The National COVID-19 Epi Model (NCEM): Estimating cases, admissions and deaths for the first wave of COVID-19 in South Africa.

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.

Direct and Indirect Effects of the Coronavirus Disease 2019 Pandemic on Private Healthcare Utilization in South Africa, March 2020-September 2021.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused severe disruptions to healthcare in many areas of the world, but data remain scarce for sub-Saharan Africa. METHODS: We evaluated trends in hospital admissions and outpatient emergency department (ED) and general practitioner (GP) visits to South Africa's largest private healthcare system during 2016-2021. We fit time series models to historical data and, for March 2020-September 2021, quantified changes in encounters relative to baseline. RESULTS: The nationwide lockdown on 27 March 2020 led to sharp reductions in care-seeking behavior that persisted for 18 months after initial declines. For example, total admissions dropped 59.6% (95% confidence interval [CI], 52.4-66.8) during home confinement and were 33.2% (95% CI, 29-37.4) below baseline in September 2021. We identified 3 waves of all-cause respiratory encounters consistent with COVID-19 activity. Intestinal infections and non-COVID-19 respiratory illnesses experienced the most pronounced declines, with some diagnoses reduced 80%, even as nonpharmaceutical interventions (NPIs) relaxed. Non-respiratory hospitalizations, including injuries and acute illnesses, were 20%-60% below baseline throughout the pandemic and exhibited strong temporal associations with NPIs and mobility. ED attendances exhibited trends similar to those for hospitalizations, while GP visits were less impacted and have returned to pre-pandemic levels. CONCLUSIONS: We found substantially reduced use of health services during the pandemic for a range of conditions unrelated to COVID-19. Persistent declines in hospitalizations and ED visits indicate that high-risk patients are still delaying seeking care, which could lead to morbidity or mortality increases in the future.

Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa.

We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa's severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021.

Strategic testing approaches for targeted disease monitoring can be used to inform pandemic decision-making.

More than 1.6 million Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests were administered daily in the United States at the peak of the epidemic, with a significant focus on individual treatment. Here, we show that objective-driven, strategic sampling designs and analyses can maximize information gain at the population level, which is necessary to increase situational awareness and predict, prepare for, and respond to a pandemic, while also continuing to inform individual treatment. By focusing on specific objectives such as individual treatment or disease prediction and control (e.g., via the collection of population-level statistics to inform lockdown measures or vaccine rollout) and drawing from the literature on capture-recapture methods to deal with nonrandom sampling and testing errors, we illustrate how public health objectives can be achieved even with limited test availability when testing programs are designed a priori to meet those objectives.

The Role of Remdesivir in South Africa: Preventing COVID-19 Deaths Through Increasing Intensive Care Unit Capacity.

Countries such as South Africa have limited intensive care unit (ICU) capacity to handle the expected number of patients with COVID-19 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.

Cost-effectiveness of Remdesivir and Dexamethasone for COVID-19 Treatment in South Africa.

BACKGROUND: Dexamethasone and remdesivir have the potential to reduce coronavirus disease 2019 (COVID)-related mortality or recovery time, but their cost-effectiveness in countries with limited intensive care resources is unknown. METHODS: We 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 the cost-effectiveness of (1) administration of dexamethasone to ventilated patients and remdesivir to nonventilated patients, (2) dexamethasone alone to both nonventilated and ventilated patients, (3) remdesivir to nonventilated patients only, and (4) dexamethasone to ventilated patients only, all relative to a scenario of standard care. We estimated costs from the health care system perspective in 2020 US dollars, deaths averted, and the incremental cost-effectiveness ratios of each scenario. RESULTS: Remdesivir for nonventilated patients and dexamethasone for ventilated patients was estimated to result in 408 (uncertainty range, 229-1891) deaths averted (assuming no efficacy [uncertainty range, 0%-70%] of remdesivir) compared with standard care and to save $15 million. This result was driven by the efficacy of dexamethasone and the reduction of ICU-time required for patients treated with remdesivir. The scenario of dexamethasone alone for nonventilated and ventilated patients requires an additional $159 000 and averts 689 [uncertainty range, 330-1118] deaths, resulting in $231 per death averted, relative to standard care. CONCLUSIONS: The use of remdesivir for nonventilated patients and dexamethasone for ventilated patients is likely to be cost-saving compared with standard care by reducing ICU days. Further efforts to improve recovery time with remdesivir and dexamethasone in ICUs could save lives and costs in South Africa.

Cost-effectiveness of remdesivir and dexamethasone for COVID-19 treatment in South Africa.

Background South 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. Methods We 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. Results Remdesivir 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. Conclusions The 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.

Key questions for modelling COVID-19 exit strategies.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.