COVID-19 vaccine uptake and effectiveness by time since vaccination in the Western Cape province, South Africa: An observational cohort study during 2020-2022 (preprint)

Background: There are few data on the real-world effectiveness of COVID-19 vaccines and boosting in Africa, which experienced high levels of SARS-CoV-2 infection in a mostly vaccine-naive population, and has limited vaccine coverage and competing health service priorities. We assessed the association between vaccination and severe COVID-19 in the Western Cape, South Africa. Methods We performed an observational cohort study of >2 million adults during 2020-2022. We described SARS-CoV-2 testing, COVID-19 outcomes, and vaccine uptake over time. We used multivariable cox models to estimate the association of BNT162b2 and Ad26.COV2.S vaccination with COVID-19-related hospitalisation and death, adjusting for demographic characteristics, underlying health conditions, socioeconomic status proxies and healthcare utilisation. Results By end 2022, only 41% of surviving adults had completed vaccination and 8% a booster dose, despite several waves of severe COVID-19. Recent vaccination was associated with notable reductions in severe COVID-19 during distinct analysis periods dominated by Delta, Omicron BA.1/2 and BA.4/5 (sub)lineages: within 6 months of completing vaccination or boosting, vaccine effectiveness was 46-92% for death (range across periods), 45-92% for admission with severe disease or death, and 25-90% for any admission or death. During the Omicron BA.4/5 wave, within 3 months of vaccination or boosting, BNT162b2 and Ad26.COV2.S were each 84% effective against death (95% CIs: 57-94 and 49-95, respectively). However, there were distinct reductions of VE at larger times post completing or boosting vaccination. Conclusions Continued emphasis on regular COVID-19 vaccination including boosting is important for those at high risk of severe COVID-19 even in settings with widespread infection-induced immunity.

Simulation-based validation of a method to detect changes in SARS-CoV-2 reinfection risk (preprint)

BackgroundGiven the high global seroprevalence of SARS-CoV-2, understanding the risk of reinfection becomes increasingly important. Models developed to track trends in reinfection risk should be robust against possible biases arising from imperfect data observation processes. ObjectivesWe performed simulation-based validation of an existing catalytic model designed to detect changes in the risk of reinfection by SARS-CoV-2. MethodsThe catalytic model assumes the risk of reinfection is proportional to observed infections. Validation involved using simulated primary infections, consistent with the number of observed infections in South Africa. We then simulated reinfection datasets that incorporated different processes that may bias inference, including imperfect observation and mortality, to assess the performance of the catalytic model. A Bayesian approach was used to fit the model to simulated data, assuming a negative binomial distribution around the expected number of reinfections, and model projections were compared to the simulated data generated using different magnitudes of change in reinfection risk. We assessed the approachs ability to accurately detect changes in reinfection risk when included in the simulations, as well as the occurrence of false positives when reinfection risk remained constant. Key FindingsThe model parameters converged in most scenarios leading to model outputs aligning with anticipated outcomes. The model successfully detected changes in the risk of reinfection when such a change was introduced to the data. Low observation probabilities (10%) of both primary- and re-infections resulted in low numbers of observed cases from the simulated data and poor convergence. LimitationsThe models performance was assessed on simulated data representative of the South African SARS-CoV-2 epidemic, reflecting its timing of waves and outbreak magnitude. Model performance under similar scenarios may be different in settings with smaller epidemics (and therefore smaller numbers of reinfections). ConclusionsEnsuring model parameter convergence is essential to avoid false-positive detection of shifts in reinfection risk. While the model is robust in most scenarios of imperfect observation and mortality, further simulation-based validation for regions experiencing smaller outbreaks is recommended. Caution must be exercised in directly extrapolating results across different epidemiological contexts without additional validation efforts.

