Estimating transmission potential and risk factors for emerging infections often relies on incomplete surveillance data. When infections are missed or their sources are misclassified, estimates of the effective reproduction number (Rs) and associations between case attributes and infection source may be biased. We developed an analytic framework to quantify and correct these biases, treating imperfect observation and entanglement of transmission chains as forms of misclassification that distort estimates of transmission parameters. The bias depends on the probability of case observation (Pobs) and the extent of concurrent introductions (Rp). We illustrate this framework using surveillance data for mpox in the Democratic Republic of the Congo (1981-1986, 2013-2017), showing that incomplete observation leads to underestimation of Rs, whereas overlapping introductions can cause overestimation. Both types of bias also attenuate odds ratios for traits that distinguish primary (spillover) from secondary (human-tohuman) infections. Accounting for these biases provides context for the global emergence of mpox in 2022 and for how current transmission patterns differ from historical trends. These results demonstrate how standard epidemiologic concepts of misclassification and missingness can clarify bias in transmission studies and improve interpretation of surveillance data for emerging pathogens, including zoonoses and vaccine-preventable infections.

Given the high global seroprevalence of SARS-CoV-2, understanding the risk of reinfection has become increasingly important. Models developed to track trends in reinfection risk should be robust against possible biases arising from imperfect data observation processes. We performed simulation-based validation of an existing catalytic model designed to detect changes in the risk of reinfection by SARS-CoV-2. The 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. To assess the performance of the catalytic model, we simulated reinfection datasets that incorporated different processes that may bias inference, including imperfect observation and mortality. 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 using different magnitudes of change in reinfection risk. We assessed the model's 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. The 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 reinfections resulted in low numbers of observed cases from the simulated data and poor convergence. The model's 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). Ensuring 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.

Background Monitoring trends in multiple infections with SARS-CoV-2, following several pandemic waves, provides insight into the biological characteristics of new variants, but also necessitates methods to understand the risk of multiple reinfections. Objectives We generalised a catalytic model designed to detect increases in the risk of SARS-CoV-2 reinfection, to assess the population-level risk of multiple reinfections. Methods The catalytic model assumes the risk of reinfection is proportional to observed infections and uses a Bayesian approach to fit model parameters to the number of nth infections among individuals that occur at least 90 days after a previous infection. Using a posterior draw from the fitted model parameters, a 95% projection interval of daily nth infections is calculated under the assumption of a constant nth infection hazard coefficient. An additional model parameter was incorporated for the increased reinfection risk detected during the Omicron wave. The generalised model’s performance was then assessed using simulation-based validation. Key findings No additional increase in the risk of third infection was detected after the increase detected during the Omicron wave. Using simulation-based validation, we show that the model can successfully detect increases in the risk of third infections under different scenarios. Limitations Even though the generalised model is intended to detect the risk of nth infections, it is validated specifically for third infections, with its applicability for four or more infections being unconfirmed. Furthermore, the method’s sensitivity to low counts of nth infections, limits application in settings with small epidemics, limited testing coverage or early in an outbreak. Conclusions The catalytic model was successfully adapted to detect increases in the risk of nth infections, enhancing our capacity to identify future changes in the risk of nth infections by SARS-CoV-2 or other similar pathogens.

