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Limited studies have been conducted on the safety and effectiveness of heterologous COVID-19 vaccine boosting in lower income settings, especially those with high-HIV prevalence., The Sisonke Heterologous mRNA-1273 boost after prime with Ad26.COV2.S (SHERPA) trial evaluated a mRNA-1273 boost after Ad26.COV2.S priming in South Africa. SHERPA was a single-arm, open-label, phase 3 study nested in the Sisonke implementation trial of 500000 healthcare workers (HCWs). Sisonke participants were offered mRNA-1273 boosters between May and November 2022, when Omicron sub-lineages were circulating. Adverse events (AE) were self-reported, and co-primary endpoints (SARS-CoV-2 infections and COVID-19 hospitalizations or deaths) were collected through national databases. We used Cox regression models with booster status as a time-varying covariate to determine the relative vaccine effectiveness (rVE) of the mRNA-1273 booster among SHERPA versus unboosted Sisonke participants. Of 11248 SHERPA participants in the rVE analysis cohort (79.3% female, median age 41), 45.4% had received one and 54.6% two Ad26.COV2.S doses. Self-reported comorbidities included HIV (18.7%), hypertension (12.9%) and diabetes (4.6%). In multivariable analysis including 413161 unboosted Sisonke participants, rVE of the booster was 59% (95%CI 29-76%) against SARS-CoV-2 infection: 77% (95%CI 9-94%) in the one-Ad26.COV2.S dose group and 52% (95%CI 13-73%) in the two-dose group. Severe COVID-19 was identified in 148 unboosted Sisonke participants, and only one SHERPA participant with severe HIV-related immunosuppression. Of 11798 participants in the safety analysis, 228 (1.9%) participants reported 575 reactogenicity events within 7 days of the booster (most commonly injection site pain, malaise, myalgia, swelling, induration and fever). More reactogenicity events were reported among those with prior SARS-CoV-2 infections (adjusted odds ratio [aOR] 2.03, 95%CI 1.59-2.59) and less among people living with HIV (PLWH) (aOR 0.49, 95%CI 0.34-0.69). There were 115 unsolicited adverse events (AEs) within 28 days of vaccination. No related serious AEs were reported. In an immunogenicity sub-study, mRNA-1273 increased binding and neutralizing antibody titres and spike-specific T-cell responses 4 weeks after boosting regardless of the number of prior Ad26.COV2.S doses, or HIV status, and generated Omicron spike-specific cross-reactive responses. mRNA-1273 boosters after one or two Ad26.COV2.S doses were well-tolerated, safe and effective against Omicron SARS-CoV-2 infections among HCWs and PLWH. Trial registration: The SHERPA study is registered in the Pan African Clinical Trials Registry (PACTR): PACTR202310615330649 and the South African National Clinical Trial Registry (SANCTR): DOH-27-052022-5778., Competing Interests: KA and BL are employees of Moderna, Inc. and may hold stock/stock options in the company. The other authors declare no conflict of interests., (Copyright: © 2024 Garrett et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The South African COVID-19 Modelling Consortium (SACMC) was established in late March 2020 to support planning and budgeting for COVID-19 related healthcare in South Africa. We developed several tools in response to the needs of decision makers in the different stages of the epidemic, allowing the South African government to plan several months ahead., Methods: Our tools included epidemic projection models, several cost and budget impact models, and online dashboards to help government and the public visualise our projections, track case development and forecast hospital admissions. Information on new variants, including Delta and Omicron, were incorporated in real time to allow the shifting of scarce resources when necessary., Results: Given the rapidly changing nature of the outbreak globally and in South Africa, the model projections were updated regularly. The updates reflected 1) the changing policy priorities over the course of the epidemic; 2) the availability of new data from South African data systems; and 3) the evolving response to COVID-19 in South Africa, such as changes in lockdown levels and ensuing mobility and contact rates, testing and contact tracing strategies and hospitalisation criteria. Insights into population behaviour required updates by incorporating notions of behavioural heterogeneity and behavioural responses to observed changes in mortality. We incorporated these aspects into developing scenarios for the third wave and developed additional methodology that allowed us to forecast required inpatient capacity. Finally, real-time analyses of the most important characteristics of the Omicron variant first identified in South Africa in November 2021 allowed us to advise policymakers early in the fourth wave that a relatively lower admission rate was likely., Conclusion: The SACMC's models, developed rapidly in an emergency setting and regularly updated with local data, supported national and provincial government to plan several months ahead, expand hospital capacity when needed, allocate budgets and procure additional resources where possible. Across four waves of COVID-19 cases, the SACMC continued to serve the planning needs of the government, tracking waves and supporting the national vaccine rollout., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Meyer-Rath et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

