The COVID-19 pandemic coincided with rising secondary bloodstream infections (BSIs) from multidrug-resistant organisms, including Candida auris. To assess candidemia trends, we conducted a retrospective analysis of blood culture isolates from public and private laboratories in South Africa taken during January 2019-June 2022. We evaluated weekly aggregated Candida BSI counts and COVID-19 cases using segmented regression within an interrupted time-series framework. In total, 15,393 candidemia cases were identified, 70% from the private sector. C. parapsilosis accounted for 39% of cases, whereas C. auris represented 26%. The proportion of C. auris increased significantly from 17% in 2019 to 31% in 2021 (p<0.01). After the pandemic onset, Candida BSIs rose by 11 cases per week (p = 0.03), largely driven by C. auris (+5 cases/week; p<0.01); peaks coincided with COVID-19 waves. Those results highlight an accelerated shift toward C. auris in Candida BSIs and the urgent need for enhanced surveillance, diagnostics, and infection prevention.

BackgroundSince there are currently no specific SARS-CoV-2 prognostic viral biomarkers for predicting disease severity, there has been interest in using SARS-CoV-2 polymerase chain reaction (PCR) cycle-threshold (Ct) values to predict disease progression.ObjectiveThis study assessed the association between in-hospital mortality of hospitalized COVID-19 cases and Ct-values of gene targets specific to SARS-CoV-2.MethodsClinical data of hospitalized COVID-19 cases from Gauteng Province from April 2020-July 2022 were obtained from a national surveillance system and linked to laboratory data. The study period was divided into pandemic waves: Asp614Gly/wave1 (7 June–22 Aug 2020); beta/wave2 (15 Nov 2020–6 Feb 2021); delta/wave3 (9 May–18 Sept 2021) and omicron/wave4 (21 Nov 2021–22 Jan 2022). Ct-value data of genes specific to SARS-CoV-2 according to testing platforms (Roche-ORF gene; GeneXpert-N2 gene; Abbott-RdRp gene) were categorized as low (Ct < 20), mid (Ct20–30) or high (Ct > 30).ResultsThere were 1205 recorded cases: 836(69.4%; wave1), 122(10.1%;wave2) 21(1.7%; wave3) and 11(0.9%;in wave4). The cases' mean age(±SD) was 49 years(±18), and 662(54.9%) were female. There were 296(24.6%) deaths recorded: 241(81.4%;wave1), 27 (9.1%;wave2), 6 (2%;wave3), and 2 (0.7%;wave4) (p < 0.001). Sample distribution by testing platforms was: Roche 1,033 (85.7%), GeneXpert 169 (14%) and Abbott 3 (0.3%). The median (IQR) Ct-values according to testing platform were: Roche 26 (22–30), GeneXpert 38 (36–40) and Abbott 21 (16–24). After adjusting for sex, age and presence of a comorbidity, the odds of COVID-19 associated death were high amongst patients with Ct values 20–30[adjusted Odds Ratio (aOR) 2.25; 95% CI: 1.60–3.18] and highest amongst cases with Ct-values <20 (aOR 3.18; 95% CI: 1.92–5.27), compared to cases with Ct-values >30.ConclusionAlthough odds of COVID19-related death were high amongst cases with Ct-values <30, Ct values were not comparable across different testing platforms, thus precluding the comparison of SARS-CoV-2 Ct-value results.