Introduction:Data on the burden of SARS-CoV-2 infections by age group and for different severity levels are lacking. We estimated the South African SARS-CoV-2 disease burden and severity, describing changes in the shape of the disease burden pyramid with successive waves. Methods:We estimated SARS-CoV-2 medically and non-medically attended illness stratified by severity (mild, severe non-fatal and fatal) during the initial five waves, spanning 1 March 2020 through 13 August 2022. We used individual-level national surveillance, healthcare utilisation and serosurvey data to estimate wave-specific hospitalisation-fatality (HFR) and infection-fatality ratios. We estimated wave-specific incidence rates per 100 000 population with 95% CIs derived from bootstrapping the individual-level data. Results:On 13 August 2022, the estimated cumulative number of SARS-CoV-2 infections in South Africa was 104.6 million, of which 399 900 (0.38%) were severe non-fatal and 258 800-289 000 (0.25 - 0.28%) fatal. 29% of severe non-fatal illness and 55 - 60% of deaths occurred outside the hospital. The highest burden of severe and fatal illness was during the Delta wave (wave 3), and the HFR across the initial three waves was similar (range 31-34%). Although there were more infections during the Omicron BA.1 wave (wave 4), there was a substantial reduction in HFR (14%). Successive waves saw a reduction in the rate of increase in mortality and hospitalisations with increasing age. Conclusions:The substantial South African national burden of SARS-CoV-2 for the initial five waves contradicts the belief of minimal impact in Africa. A high proportion of severe non-fatal and fatal illness occurred outside of the hospital, highlighting the importance of studies of health-seeking and vital registration systems to document the full burden of illness. The highest burden of severe illness and death was in the Delta wave. Following the Omicron emergence, severe illness reduced, and with successive waves, proportionately more children were infected and became ill, suggesting a transition to a more endemic pattern.

Epidemiological delays, such as incubation periods, serial intervals, and hospital lengths of stay, are among key quantities in infectious disease epidemiology that inform public health policy and clinical practice. This information is used to inform mathematical and statistical models, which in turn can inform control strategies. There are three main challenges that make delay distributions difficult to estimate. First, the data are commonly censored (e.g., symptom onset may only be reported by date instead of the exact time of day). Second, delays are often right truncated when being estimated in real time (not all events that have occurred have been observed yet). Third, during a rapidly growing or declining outbreak, overrepresentation or underrepresentation, respectively, of recently infected cases in the data can lead to bias in estimates. Studies that estimate delays rarely address all these factors and sometimes report several estimates using different combinations of adjustments, which can lead to conflicting answers and confusion about which estimates are most accurate. In this work, we formulate a checklist of best practices for estimating and reporting epidemiological delays with a focus on the incubation period and serial interval. We also propose strategies for handling common biases and identify areas where more work is needed. Our recommendations can help improve the robustness and utility of reported estimates and provide guidance for the evaluation of estimates for downstream use in transmission models or other analyses.

Epidemiological delays are key quantities that inform public health policy and clinical practice. They are used as inputs for mathematical and statistical models, which in turn can guide control strategies. In recent work, we found that censoring, right truncation, and dynamical bias were rarely addressed correctly when estimating delays and that these biases were large enough to have knock-on impacts across a large number of use cases. Here, we formulate a checklist of best practices for estimating and reporting epidemiological delays. We also provide a flowchart to guide practitioners based on their data. Our examples are focused on the incubation period and serial interval due to their importance in outbreak response and modeling, but our recommendations are applicable to other delays. The recommendations, which are based on the literature and our experience estimating epidemiological delay distributions during outbreak responses, can help improve the robustness and utility of reported estimates and provide guidance for the evaluation of estimates for downstream use in transmission models or other analyses.