The National COVID-19 Epi Model (NCEM): Estimating cases, admissions and deaths in South Africa (preprint)

Background: 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. Methods: 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). Results: Between end-March and early September 2020, the model was updated several times 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 in a variety of formats such as presentations, reports and dashboards. 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. Conclusions: The NCEM attempted to incorporate a high level of local data to contextualise the model appropriately to address South Africas population and health system characteristics. Origin and contextualisation of data and understanding of the populations interaction with the health system played a vital role in producing and updating estimates of resource needs, demonstrating the importance of harnessing and developing local modelling capacity.

The Role of Modelling and Analytics in South African COVID-19 Planning and Budgeting (preprint)

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 of time. 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 SACMCs models, developed rapidly in an emergency setting and regularly updated with local data, supported national and provincial government to plan several months ahead of time, 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.

Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves (preprint)

Objectives We aimed to quantify transmission trends in South Africa during the first four waves of the COVID-19 pandemic using estimates of the time-varying reproduction number (R) and to compare the robustness of R estimates based on three different data sources and using data from public and private sector service providers. Methods We estimated R from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalizations, and hospital-associated deaths, using a method which models daily incidence as a weighted sum of past incidence. We also estimated R separately using public and private sector data. Results Nationally, the maximum case-based R following the introduction of lockdown measures was 1.55 (CI: 1.43-1.66), 1.56 (CI: 1.47-1.64), 1.46 (CI: 1.38-1.53) and 3.33 (CI: 2.84-3.97) during the first (Wuhan-Hu), second (Beta), third (Delta), and fourth (Omicron) waves respectively. Estimates based on the three data sources (cases, hospitalisations, deaths) were generally similar during the first three waves but cases-based estimates were higher during the fourth wave. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave. Discussion Agreement between R estimates using different data sources during the first three waves suggests data from any of these sources could be used in early stages of a future pandemic. High R estimates for Omicron relative to earlier waves is interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights the fact that clients of the public and private sectors did not experience two separate epidemics, except perhaps to a limited extent during the strictest lockdowns during the first wave.

Clinical severity of SARS-CoV-2 Omicron BA.4 and BA.5 lineages in South Africa (preprint)

Omicron lineages BA.4 and BA.5 drove a fifth wave of COVID-19 cases in South Africa. We assessed the severity of BA.4/BA.5 infections using the presence/absence of the S-gene target for infections diagnosed using the TaqPath PCR assay between 1 October 2021 and 26 April 2022. We linked national COVID-19 individual-level data including case, laboratory test and hospitalisation data. We assessed severity using multivariable logistic regression comparing the risk of hospitalisation and risk of severe disease, once hospitalised, for Delta, BA.1, BA.2 and BA.4/BA.5 infections. After controlling for factors associated with hospitalisation and severe outcome respectively, BA.4/BA.5-infected individuals had a similar odds of hospitalisation (aOR1.24, 95% CI 0.98–1.55) and severe outcome (aOR 0.71, 95%CI 0.41–1.25) compared to BA.1-infected individuals. Newly emerged Omicron lineages BA.4/BA.5 continue to show reduced clinical severity compared to previous variants, as observed for Omicron BA.1.

Vaccine effectiveness against hospital admission in South African health care workers who received a homologous booster of Ad26.COV2 during an Omicron COVID19 wave: Preliminary Results of the Sisonke 2 Study. (preprint)