Background In 2021, the HIV prevalence among South African adults was 18% and more than 2 million people had uncontrolled HIV and, therefore, had increased risk of poor outcomes with SARS-CoV-2 infection. We investigated trends in COVID-19 admissions and factors associated with in -hospital COVID-19 mortality among people living with HIV and people without HIV. Methods In this analysis of national surveillance data, we linked and analysed data collected between March 5, 2020, and May 28, 2022, from the DATCOV South African national COVID-19 hospital surveillance system, the SARS-CoV-2 case line list, and the Electronic Vaccination Data System. All analyses included patients hospitalised with SARS-CoV-2 with known in -hospital outcomes (ie, who were discharged alive or had died) at the time of data extraction. We used descriptive statistics for admissions and mortality trends. Using post -imputation randomeffect multivariable logistic regression models, we compared characteristics and the case fatality ratio of people with HIV and people without HIV. Using modified Poisson regression models, we compared factors associated with mortality among all people with COVID-19 admitted to hospital and factors associated with mortality among people with HIV. Findings: Among 397 082 people with COVID-19 admitted to hospital, 301 407 (759%) were discharged alive, 89 565 (226%) died, and 6110 (15%) had no recorded outcome. 270 737 (682%) people with COVID-19 had documented HIV status (22 858 with HIV and 247 879 without). Comparing characteristics of people without HIV and people with HIV in each COVID-19 wave, people with HIV had increased odds of mortality in the D614G (adjusted odds ratio 119, 95% CI 109-129), beta (108, 101-116), delta (110, 103-118), omicron BA.1 and BA.2 (171, 154-190), and omicron BA.4 and BA.5 (181, 141-233) waves. Among all COVID-19 admissions, mortality was lower among people with previous SARS-CoV-2 infection (adjusted incident rate ratio 032, 95% CI 029-034) and with partial (093, 090-096), full (070, 067-073), or boosted (050, 041-062) COVID-19 vaccination. Compared with people without HIV who were unvaccinated, people without HIV who were vaccinated had lower risk of mortality (068, 065-071) but people with HIV who were vaccinated did not have any difference in mortality risk (108, 096-123). In-hospital mortality was higher for people with HIV with CD4 counts less than 200 cells per mu L, irrespective of viral load and vaccination status. Interpretation HIV and immunosuppression might be important risk factors for mortality as COVID-19 becomes endemic.

ABSTRACTBackgroundA third of people may experience persistent symptoms following COVID-19. With over 90% of South Africans having evidence of prior SARS-CoV-2 infection, it is likely that many people could be affected by Post COVID-19 Condition (PCC).MethodsThe was a prospective, longitudinal observational cohort study recruiting hospitalised and non-hospitalised participants, infected during the periods that Beta, Delta and Omicron BA.1 variants dominated in South Africa. Participants aged 18 years or older were randomly selected to undergo telephone assessment at 1, 3 and 6 months after hospital discharge or laboratory-confirmed SARS-CoV-2 infection. Participants were assessed using a standardised questionnaire for evaluation of symptoms and health-related quality of life. We used negative binomial regression models to determine factors associated with the presence of ≥1 symptoms at 6 months.FindingsAmong hospitalised and non-hospitalised participants, 46.7% (1,227/2,626) and 18.5% (199/1,074) had ≥1 symptoms at 6 months (p=<0.001). Among hospitalised participants 59.5%, 61.2% and 18.5% experienced ≥1 symptoms at 6 months among individuals infected during the Beta, Delta and Omicron dominant waves respectively. Among PLWH who were hospitalised, 40.4% had ≥1 symptoms at 6 months compared to 47.1% among HIV-uninfected participants (p=0.108).Risk factors for PCC included older age, female sex, non-black race, the presence of a comorbidity, greater number of acute COVID-19 symptoms, hospitalisation/ COVID-19 severity and wave period (individuals infected during the Omicron-dominated wave had a lower risk of persistent symptoms [adjusted Incident Risk Ratio 0.45; 95% Confidence Interval 0.36 – 0.57] compared to those infected during the Beta-dominated wave). There were no associations between self-reported vaccination status before or after SARS-CoV-2 infection with persistent symptoms.InterpretationThe study revealed a high prevalence of persistent symptoms among South African participants at 6 months although decreased risk for PCC among participants infected during the Omicron BA.1 wave. These findings have serious implications for countries with resource-constrained healthcare systems.FundingBill & Melinda Gates Foundation, UK Foreign, Commonwealth & Development Office, and Wellcome.