In the context of polio eradication efforts, accurate assessment of vaccination programme effectiveness is essential to public health planning and decision making. Such assessments are often based on zero-dose children, estimated using the number of children who did not receive the first dose of the Diphtheria-Tetanus-Pertussis containing vaccine as a proxy. Our study introduces a novel approach to directly estimate the number of children susceptible to poliovirus type 2 (PV2) and uses this approach to provide district-level estimates for South Africa of susceptible children born between 2017 and 2022. We used district-level data on annual doses of inactivated poliovirus vaccine (IPV) administered, live births, and population sizes, from 2017 through 2022. We imputed missing vaccination data, implemented flexible assumptions regarding dose distribution in the eligible population, and used estimated efficacy values for one, two, three, and four doses of IPV, to compute the number of susceptible and immune children by birth year. We validated our approach by comparing an intermediary output with zero-dose children (ZDC) estimated using data reported by WHO/UNICEF Estimates of National Immunization Coverage (WUENIC). Our results indicate high heterogeneity in susceptibility to PV2 across South Africa's 52 districts as of the end of 2022. In children under 5 years, PV2 susceptibility ranged from approximately 30 % in districts including Xhariep (31.9 %), Ekurhuleni (30.1 %), and Central Karoo (29.8 %), to less than 4 % in Sarah Baartman (1.9 %), Buffalo City (2.1 %), and eThekwini (3.2 %). Our susceptibility estimates were consistently higher than ZDC over the timeframe. We estimated that ZDC decreased nationally from 155,168 (152,737-158,523) in 2017 to 108,593 in 2021, and increased to 127,102 in 2022, a trend consistent with ZDC derived from data reported by WUENIC. While our approach provides a more comprehensive profile of PV2 susceptibility, our susceptibility and ZDC estimates generally agree in the ranking of districts according to risk.

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.

The uptake of wastewater-based epidemiology (WBE) for SARS-CoV-2 as a complementary tool for monitoring population-level epidemiological features of the COVID-19 pandemic in low-and-middle-income countries (LMICs) is low. We report on the findings from the South African SARS-CoV-2 WBE surveillance network and make recommendations regarding the implementation of WBE in LMICs. Eight laboratories quantified influent wastewater collected from 87 wastewater treatment plants in all nine South African provinces from 01 June 2021 to 31 May 2022 inclusive, during the 3rd and 4th waves of COVID-19. Correlation and regression analyses between wastewater levels of SARS-CoV-2 and district laboratory-confirmed caseloads were conducted. The sensitivity and specificity of novel 'rules' based on WBE data to predict an epidemic wave were determined. Amongst 2158 wastewater samples, 543/648 (85 %) samples taken during a wave tested positive for SARS-CoV-2 compared with 842 positive tests from 1512 (55 %) samples taken during the interwave period. Overall, the regression-co-efficient was 0,66 (95 % confidence interval = 0,6-0,72, R-2 = 0.59), ranging from 0.14 to 0.87 by testing laboratory. Early warning of the 4th wave of SARS-CoV-2 in Gauteng Province in November-December 2021 was demonstrated. A 50 % increase in log copies of SARS-CoV-2 compared with a rolling mean over the previous five weeks was the most sensitive predictive rule (58 %) to predict a new wave. Our findings support investment in WBE for SARS-CoV-2 surveillance in LMICs as an early warning tool. Standardising test methodology is necessary due to varying correlation strengths across laboratories and redundancy across testing plants. A sentinel site model can be used for surveillance networks without affecting WBE finding for decision-making. Further research is needed to identify optimal test frequency and the need for normalisation to population size to identify predictive and interpretive rules to support early warning and public health action.

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.

Bats, rodents and monkeys are reservoirs for emerging zoonotic infections. We sought to describe the frequency of human exposure to these animals and the seasonal and geographic variation of these exposures in Bangladesh. During 2013–2016, we conducted a cross-sectional survey in a nationally representative sample of 10,002 households from 1001 randomly selected communities. We interviewed household members about exposures to bats, rodents and monkeys, including a key human–bat interface–raw date palm sap consumption. Respondents reported observing rodents (90%), bats (52%) and monkeys (2%) in or around their households, although fewer reported direct contact. The presence of monkeys around the household was reported more often in Sylhet division (7%) compared to other divisions. Households in Khulna (17%) and Rajshahi (13%) were more likely to report drinking date palm sap than in other divisions (1.5–5.6%). Date palm sap was mostly consumed during winter with higher frequencies in January (16%) and February (12%) than in other months (0–5.6%). There was a decreasing trend in drinking sap over the three years. Overall, we observed substantial geographic and seasonal patterns in human exposure to animals that could be sources of zoonotic disease. These findings could facilitate targeting emerging zoonoses surveillance, research and prevention efforts to areas and seasons with the highest levels of exposure.