Following the results of the ENSEMBLE 2 study, which demonstrated improved vaccine efficacy of a two-dose regimen of Ad26.COV.2 vaccine given 2 months apart, we expanded the Sisonke study which had provided single dose Ad26.COV.2 vaccine to almost 500 000 health care workers (HCW) in South Africa to include a booster dose of the Ad26.COV.2. Sisonke 2 enrolled 227 310 HCW from the 8 November to the 17 December 2021. Enrolment commenced before the onset of the Omicron driven fourth wave in South Africa affording us an opportunity to evaluate early VE in preventing hospital admissions of a homologous boost of the Ad26.COV.2 vaccine given 6-9 months after the initial vaccination in HCW. We estimated vaccine effectiveness (VE) of the Ad26.COV2.S vaccine booster in 69 092 HCW as compared to unvaccinated individuals enrolled in the same managed care organization using a test negative design. We compared VE against COVID19 admission for omicron during the period 15 November to 20 December 2021. After adjusting for confounders, we observed that VE for hospitalisation increased over time since booster dose, from 63% (95%CI 31-81%); to 84% (95% CI 67-92%) and then 85% (95% CI: 54-95%), 0-13 days, 14-27 days, and 1-2 months post-boost. We provide the first evidence of the effectiveness of a homologous Ad26.COV.2 vaccine boost given 6-9 months after the initial single vaccination series during a period of omicron variant circulation. This data is important given the increased reliance on the Ad26.COV.2 vaccine in Africa.

Bounding the levels of transmissibility & immune evasion of the Omicron variant in South Africa (preprint)

A new SARS-CoV-2 variant of concern, Omicron (B.1.1.529), has been identified based on genomic sequencing and epidemiological data in South Africa. Presumptive Omicron cases in South Africa have grown extremely rapidly, despite high prior exposure and moderate vaccination coverage. The available evidence suggests that Omicron spread is at least in part due to evasion of this immune protection, though Omicron may also exhibit higher intrinsic transmissibility. Using detailed laboratory and epidemiological data from South Africa, we estimate the constraints on these two characteristics of the new variant and their relationship. Our estimates and associated uncertainties provide essential information to inform projection and scenario modeling analyses, which are crucial planning tools for governments around the world. One Sentence Summary We report a region of plausibility for the relative transmissibility and immune escape characteristics of the SARS-CoV-2 Omicron variant estimated by integrating laboratory and epidemiological data from South Africa.

Early assessment of the clinical severity of the SARS-CoV-2 Omicron variant in South Africa (preprint)

ABSTRACT Background The SARS-CoV-2 Omicron variant of concern (VOC) almost completely replaced other variants in South Africa during November 2021, and was associated with a rapid increase in COVID-19 cases. We aimed to assess clinical severity of individuals infected with Omicron, using S Gene Target Failure (SGTF) on the Thermo Fisher Scientific TaqPath COVID-19 PCR test as a proxy. Methods We performed data linkages for (i) SARS-CoV-2 laboratory tests, (ii) COVID-19 case data, (iii) genome data, and (iv) the DATCOV national hospital surveillance system for the whole of South Africa. For cases identified using Thermo Fisher TaqPath COVID-19 PCR, infections were designated as SGTF or non-SGTF. Disease severity was assessed using multivariable logistic regression models comparing SGTF-infected individuals diagnosed between 1 October to 30 November to (i) non-SGTF in the same period, and (ii) Delta infections diagnosed between April and November 2021. Results From 1 October through 6 December 2021, 161,328 COVID-19 cases were reported nationally; 38,282 were tested using TaqPath PCR and 29,721 SGTF infections were identified. The proportion of SGTF infections increased from 3% in early October (week 39) to 98% in early December (week 48). On multivariable analysis, after controlling for factors associated with hospitalisation, individuals with SGTF infection had lower odds of being admitted to hospital compared to non-SGTF infections (adjusted odds ratio (aOR) 0.2, 95% confidence interval (CI) 0.1-0.3). Among hospitalised individuals, after controlling for factors associated with severe disease, the odds of severe disease did not differ between SGTF-infected individuals compared to non-SGTF individuals diagnosed during the same time period (aOR 0.7, 95% CI 0.3-1.4). Compared to earlier Delta infections, after controlling for factors associated with severe disease, SGTF-infected individuals had a lower odds of severe disease (aOR 0.3, 95% CI 0.2-0.5). Conclusion Early analyses suggest a reduced risk of hospitalisation among SGTF-infected individuals when compared to non-SGTF infected individuals in the same time period. Once hospitalised, risk of severe disease was similar for SGTF- and non-SGTF infected individuals, while SGTF-infected individuals had a reduced risk of severe disease when compared to earlier Delta-infected individuals. Some of this reducton is likely a result of high population immunity.