We conducted an epidemiologic survey to determine the seroprevalence of SARS-CoV-2 anti-nucleocapsid (anti-N) and anti-spike (anti-S) protein IgG from 1 March to 11 April 2022 after the BA.1-dominant wave had subsided in South Africa and prior to another wave dominated by the BA.4 and BA.5 (BA.4/BA.5) sub-lineages. We also analysed epidemiologic trends in Gauteng Province for cases, hospitalizations, recorded deaths, and excess deaths were evaluated from the inception of the pandemic through 17 November 2022. Despite only 26.7% (1995/7470) of individuals having received a COVID-19 vaccine, the overall seropositivity for SARS-CoV-2 was 90.9% (95% confidence interval (CI), 90.2 to 91.5) at the end of the BA.1 wave, and 64% (95% CI, 61.8 to 65.9) of individuals were infected during the BA.1-dominant wave. The SARS-CoV-2 infection fatality risk was 16.5–22.3 times lower in the BA.1-dominant wave compared with the pre-BA.1 waves for recorded deaths (0.02% vs. 0.33%) and estimated excess mortality (0.03% vs. 0.67%). Although there are ongoing cases of COVID-19 infections, hospitalization and death, there has not been any meaningful resurgence of COVID-19 since the BA.1-dominant wave despite only 37.8% coverage by at least a single dose of COVID-19 vaccine in Gauteng, South Africa.

Abstract Background In this study, we compared admission incidence risk and the risk of mortality in the Omicron BA.4/BA.5 wave to previous waves. Methods Data from South Africa's SARS-CoV-2 case linelist, national COVID-19 hospital surveillance system, and Electronic Vaccine Data System were linked and analyzed. Wave periods were defined when the country passed a weekly incidence of 30 cases/100 000 population. In-hospital case fatality ratios (CFRs) during the Delta, Omicron BA.1/BA.2, and Omicron BA.4/BA.5 waves were compared using post-imputation random effect multivariable logistic regression models. Results The CFR was 25.9% (N = 37 538 of 144 778), 10.9% (N = 6123 of 56 384), and 8.2% (N = 1212 of 14 879) in the Delta, Omicron BA.1/BA.2, and Omicron BA.4/BA.5 waves, respectively. After adjusting for age, sex, race, comorbidities, health sector, and province, compared with the Omicron BA.4/BA.5 wave, patients had higher risk of mortality in the Omicron BA.1/BA.2 wave (adjusted odds ratio [aOR], 1.3; 95% confidence interval [CI]: 1.2–1.4) and Delta wave (aOR, 3.0; 95% CI: 2.8–3.2). Being partially vaccinated (aOR, 0.9; 95% CI: .9–.9), fully vaccinated (aOR, 0.6; 95% CI: .6–.7), and boosted (aOR, 0.4; 95% CI: .4–.5) and having prior laboratory-confirmed infection (aOR, 0.4; 95% CI: .3–.4) were associated with reduced risks of mortality. Conclusions Overall, admission incidence risk and in-hospital mortality, which had increased progressively in South Africa's first 3 waves, decreased in the fourth Omicron BA.1/BA.2 wave and declined even further in the fifth Omicron BA.4/BA.5 wave. Mortality risk was lower in those with natural infection and vaccination, declining further as the number of vaccine doses increased.