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.

Understanding the protection conferred by previous infection against repeat infection, illness, and severe disease is key to projecting the future epidemiology of COVID-19 and to guiding vaccine policy decisions. In The Lancet, The COVID-19 Forecasting Team1COVID-19 Forecasting TeamPast SARS-CoV-2 infection protection against re-infection: a systematic review and meta-analysis.Lancet. 2023; (published online Feb 16.)https://doi.org/10.1016/S0140-6736(22)02465-5Google Scholar report data from a systematic review and meta-analysis of 65 studies from 19 different countries estimating the reduction in COVID-19 risk among individuals with previous SARS-CoV-2 infection, compared with those without a previous infection. Although there have been several previous systematic reviews that address this question, the current study adds substantial new information through the inclusion of an analysis of the change in protection conferred by previous infection with time since infection and an analysis stratified by SARS-CoV-2 variant. This analysis is particularly important following the emergence of the omicron variant in late 2021, with rapid spread globally. Currently, in most parts of the world, COVID-19 is dominated by different omicron sublineages, with ongoing emergence of new sublineages demonstrating the importance of potential immune escape.2Subissi L von Gottberg A Thukral L et al.An early warning system for emerging SARS-CoV-2 variants.Nat Med. 2022; 28: 1110-1115Crossref PubMed Scopus (16) Google Scholar The COVID-19 Forecasting Team1COVID-19 Forecasting TeamPast SARS-CoV-2 infection protection against re-infection: a systematic review and meta-analysis.Lancet. 2023; (published online Feb 16.)https://doi.org/10.1016/S0140-6736(22)02465-5Google Scholar found a high level of protection against reinfection and symptomatic disease for ancestral, alpha, beta, and delta variants (mean pooled estimate >82%) but substantially lower protection (approximately 45%) for the omicron BA.1 variant. Protection against severe disease was high for all variants evaluated (>85% at 40 weeks). Protection against reinfection with alpha, beta, and delta variants waned over time but remained higher than 75% at 40 weeks. By contrast, protection against reinfection by omicron BA.1 waned more rapidly, decreasing to 36% at 40 weeks. The data available for the omicron sublineages BA.2, BA.4, and BA.5, although limited, suggested that protection against these sublineages was lower if the past infection was with a pre-omicron variant compared with omicron; however, reinfection of those with a past omicron infection was higher for BA.4 and BA.5, highlighting the ongoing importance of immune evasion as a selective pressure driving the emergence of new subvariants. The key limitations of this study include the small number of studies done in low-income and middle-income countries, many of which have had high rates of SARS-CoV-2 infection, and the scarce data available on omicron BA.4 and BA.5 and other emerging sublineages. In addition, studies are needed with longer-term follow-up for the effects of waning protection and the protection conferred by repeated infections with different variants. The immune escape properties of omicron BA.1 were first detected by routine monitoring of population-level reinfection trends on the basis of epidemiological data.3Pulliam JRC van Schalkwyk C Govender N et al.Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa.Science. 2022; 376eabn4947Crossref PubMed Scopus (313) Google Scholar Although molecular surveillance and immunological analyses are essential for understanding the mechanisms underpinning immune evasion,4DeGrace MM Ghedin E Frieman MB et al.Defining the risk of SARS-CoV-2 variants on immune protection.Nature. 2022; 605: 640-652Crossref PubMed Scopus (51) Google Scholar the public health implications of viral evolution are fundamentally an epidemiological question. In the context of high population-level immunity, novel approaches are needed for sustained surveillance to assess the epidemiological consequences of new SARS-CoV-2 variants and sublineages, as well as mechanisms to support these platforms and ensure representation of low-income and middle-income countries. Potential approaches could include systematic repeated community testing, such as the innovative Office for National Statistics study in the UK,5Pouwels KB House T Pritchard E et al.Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey.Lancet Public Health. 2021; 6: e30-e38Summary Full Text Full Text PDF PubMed Scopus (70) Google Scholar repeated analyses of routinely collected large datasets,6Chemaitelly H Ayoub HH AlM S et al.Protection from previous natural infection compared with mRNA vaccination against SARS-CoV-2 infection and severe COVID-19 in Qatar: a retrospective cohort study.Lancet Microbe. 