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 it 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.

BackgroundIt is frequently of epidemiological and/or clinical interest to estimate the date of HIV infection or time-since-infection of individuals. Yet, for over 15 years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the ‘Fiebig staging’ system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results using tests of different sensitivity. To develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests – as long as the test sensitivity is known. For this purpose, test sensitivity is the probability of a positive result as a function of time since infection.MethodsThe present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) curatorship of HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time ‘point estimates’ and referred to as Estimated Dates of Detectable Infection (EDDIs). The tool is designed for easy bulk processing of information (as may be appropriate for research studies) but can also be used for individual patients (such as in clinical practice).ResultsIn many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of diagnostic testing histories into infection time estimates, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic.ConclusionsThis tool, available at https://tools.incidence-estimation.org/idt/, is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.

In line with previous instalments of analysis from this ongoing study to monitor ‘Covid Seroprevalence’ among blood donors in South Africa, we report on an analysis of 3395 samples obtained in mid-March 2022 from all provinces of South Africa – a timepoint just after the fourth (primarily omicron) wave of infections. As in our previous analyses, we see no evidence of age and sex dependence of prevalence, but significant variation by race. Differences between provinces have largely disappeared, as prevalence appears to have saturated. In contrast to previous estimates from this study, which reported only prevalence of anti-nucleocapsid antibodies, this present work also reports results from testing for anti-spike antibodies. This addition allows us to categorise those donors whose only antibodies are from vaccination. Our race-weighted national extrapolation is that 98% of South Africans have some antibodies, noting that 10% have anti-spike antibodies but not anti-nucleocapsid antibodies - a reasonable proxy for having both 1) been vaccinated and 2) avoided infection.

Background The 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, from March 2020 to September 2021, quantified changes in encounters relative to baseline. Results The nationwide lockdown on 26 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: 52.4, 66.8] during home confinement and were 33.2% [29, 37.4] below baseline in September 2021. We identified three 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 non-pharmaceutical interventions (NPIs) were relaxed. Non-respiratory hospitalizations, including injuries and acute illnesses (e.g., heart attack, stroke), were 20-60% below baseline throughout the pandemic and exhibited strong temporal associations with NPIs and mobility behavior. ED attendances exhibited similar trends to hospitalizations, while GP visits, particularly for chronic illnesses and HIV, were less impacted and have returned to pre-pandemic levels. Conclusions We find 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.

In line with previous instalments of analysis from this ongoing study to monitor ‘Covid Seroprevalence’ among blood donors in South Africa, we report on an analysis of 3395 samples obtained in mid-March 2022 from all provinces of South Africa – a timepoint just after the fourth (primarily omicron) wave of infections. As in our previous analyses, we see no evidence of age and sex dependence of prevalence, but significant variation by race. Differences between provinces have largely disappeared, as prevalence appears to have saturated. In contrast to previous estimates from this study, which reported only prevalence of anti-nucleocapsid antibodies, this present work also reports results from testing for anti-spike antibodies. This addition allows us to categorise those donors whose only antibodies are from vaccination. Our race-weighted national extrapolation is that 98% of South Africans have some antibodies, noting that 10% have anti-spike antibodies but not anti-nucleocapsid antibodies - a reasonable proxy for having both 1) been vaccinated and 2) avoided infection.