Background The first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus and deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns in in-hospital deaths have not fully been investigated in South Africa. This study uses national COVID-19 hospitalization data to investigate the spatial effects on hospital deaths after adjusting for known mortality risk factors. Methods COVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD). Generalized structured additive logistic regression model was used to assess spatial effects on COVID-19 in-hospital deaths adjusting for demographic and clinical covariates. Continuous covariates were modelled by assuming second-order random walk priors, while spatial autocorrelation was specified with Markov random field prior and fixed effects with vague priors respectively. The inference was fully Bayesian. Results The risk of COVID-19 in-hospital mortality increased with patient age, with admission to intensive care unit (ICU) (aOR = 4.16; 95% Credible Interval: 4.05–4.27), being on oxygen (aOR = 1.49; 95% Credible Interval: 1.46–1.51) and on invasive mechanical ventilation (aOR = 3.74; 95% Credible Interval: 3.61–3.87). Being admitted in a public hospital (aOR = 3.16; 95% Credible Interval: 3.10–3.21) was also significantly associated with mortality. Risk of in-hospital deaths increased in months following a surge in infections and dropped after months of successive low infections highlighting crest and troughs lagging the epidemic curve. After controlling for these factors, districts such as Vhembe, Capricorn and Mopani in Limpopo province, and Buffalo City, O.R. Tambo, Joe Gqabi and Chris Hani in Eastern Cape province remained with significantly higher odds of COVID-19 hospital deaths suggesting possible health systems challenges in those districts. Conclusion The results show substantial COVID-19 in-hospital mortality variation across the 52 districts. Our analysis provides information that can be important for strengthening health policies and the public health system for the benefit of the whole South African population. Understanding differences in in-hospital COVID-19 mortality across space could guide interventions to achieve better health outcomes in affected districts.

ABSTRACTBackgroundWe conducted a seroepidemiological survey from October 22 to December 9, 2021, in Gauteng Province, South Africa, to determine SARS-CoV-2 immunoglobulin G (IgG) seroprevalence primarily before the fourth wave of coronavirus disease 2019 (Covid-19), in which the B.1.1.529 (Omicron) variant was dominant. We evaluated epidemiological trends in case rates and rates of severe disease through to January 12, 2022, in Gauteng.MethodsWe contacted households from a previous seroepidemiological survey conducted from November 2020 to January 2021, plus an additional 10% of households using the same sampling framework. Dry blood spot samples were tested for anti-spike and anti-nucleocapsid protein IgG using quantitative assays on the Luminex platform. Daily case, hospital admission, and reported death data, and weekly excess deaths, were plotted over time.ResultsSamples were obtained from 7010 individuals, of whom 1319 (18.8%) had received a Covid-19 vaccine. Overall seroprevalence ranged from 56.2% (95% confidence interval [CI], 52.6 to 59.7) in children aged <12 years to 79.7% (95% CI, 77.6 to 81.5) in individuals aged >50 years. Seropositivity was more likely in vaccinated (93.1%) vs unvaccinated (68.4%) individuals. Epidemiological data showed SARS-CoV-2 infection rates increased and subsequently declined more rapidly than in previous waves. Infection rates were decoupled from Covid-19 hospitalizations, recorded deaths, and excess deaths relative to the previous three waves.ConclusionsWidespread underlying SARS-CoV-2 seropositivity was observed in Gauteng Province before the Omicron-dominant wave. Epidemiological data showed a decoupling of hospitalization and death rates from infection rate during Omicron circulation.