2022; 3: e944-e955Summary Full Text Full Text PDF PubMed Scopus (6) Google Scholar and longitudinal cohort studies.7Cohen C Kleynhans J von Gottberg A et al.SARS-CoV-2 incidence, transmission, and reinfection in a rural and an urban setting: results of the PHIRST-C cohort study, South Africa, 2020–21.Lancet Infect Dis. 2022; 22: 821-834Summary Full Text Full Text PDF PubMed Scopus (35) Google Scholar Routine monitoring for changes in epidemiological trends, such as disease severity and repeated infection with different variants in populations with well characterised immune histories, will be essential to maintain alongside platforms for ongoing molecular characterisation of the evolving virus. Although it is impossible to predict the long-term trajectory of SARS-CoV-2 circulation with certainty, the short duration of immunity combined with high transmissibility of the omicron variant and its sublineages hint that SARS-CoV-2 may not experience the kind of dynamic resonance that drives annual influenza epidemics.8Dushoff J Plotkin JB Levin SA Earn DJD Dynamical resonance can account for seasonality of influenza epidemics.Proc Natl Acad Sci USA. 2004; 101: 16915-16916Crossref PubMed Scopus (273) Google Scholar Although seasonal factors are likely to drive some variation in SARS-CoV-2 incidence throughout the year, the ratio of the peak incidence to the trough could be much closer to 1. Furthermore, in the long run, most infections will occur in people with strong protection against severe disease because of previous infection, vaccination, or both. Together, these results suggests that, similar to other human coronaviruses, there might be a low seasonal hospitalisation burden associated with SARS-CoV-2. The high and sustained levels of protection conferred by previous infection against severe disease have important implications for COVID-19 vaccine policy. By September, 2021, global SARS-CoV-2 seroprevalence was estimated at 59%, with substantial variation in the proportion of immunity induced by infection or vaccination in different settings.9Bergeri I Whelan MG Ware H et al.Global SARS-CoV-2 seroprevalence from January 2020 to April 2022: a systematic review and meta-analysis of standardized population-based studies.PLoS Med. 2022; 19e1004107Crossref PubMed Scopus (27) Google Scholar Seroprevalence in Africa was estimated at 87% in December, 2021, largely as a result of infection.9Bergeri I Whelan MG Ware H et al.Global SARS-CoV-2 seroprevalence from January 2020 to April 2022: a systematic review and meta-analysis of standardized population-based studies.PLoS Med. 2022; 19e1004107Crossref PubMed Scopus (27) Google Scholar High levels of immunity are an important contributor to the lower levels of severity observed with infection caused by emerging omicron subvariants.10Davies M-A Morden E Rousseau P et al.Outcomes of laboratory-confirmed SARS-CoV-2 infection during resurgence driven by Omicron lineages BA.4 and BA.5 compared with previous waves in the Western Cape Province, South Africa.Int J Infect Dis. 2022; 127: 63-68Summary Full Text Full Text PDF PubMed Scopus (8) Google Scholar As SARS-CoV-2 epidemiology shifts to more stable circulation patterns in the context of high levels of immunity, studies of the burden and cost of SARS-CoV-2 infection and risk groups for severe disease are needed to guide rational vaccination policy and decisions around prioritisation in relation to other vaccine-preventable diseases. CC reports grants paid to her institution from the US Centers for Disease Control and Prevention, the Sanofi Pasteur, PATH, the Bill & Melinda Gates Foundation, and the South African Medical Research Council unrelated to the topic discussed here, and a grant from the Wellcome Trust related to real-time monitoring of COVID-19 reinfections. JP reports grants paid to her institution from the Wellcome Trust and the South African Department of Science and Innovation-National Research Foundation broadly related to real-time monitoring of COVID-19 reinfections. Past SARS-CoV-2 infection protection against re-infection: a systematic review and meta-analysisProtection from past infection against re-infection from pre-omicron variants was very high and remained high even after 40 weeks. Protection was substantially lower for the omicron BA.1 variant and declined more rapidly over time than protection against previous variants. Protection from severe disease was high for all variants. The immunity conferred by past infection should be weighed alongside protection from vaccination when assessing future disease burden from COVID-19, providing guidance on when individuals should be vaccinated, and designing policies that mandate vaccination for workers or restrict access, on the basis of immune status, to settings where the risk of transmission is high, such as travel and high-occupancy indoor settings. Full-Text PDF Open Access

