SARS-CoV-2 lineages in Botswana, June 2022–November 2024. Stacked area plot showing the monthly proportion of parent lineages among 387 high-quality genomes collected in the Greater Gaborone District. Parent categories (e.g., BA, XBB, KP) are displayed, while specific sublineages (e.g., BA.4, BA.5, BQ.1.1, BA.2.86.2, KP.2.3, XEC.9) were identified from Pango lineage assignments in the sequencing metadata. Three major waves of lineage turnover were observed: BA.4/BA.5 predominated in mid-2022, followed by BQ/FN and XBB in late 2022–2023, and subsequently replaced by BA.2.86, KP, and XEC.9 during 2024.

Figure 2:

Time-resolved phylogeny of 387 SARS-CoV-2 genomes from Botswana. Maximum-likelihood phylogeny annotated with Nextstrain clade designations, showing sequential replacement of lineages between June 2022 and November 2024. Early genomes clustered within BA.4 (22A) and BA.5 (22B) clades, followed by diversification into BQ.1 (22E) and XBB (23A, 23B, 23D). A third wave began in late 2023 with the emergence of BA.2.86 (23I) and JN.1 (24A), which diversified into descendant clades in 2024, including 24B (JN.1.1.1), 24C (KP.3), and 24E (KP.1.1). The most recent tips were dominated by XEC (24F), particularly XEC.9, along with detections of KP.2.3 (24G), reflecting the ongoing diversification of JN.1-derived lineages.

Figure 3:

Lineage-specific time-scaled phylogenies of major SARS-CoV-2 lineages from Botswana (June 2022–November 2024), annotated by HIV status. Branch lengths are scaled to calendar time. Tip colors indicate HIV-positive, HIV-negative, or unknown status. These lineage-resolved trees depict within-lineage diversification and provide a framework to evaluate potential associations between HIV status and phylogenetic clustering.