Background: Assessment of disease severity associated with a novel pathogen or variant provides critical information needed by public health agencies and governments to develop appropriate responses. The SARS-CoV-2 Omicron Variant of Concern (VOC) spread rapidly through populations worldwide before robust epidemiological and laboratory data were available to investigate its relative severity. Here we develop a set of methods that make use of non-linked, aggregate data to answer questions of severity and variant dynamics.Methods: Using data from the National Institute for Communicable Disease in South Africa, we determined lag intervals most consistent with time from case ascertainment to hospital admission and within-hospital death. We then utilize these intervals to estimate and compare case hospitalization and case fatality ratios across the four epidemic waves that South Africa has faced, each dominated by a different VOC.Findings: We find that lag intervals and disease severity are age-dependent and have varied throughout the pandemic as different VOCs have driven infections. At an aggregate level, fluctuations in cases are generally followed by a similar trend in hospitalizations within 7 days and deaths within 15 days. We note a marked reduction in disease severity throughout the Omicron period relative to prior waves, most significant in older age groups.Interpretation: These methods provide useful estimates of the impact of novel SARS-CoV-2 VOCs, especially for application in settings where resources and access to individual-level data are limited.Funding: National Institute for Communicable Diseases of South Africa, South African National GovernmentDeclaration of Interest: WH reports his position as a member of Biobot Analytics’ scientific advisory board and has received stock options in Biobot Analytics, as well as payment for expert witness testimony on the expected course of the COVID-19 pandemic. DS reports previous employment at Pfizer Inc. prior to the initiation of this analysis and stock in Pfizer Inc. DS also reports compensation for occasional one-hour blinded consultancies for several consulting companies over the past 36 months. MS reports receipt of institutional research funds from the Johnson and Johnson Foundation and from Pfizer Inc.; neither funder had a role in the design or content of the current manuscript. CC reports grant funding from the Wellcome Trust, South Africa MRC, US CDC, and Sanofi Pasteur to institute COVID-19 research in the past 36 months, as well as a role on the scientific advisory committee for “BCHW: Burden of COVID-19 among health care workers, assessing infection, risk factors, vaccine effectiveness, working experiences and one-health implications: a mixed methodology, multisite international study”. RW reports shareholding stock in the following health/pharmaceutical companies in South Africa, none related to this work: Adcock Ingram Holdings Ltd, Dischem Pharmacies Ltd, Discovery Ltd, Netcare Ltd, Aspen Pharmacare Holdings Ltd. All interests listed are outside the current work. All other authors declare no competing interests. Ethical Approval: Ethics approval was not required for this study; data obtained through the data-sharing agreement between study authors and utilized in the analysis was aggregated and properly de-identified.

Background Up to the end of January, 2022, South Africa has had four recognisable COVID-19 pandemic waves, each predominantly dominated by one variant of concern: the ancestral strain with an Asp614Gly mutation during the first wave, the beta variant (B.1.351) during the second wave, the delta variant (B.1.617.2) during the third wave, and lastly, the omicron variant (B.1.1.529) during the fourth wave. We aimed to assess the clinical disease severity of patients admitted to hospital with SARS-CoV-2 infection during the omicron wave and compare the findings with those of the preceding three pandemic waves in South Africa. Methods We defined the start and end of each pandemic wave as the crossing of the threshold of weekly incidence of 30 laboratory-confirmed SARS-CoV-2 cases per 100 000 population. Hospital admission data were collected through an active national COVID-19-specific surveillance programme. We compared disease severity across waves by postimputation random effect multivariable logistic regression models. Severe disease was defined as one or more of the following: acute respiratory distress, receipt of supplemental oxygen or mechanical ventilation, admission to intensive care, or death. Findings We analysed 335 219 laboratory-confirmed SARS-CoV-2 hospital admissions with a known outcome, constituting 10.4% of 3 216 179 cases recorded during the four waves. During the omicron wave, 52 038 (8.3%) of 629 617 cases were admitted to hospital, compared with 71 411 (12.9%) of 553 530 in the Asp614Gly wave, 91 843 (12.6%) of 726 772 in the beta wave, and 131 083 (10.0%) of 1 306 260 in the delta wave (p<0.0001). During the omicron wave, 15 421 (33.6%) of 45 927 patients admitted to hospital had severe disease, compared with 36 837 (52.3%) of 70 424 in the Asp614Gly wave, 57 247 (63.4%) of 90 310 in the beta wave, and 81 040 (63.0%) of 128 558 in the delta wave (p<0.0001). The in-hospital case-fatality ratio during the omicron wave was 10.7%, compared with 21.5% during the Asp614Gly wave, 28.8% during the beta wave, and 26.4% during the delta wave (p<0.0001). Compared with those admitted to hospital during the omicron wave, patients admitted during the other three waves had more severe clinical presentations (adjusted odds ratio 2.07 [95% CI 2.01-2.13] in the Asp614Gly wave, 3.59 [3.49-3.70] in the beta wave, and 3.47 [3.38-3.57] in the delta wave). Interpretation The trend of increasing cases and admissions across South Africa's first three waves shifted in the omicron wave, with a higher and quicker peak but fewer patients admitted to hospital, less clinically severe illness, and a lower case-fatality ratio compared with the preceding three waves. Omicron marked a change in the SARSCoV-2 epidemic curve, clinical profile, and deaths in South Africa. Extrapolations to other populations should factor in differing vaccination and previous infection levels. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.