Background: Outbreak response modelling often involves collaboration among academics, and experts from governmental and non-governmental organizations. We conducted a systematic review of modelling studies on human vaccine-preventable disease (VPD) outbreaks to identify patterns in modelling practices between two collaboration types. We complemented this with a mini comparison of foot-and-mouth disease (FMD), a veterinary disease that is controllable by vaccination.Methods: We searched three databases for modelling studies that assessed the impact of an outbreak response. We extracted data on author affiliation type (academic institution, governmental, and non-governmental organizations), location studied, and whether at least one author was affiliated to the studied location. We also extracted the outcomes and interventions studied, and model characteristics. Included studies were grouped into two collaboration types: purely academic (papers with only academic affiliations), and mixed (all other combinations) to help investigate differences in modelling patterns between collaboration types in the human disease literature and overall differences with FMD collaboration practices.Results: Human VPDs formed 227 of 252 included studies. Purely academic collaborations dominated the human disease studies (56%). Notably, mixed collaborations increased in the last seven years (2013-2019). Most studies had an author affiliated to an institution in the country studied (75.2%) but this was more likely among the mixed collaborations. Contrasted to the human VPDs, mixed collaborations dominated the FMD literature (56%). Furthermore, FMD studies more often had an author with an affiliation to the country studied (92%) and used complex model design, including stochasticity, and model parametrization and validation.Conclusion: The increase in mixed collaboration studies over the past seven years could suggest an increase in the uptake of modelling for outbreak response decision-making. We encourage more mixed collaborations between academic and non-academic institutions and the involvement of locally affiliated authors to help ensure that the studies suit local contexts.

Background & aims of study Quantifying immunity to poliovirus serotypes in the South African population is valuable to inform public health resource allocation, particularly in the context of recent and ongoing outbreaks in the region. We aimed to estimate district-level humoral and mucosal immunity to wild poliovirus type 1 (WPV1), in five-year age bands, for each of South Africa’s 52 districts. Methods & results We used data on annual doses of Oral Poliovirus Vaccine (OPV) and Inactivated Poliovirus Vaccine (IPV) administered at the district level between 2009 and mid-2022, live births in these years, population sizes, and a nationally representative serosurvey of young children conducted in 1995. We used a combination of bootstrapping and proxy measures where data on doses administered were incomplete. We simulated distribution of vaccine doses to the eligible population and then estimated immunity levels by birth year, assuming a per-dose efficacy of 76% for OPV and n-dose efficacies for people who received 1-4 IPV doses. Mucosal immunity levels for individuals born between the 1995 serosurvey and 2009 were extrapolated for each realization by fitting a straight line from the survey-estimated immunity level to the level estimated based on 2009 data. Our results highlight that immunity to WPV1 is highly heterogeneous across districts. These estimates are a key input for the South African Centre for Epidemiological Modelling and Analysis (SACEMA) poliovirus transmission model. Implications Our approach allows us to estimate immunity from imperfect data, is applicable to other poliovirus serotypes, and can be easily adapted to similar data-poor settings for other diseases. Future work will prioritize the investigation of immunity to poliovirus type 2 in South Africa, given the recent emergence of circulating vaccine-derived poliovirus type 2 in the region. Moreover, we are developing a robust and transparent tool that will enable us to estimate immunity to a wide range of pathogens in diverse settings. This tool will enable the user to provide valuable insights into the immunological landscape, thereby facilitating targeted interventions.