There are limited published data within sub-Saharan Africa describing hospital pathways of COVID-19 patients hospitalized. These data are crucial for the parameterisation of epidemiological and cost models, and for planning purposes for the region. We evaluated COVID-19 hospital admissions from the South African national hospital surveillance system (DATCOV) during the first three COVID-19 waves between May 2020 and August 2021. We describe probabilities and admission into intensive care units (ICU), mechanical ventilation, death, and lengths of stay (LOS) in non-ICU and ICU care in public and private sectors. A log-binomial model was used to quantify mortality risk, ICU treatment and mechanical ventilation between time periods, adjusting for age, sex, comorbidity, health sector and province. There were 342,700 COVID-19-related hospital admissions during the study period. Risk of ICU admission was 16% lower during wave periods (adjusted risk ratio (aRR) 0.84 [0.82-0.86]) compared to between-wave periods. Mechanical ventilation was more likely during a wave overall (aRR 1.18 [1.13-1.23]), but patterns between waves were inconsistent, while mortality risk in non-ICU and ICU were 39% (aRR 1.39 [1.35-1.43]) and 31% (aRR 1.31 [1.27-1.36]) higher during a wave, compared to between-wave periods, respectively. If patients had had the same probability of death during waves vs between-wave periods, we estimated approximately 24% [19%-30%] of deaths (19,600 [15,200-24,000]) would not have occurred over the study period. LOS differed by age (older patients stayed longer), ward type (ICU stays were longer than non-ICU) and death/recovery outcome (time to death was shorter in non-ICU); however, LOS remained similar between time periods. Healthcare capacity constraints as inferred by wave period have a large impact on in-hospital mortality. It is crucial for modelling health systems strain and budgets to consider how input parameters related to hospitalisation change during and between waves, especially in settings with severely constrained resources., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Jamieson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In March 2020 the South African COVID-19 Modelling Consortium was formed to support government planning for COVID-19 cases and related healthcare. Models were developed jointly by local disease modelling groups to estimate cases, resource needs and deaths due to COVID-19. The National COVID-19 Epi Model (NCEM) while initially developed as a deterministic compartmental model of SARS-Cov-2 transmission in the nine provinces of South Africa, was adapted several times over the course of the first wave of infection in response to emerging local data and changing needs of government. By the end of the first wave, the NCEM had developed into a stochastic, spatially-explicit compartmental transmission model to estimate the total and reported incidence of COVID-19 across the 52 districts of South Africa. The model adopted a generalised Susceptible-Exposed-Infectious-Removed structure that accounted for the clinical profile of SARS-COV-2 (asymptomatic, mild, severe and critical cases) and avenues of treatment access (outpatient, and hospitalisation in non-ICU and ICU wards). Between end-March and early September 2020, the model was updated 11 times with four key releases to generate new sets of projections and scenario analyses to be shared with planners in the national and provincial Departments of Health, the National Treasury and other partners. Updates to model structure included finer spatial granularity, limited access to treatment, and the inclusion of behavioural heterogeneity in relation to the adoption of Public Health and Social Measures. These updates were made in response to local data and knowledge and the changing needs of the planners. The NCEM attempted to incorporate a high level of local data to contextualise the model appropriately to address South Africa's population and health system characteristics that played a vital role in producing and updating estimates of resource needs, demonstrating the importance of harnessing and developing local modelling capacity., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Silal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In response to the global pandemic of COVID-19, South Africa implemented a strict lockdown in March 2020 before its first COVID-19 wave started, gradually lifted restrictions between May and September 2020, and then re-imposed restrictions in December 2020 in response to its second wave. There is concern that COVID-19-related morbidity and mortality, the deprioritization of TB activities, fear of transmission, and societal restrictions led to a reduction in tuberculosis (TB) treatment initiations. We analysed monthly public sector, facility-level data from South Africa's District Health Information System (DHIS) from January 2019 to April 2021 to quantify changes in TB treatment initiation numbers stratified by province, setting, and facility type and compared the timing of these changes to COVID-19 case numbers and government lockdown levels. At the 1189 facilities that reported observations for all 28 months of our study period, TB treatment initiations in 2020 were 20.4% lower than in 2019 and 21.9% lower in the first four months of 2021 than in 2019. At the 3669 facilities that reported observations in ≤28 months, numbers of TB treatment initiations declined sharply in all provinces in May-August 2020, compared to the same months in 2019. After recovering somewhat in the last four months of 2020, numbers plummeted again in early 2021. Percentage reductions were somewhat larger in urban and peri-urban areas than in rural areas. Most provinces experienced a clear inverse relationship between COVID-19 cases and TB treatment initiations but little relationship between TB treatment initiations and lockdown level. The COVID-19 pandemic and responses to it resulted in substantial declines in the number of individuals starting treatment for TB in South Africa and risked progress toward achieving TB management goals. Exceptional effort will be needed to sustain gains in combating TB., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Benade et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)