Older age, male sex, and non-white race have been reported to be risk factors for COVID-19 mortality. Few studies have explored how these intersecting factors contribute to COVID-19 outcomes. This study aimed to compare demographic characteristics and trends in SARS-CoV-2 admissions and the health care they received. Hospital admission data were collected through DATCOV, an active national COVID-19 surveillance programme. Descriptive analysis was used to compare admissions and deaths by age, sex, race, and health sector as a proxy for socio-economic status. COVID-19 mortality and healthcare utilisation were compared by race using random effect multivariable logistic regression models. On multivariable analysis, black African patients (adjusted OR [aOR] 1.3, 95% confidence interval [CI] 1.2, 1.3), coloured patients (aOR 1.2, 95% CI 1.1, 1.3), and patients of Indian descent (aOR 1.2, 95% CI 1.2, 1.3) had increased risk of in-hospital COVID-19 mortality compared to white patients; and admission in the public health sector (aOR 1.5, 95% CI 1.5, 1.6) was associated with increased risk of mortality compared to those in the private sector. There were higher percentages of COVID-19 hospitalised individuals treated in ICU, ventilated, and treated with supplemental oxygen in the private compared to the public sector. There were increased odds of non-white patients being treated in ICU or ventilated in the private sector, but decreased odds of black African patients being treated in ICU (aOR 0.5; 95% CI 0.4, 0.5) or ventilated (aOR 0.5; 95% CI 0.4, 0.6) compared to white patients in the public sector. These findings demonstrate the importance of collecting and analysing data on race and socio-economic status to ensure that disease control measures address the most vulnerable populations affected by COVID-19. Significance: These findings demonstrate the importance of collecting data on socio-economic status and race alongside age and sex, to identify the populations most vulnerable to COVID-19. This study allows a better understanding of the pre-existing inequalities that predispose some groups to poor disease outcomes and yet more limited access to health interventions. Interventions adapted for the most vulnerable populations are likely to be more effective. The national government must provide efficient and inclusive non-discriminatory health services, and urgently improve access to ICU, ventilation and oxygen in the public sector. Transformation of the healthcare system is long overdue, including narrowing the gap in resources between the private and public sectors.