Background - South Africa aims to transition from a two-tiered healthcare system (public and private) to universal health coverage. Data on red blood cell (RBC) product usage reveal disparities between the sectors. Blood transfusion services further need to understand differing disease profiles and transfusion prescribing practices between the sectors to ensure blood security should the transition to a two-tiered health system come to fruition. Materials and methods - Operational data for public and private healthcare RBC requests between 1 January 2014 and 31 March 2019, obtained from the South African National Blood Service (SANBS), were retrospectively analysed. Sector-specific demographic and utilisation trends were compared for the were also delineated for 2018. Results - Between 2014 and 2019, 2,356,411 public and private sector RBC transfusion events resulted in the issue of 4,020,094 RBC units (1,553,159 transfusion events and 2,495,054 units within the public sector versus 803,282 transfusion events and 1,525,040 units in private). The dominant clinical disciplines within the public sector were Medical (32.9%), Gynaecology/Obstetrics (27.3%), General Surgery (13.6%), and Paediatrics (including Paediatric Surgery) (6.5%), compared to Intensive Care Units (33.2%), Medical (28.3%), General Surgery (10.4%), and Haematology/Oncology (8.3%) in the private sector. Median pre-transfusion Hb values for 2018 were lower in the public than in the private sector: 6.9 g/dL public sector versus 8 g/dL private sector. Discussion - Clinical drivers of RBC usage within the public and private healthcare sectors in South Africa differ significantly. Disparate pre-transfusion Hb between the sectors are likely due to differing disease profiles and severity, as well as differences in practice in prescribing transfusions. Implementation of a nationally co-ordinated Patient Blood Management programme may help to address these disparities and help ensure a sustainable blood transfusion system.

By May 2021, South Africa (SA) had experienced two 'waves' of COVID-19 infections, with an initial peak of infections reached in July 2020, followed by a larger peak of infections in January 2021. Public health decisions rely on accurate and timely disease surveillance and epidemiological analyses, and accessibility of data at all levels of government is critical to inform stakeholders to respond effectively. In this paper, we describe the adaptation, development and operation of epidemiological surveillance and modelling systems in SA in response to the COVID-19 epidemic, including data systems for monitoring laboratory-confirmed COVID-19 cases, hospitalisations, mortality and recoveries at a national and provincial level, and how these systems were used to inform modelling projections and public health decisions. Detailed descriptions on the characteristics and completeness of individual datasets are not provided in this paper. Rapid development of robust data systems was necessary to support the response to the SA COVID-19 epidemic. These systems produced data streams that were used in decision-making at all levels of government. While much progress was made in producing epidemiological data, challenges remain to be overcome to address gaps to better prepare for future waves of COVID-19 and other health emergencies.

Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022. These data were used to estimate the time-varying reproduction number (R) in South Africa, as described in https://www.medrxiv.org/content/10.1101/2022.07.22.22277932v1.full.

OBJECTIVES:The aim of this study was 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:R was estimated from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalisations, and hospital-associated deaths, using a method that models daily incidence as a weighted sum of past incidence, as implemented in the R package EpiEstim. R was also estimated 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 higher during the fourth wave for case-based estimates. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave. CONCLUSION:Agreement between R estimates using different data sources during the first three waves suggests that data from any of these sources could be used in the early stages of a future pandemic. The high R estimates for Omicron relative to earlier waves are interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights that clients of the public and private sectors did not experience two separate epidemics, except perhaps to a limited extent during the strictest lockdowns in the first wave.