Breakthrough Covid-19 infections during periods of circulating Beta, Delta and Omicron variants of concern, among health care workers in the Sisonke Ad26.COV2.S vaccine trial, South Africa (preprint)

Background We report breakthrough infections (BTIs) during periods of circulating Beta, Delta and Omicron variants of concern, among health care workers (HCW) participating in the Sisonke phase 3B Ad26.COV2.S vaccine trial ( ClinicalTrials.gov number, NCT04838795 ). Data were gathered between 17 February and 15 December 2021. Duration of each period in this study was 89 days for Beta, 180 days or Delta and 30 days for Omicron. Results A total of 40 538 BTIs were observed, with 609 during Beta, 22 279 during Delta and 17 650 during Omicron. By 15 December, daily infections during Omicron were three times that seen during the peak observed during Delta. However, unlike the Delta period, with Omicron there was a clear and early de-coupling of hospitalisation from cases as a percentage of the Delta peak curves. Omicron significantly infected a greater proportion of HCW in the 18-30 year age-group, compared with the 55+ age group. There were 1 914 BTI-related hospitalisations - 77, 1 429 and 408 in the Beta (89 days), Delta (180 days) and Omicron (30 days) periods, respectively. During Omicron, 91% hospitalized HCWs required general ward care, 6% high care and 3% intensive care, compared with 89% general ward care, 4% high care and 7% intensive care, during Delta and 78% general care, 7% high care and 16% intensive care during Beta (p<0.001). During Beta and Beta 43% of hospitalized HCW needed supplementary oxygen and 7-8% needed ventilation, compared with 16% and 0.2% respectively during the Omicron period (p<0.001). Median length of hospitalization was significantly lower with Omicron compared with Beta and Delta (3 days compared with 5-6 days, p<0.001). Conclusions We illustrate more BTIs but reassuringly less severe Covid-19 with Omicron. Re-infections and Omicron-driven primary infections were likely driven by high population SARS-CoV-2 seroprevalence, waning vaccine effectiveness over time, increased Omicron infectivity, Omicron immune evasion or a combination of these and need further investigation. Follow-up of this cohort will continue and reports will be updated, as time and infections accrue.

Safety of the single-dose Ad26.CoV2.S vaccine among healthcare workers in the phase 3b Sisonke study in South Africa (preprint)

Background: The Sisonke openlabel phase 3b implementation study aimed to assess the safety and effectiveness of the Janssen Ad26.CoV2.S vaccine among health care workers (HCWs) in South Africa. Here, we present the safety data. Methods: We monitored adverse events (AEs) at vaccination sites, through self reporting triggered by text messages after vaccination, health care provider reports and by active case finding. The frequency and incidence rate of non serious and serious AEs were evaluated from day of first vaccination (17 February 2021) until 28 days after the final vaccination (15 June 2021). COVID 19 breakthrough infections, hospitalisations and deaths were ascertained via linkage of the electronic vaccination register with existing national databases. Findings: Of 477,234 participants, 10,279 (2.2%) reported AEs, of which 139 (1.4%) were serious. Women reported more AEs than men (2.3% vs. 1.6%). AE reports decreased with increasing age (3.2% for 18 to 30, 2.1% for 31 to 45, 1.8% for 46 to 55 and 1.5% in >55 year olds). Participants with previous COVID 19 infection reported slightly more AEs (2.6% vs. 2.1%). The commonest reactogenicity events were headache and body aches, followed by injection site pain and fever, and most occurred within 48 hours of vaccination. Two cases of Thrombosis with Thrombocytopenia Syndrome and four cases of Guillain Barre Syndrome were reported post-vaccination. Serious AEs and AEs of special interest including vascular and nervous system events, immune system disorders and deaths occurred at lower than the expected population rates. Interpretation: The single-dose Ad26.CoV2.S vaccine had an acceptable safety profile supporting the continued use of this vaccine in our setting.