Background: The B.1.1.529 (Omicron BA.1) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in mid-November 2021 in Southern Africa and subsequently resulted in a global resurgence of coronavirus disease 2019 (Covid-19). One-year later, sub-lineages of Omicron dominate globally as the cause of Covid-19. We undertook a population-based sero-survey to investigate the force of SARS-CoV-2 infections during the BA.1 dominant wave and its contribution to ongoing evolution of infection-induced immunity, and the subsequent trajectory of severe and fatal Covid-19 in Gauteng (South Africa). Methods: We conducted an epidemiologic survey to determine the sero-prevalence of SARS-CoV-2 anti-nucleocapsid (anti-N) and anti-spike (anti-S) protein IgG from March 1 to April 11, 2022, after the BA.1-dominant wave had subsided, and prior to another wave dominated by the BA.4 and BA.5 (BA.4/BA.5) sub-lineages. Population-based sampling included households in an earlier survey from October 22 to December 9, 2021 preceding the BA.1 dominant wave. Dried-blood-spot samples were quantitatively tested for anti-N and anti-S IgG. Epidemiologic trends in Gauteng for cases, hospitalizations, recorded deaths, and excess deaths were evaluated from the inception of the pandemic to the onset of the BA.1 dominant wave (pre-BA.1), during the BA.1 dominant wave (October 23, 2021 to March 21, 2022), and the subsequent eight-month period until November 17, 2022 when multiple sub-lineages of Omicron circulated. Results: The 7510 participants included 2420 with paired samples from the earlier survey. Despite only 26.7% (1995/7470) of individuals having received a Covid-19 vaccine, the overall sero-prevalence of either anti-N or anti-S IgG at the end of the BA.1 wave was 90.9% (95% confidence interval [CI], 90.2 to 91.5), including 89.5% in Covid-19 unvaccinated individuals. The rate of infection during the BA.1 dominant wave was 64% (95%CI, 61.8 to 65.9), in individuals with paired samples. Of all cumulative recorded Covid-19 hospitalisations and deaths, 13.8% and 5.8% occurred during the BA.1-dominant wave over a five-month period, whilst the subsequent eight-month Omicron sub-lineage era contributed a further 7.2% and 3.0%, respectively. The SARS-CoV-2 infection fatality risk 16.5-22.3 times lower in the BA.1-dominant compared with pre-BA.1 waves for recorded deaths (0.02% vs. 0.33%, a factor of 16.5) and estimated excess mortality (0.03% vs. 0.67%, a factor of 22.3). Conclusions: South Africa experienced a higher rate of serological inferred infections in the BA.1-dominant wave, resulting in an increase in infection-induced immunity from 73% (pre-BA.1) to 90% (post-BA.1 wave). Although there are ongoing cases of Covid-19 infections, hospitalization and death, there has not been any meaningful resurgence of Covid-19 since the BA.1- dominant wave despite only modest (37.8%) coverage by at least a single dose of Covid-19 vaccine in Gauteng. Funding Information: Funded by the Bill and Melinda Gates Foundation. Declaration of Interests: Dr. Madhi reports grants from the Bill & Melinda Gates Foundation during the conduct of the study, grants and personal fees from the Bill & Melinda Gates Foundation, grants from the South African Medical Research Council, grants from Novavax, grants from Pfizer, grants from Minervax, and grants from the European & Developing Countries Clinical Trials Partnership, outside the submitted work. Dr. Kwatra, Dr. Dhar, Mr. Mukendi, Dr Alane Izu and Dr. Mutevedzi report grants from the Bill & Melinda Gates Foundation during the conduct of the study. Mr. Welch shareholdings in Adcock Ingram Holdings Ltd, Aspen Pharmacare Holdings Ltd, Dischem Pharmacies Ltd, Discovery Ltd, and Netcare Ltd, outside the submitted work. Dr. Myers, Dr. Jassat, and Dr. Blumberg have nothing to disclose. Ethics Considerations: The Human Research Ethics Committee at the University of the Witwatersrand granted a waiver for ethics approval of the survey, which was being done as part of public health surveillance by the Gauteng Department of Health. All participants were, however, required to provide written informed consent; and individuals within a household were free to decline participation.

