An extended catalytic model to assess changes in risk for multiple reinfections with SARS-CoV-2 (preprint)

Background: The SARS-CoV-2 pandemic has illustrated that monitoring trends in multiple infections can provide insight into the biological characteristics of new variants. Following several pandemic waves, many people have already been infected and reinfected by SARS-CoV-2 and therefore methods are needed to understand the risk of multiple reinfections. Objectives: In this paper, we extended an existing catalytic model designed to detect increases in the risk of reinfection by SARS-CoV-2 to detect increases in 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 whose nth infection was observed at least 90 days before. 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 introduced to consider the increased risk of reinfection detected during the Omicron wave. Validation was performed to assess the model's ability to detect increases in the risk of third infections. Key Findings: The model parameters converged when applying the model's fitting and projection procedure to the number of observed third SARS-COV-2 infections in South Africa. No additional increase in the risk of third infection was detected after the increase detected during the Omicron wave. The validation of the third infections method showed that the model can successfully detect increases in the risk of third infections under different scenarios. Limitations: Even though the extended model is intended to detect the risk of nth infections, the method was only validated for detecting increases in the risk of third infections and not for four or more infections. The method is very sensitive to low numbers of nth infections, so it might not be usable in settings with small epidemics, low coverage of testing or early in an outbreak. Conclusions: The catalytic model to detect increases in the risk of reinfections was successfully extended to detect increases in the risk of nth infections and could contribute to future detection of increases in the risk of nth infections by SARS-CoV-2 or other similar pathogens.

The role of wastewater-based epidemiology for SARS-CoV-2 in developing countries: cumulative evidence from South Africa supports sentinel site surveillance to guide public health decision-making (preprint)

Background: The World Health Organisation recommends wastewater based epidemiology (WBE) for SARS-CoV-2 as a complementary tool for monitoring population-level epidemiological features of the COVID-19 pandemic. Yet, uptake of WBE in low-to-middle income countries (LMIC) is low. We report on findings from SARS-CoV-2 WBE surveillance network in South Africa, and make recommendations regarding implementation of WBE in LMICs Methods: Seven laboratories using different test methodology, quantified influent wastewater collected from 87 wastewater treatment plants (WWTPs) located in all nine South African provinces for SARS-CoV-2 from 01 June 2021 to 31 May 2022 inclusive, during the 3rd and 4th waves of COVID-19. Regression analysis with district laboratory confirmed SARS-CoV-2 case loads, controlling for district, size of plant and testing frequency was determined. The sensitivity and specificity of rules based on WBE data to predict an epidemic wave based on SARS-CoV-2 wastewater levels were determined. Results: Among 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 squared=0.59), but ranged 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 SARS-CoV-2 compared with a rolling mean over the previous 5 weeks was the most sensitive predictive rule (58%) to predict a new wave. Conclusion: Variation in the strength of correlation across testing laboratories, and redundancy of findings across co-located testing plants, suggests that test methodology should be standardised and that surveillance networks may utilise a sentinel site model without compromising the value of WBE findings for public health decision-making. Further research is needed to identify optimal test frequency and the need for normalisation to population size, so as to identify predictive and interpretive rules to support early warning and public health action. Our findings support investment in WBE for SARS-CoV-2 surveillance in low and middle-income countries.

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.

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.

The role of remdesivir in South Africa: preventing COVID-19 deaths through increasing ICU capacity (preprint)

Countries such as South Africa have limited intensive care unit (ICU) capacity to handle the expected number of COVID-19 patients requiring ICU care. Remdesivir can prevent deaths in countries such as South Africa by decreasing the number of days people spend in ICU, therefore freeing up ICU bed capacity.