SARS-CoV-2 reinfection trends in South Africa: analysis of routine surveillance data (preprint)

Objective: To examine whether SARS-CoV-2 reinfection risk has changed through time in South Africa, in the context of the emergence of the Beta and Delta variants Design: Retrospective analysis of routine epidemiological surveillance data Setting: Line list data on SARS-CoV-2 with specimen receipt dates between 04 March 2020 and 30 June 2021, collected through South Africa's National Notifiable Medical Conditions Surveillance System Participants: 1,551,655 individuals with laboratory-confirmed SARS-CoV-2 who had a positive test result at least 90 days prior to 30 June 2021. Individuals having sequential positive tests at least 90 days apart were considered to have suspected reinfections. Main outcome measures: Incidence of suspected reinfections through time; comparison of reinfection rates to the expectation under a null model (approach 1); empirical estimates of the time-varying hazards of infection and reinfection throughout the epidemic (approach 2) Results: 16,029 suspected reinfections were identified. The number of reinfections observed through the end of June 2021 is consistent with the null model of no change in reinfection risk (approach 1). Although increases in the hazard of primary infection were observed following the introduction of both the Beta and Delta variants, no corresponding increase was observed in the reinfection hazard (approach 2). Contrary to expectation, the estimated hazard ratio for reinfection versus primary infection was lower during waves driven by the Beta and Delta variants than for the first wave (relative hazard ratio for wave 2 versus wave 1: 0.75 (95% CI: 0.59-0.97); for wave 3 versus wave 1: 0.70 (95% CI: 0.55-0.90)). Although this finding may be partially explained by changes in testing availability, it is also consistent with a scenario in which variants have increased transmissibility but little or no evasion of immunity. Conclusion: We conclude there is no population-wide epidemiological evidence of immune escape and recommend ongoing monitoring of these trends.

Cost-effectiveness of remdesivir and dexamethasone for COVID-19 treatment in South Africa (preprint)

BackgroundSouth Africa recently experienced a first peak in COVID-19 cases and mortality. Dexamethasone and remdesivir both have the potential to reduce COVID-related mortality, but their cost-effectiveness in a resource-limited setting with scant intensive care resources is unknown. MethodsWe projected intensive care unit (ICU) needs and capacity from August 2020 to January 2021 using the South African National COVID-19 Epi Model. We assessed cost-effectiveness of 1) administration of dexamethasone to ventilated patients and remdesivir to non-ventilated patients, 2) dexamethasone alone to both non-ventilated and ventilated patients, 3) remdesivir to non-ventilated patients only, and 4) dexamethasone to ventilated patients only; all relative to a scenario of standard care. We estimated costs from the healthcare system perspective in 2020 USD, deaths averted, and the incremental cost effectiveness ratios of each scenario. ResultsRemdesivir for non-ventilated patients and dexamethasone for ventilated patients was estimated to result in 1,111 deaths averted (assuming a 0-30% efficacy of remdesivir) compared to standard care, and save $11.5 million. The result was driven by the efficacy of the drugs, and the reduction of ICU-time required for patients treated with remdesivir. The scenario of dexamethasone alone to ventilated and non-ventilated patients requires additional $159,000 and averts 1,146 deaths, resulting in $139 per death averted, relative to standard care. ConclusionsThe use of dexamethasone for ventilated and remdesivir for non-ventilated patients is likely to be cost-saving compared to standard care. Given the economic and health benefits of both drugs, efforts to ensure access to these medications is paramount. 40-word summary of articles main pointThe use of remdesivir and dexamethasone for treatment of severe COVID-19 in South Africa is likely to be cost-saving relative to standard care. Enabling access to these medications should be prioritize to improve patient outcomes and reduce total costs.