ABSTRACT Background Clinical severity of patients hospitalised with SARS-CoV-2 infection during the Omicron (fourth) wave was assessed and compared to trends in the D614G (first), Beta (second), and Delta (third) waves in South Africa. Methods Weekly incidence of 30 laboratory-confirmed SARS-CoV-2 cases/100,000 population defined the start and end of each wave. Hospital admission data were collected through an active national COVID-19-specific surveillance programme. Disease severity was compared across waves by post-imputation random effect multivariable logistic regression models. Severe disease was defined as one or more of acute respiratory distress, supplemental oxygen, mechanical ventilation, intensive-care admission or death. Results 335,219 laboratory-confirmed SARS-CoV-2 admissions were analysed, constituting 10.4% of 3,216,179 cases recorded during the 4 waves. In the Omicron wave, 8.3% of cases were admitted to hospital (52,038/629,617) compared to 12.9% (71,411/553,530) in the D614G, 12.6% (91,843/726,772) in the Beta and 10.0% (131,083/1,306,260) in the Delta waves (p<0.001). During the Omicron wave, 33.6% of admissions experienced severe disease compared to 52.3%, 63.4% and 63.0% in the D614G, Beta and Delta waves (p<0.001). The in-hospital case fatality ratio during the Omicron wave was 10.7%, compared to 21.5%, 28.8% and 26.4% in the D614G, Beta and Delta waves (p<0.001). Compared to the Omicron wave, patients had more severe clinical presentations in the D614G (adjusted odds ratio [aOR] 2.07; 95% confidence interval [CI] 2.01-2.13), Beta (aOR 3.59; CI: 3.49-3.70) and Delta (aOR 3.47: CI: 3.38-3.57) waves. Conclusion The trend of increasing cases and admissions across South Africa’s first three waves shifted in Omicron fourth wave, with a higher and quicker peak but fewer admitted patients, who experienced less clinically severe illness and had a lower case-fatality ratio. Omicron marked a change in the SARS-CoV-2 epidemic curve, clinical profile and deaths in South Africa. Extrapolations to other populations should factor in differing vaccination and prior infection levels.

Abstract 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.

Background: Covid-19 vaccine rollout is lagging in Africa, where there has been a high force of SARS-CoV-2 infection. We evaluated the effect of SARS-CoV-2 infection prior to vaccination with the ChAdOx-nCoV19 (AZD1222) vaccine on antibody responses through to 180 days (D180).Methods: We undertook a post-hoc immunogenicity analysis after two doses of AZD1222 in a randomised, placebo-controlled phase Ib/2a study undertaken in South Africa. Recipients were stratified by serological assessment of SARS-CoV-2 infection prior to 1st dose into baseline seropositive or baseline seronegative groups. Binding immunoglobulin G (IgG) to spike (anti-S) and receptor binding domain (anti-RBD) were measured prior to first dose (D0), 2nd dose (D28), and at D42 and D180. Neutralizing antibody (NAb) against SARS-CoV-2 variants D614G, Beta, Delta, Gamma, A.VOI.V2, Omicron BA1 and BA.4 variants and SARS-CoV-1, were measured by pseudovirus assay (D28, D42 and D180). Antibody dependent cellular cytoxicity against D614G and Delta was measured at D28 and D42.Findings: Anti-S (and anti-RBD) IgG geometric mean concentrations (GMCs) were higher throughout in the baseline seropositive than seronegative group, persisting to D180 (GMCs: 517.8 vs 82.1 BAU/ml). Similarly, the percentage who had anti-S IgG ≥264BAU/ml (i.e. putative 80% risk reduction threshold [PRRT] against wild type (WT)/Alpha symptomatic Covid-19) was 76.6% vs 13.8% at D180. Also D614G NAb geometric mean titres (GMT) were higher in the baseline seropositive than seronegative group, as was the percentage with titres ≥185 (80% PRRT against WT/Alpha Covid-19) even at D180 (92.0% vs 18.2%). Similar findings were observed for Beta, A.VOI.V2 and Gamma at D28, D42 and D180. NAb GMTS against BA.1 and BA.4 were higher in the baseline seropositive than seronegative group at D28 (499 vs. 14; and 436 vs. 25.0) and D42 (535 vs. 16; and 429 vs. 25.0), as was the percentage with NAb titres ≥185 for BA.1 and BA.4 at D28 (84.0% and 87.5% vs. 0%) and D42 (84.0% and 87.5% vs. 0%). Interpretation: A single dose of AZD1222 in the general African population, where Covid-19 vaccine coverage is low and SARS-CoV-2 seropositivity is 90%, could enhance the magnitude and quality of antibody responses to SARS-CoV-2.Trial Registration Details: The COV005 study is registered with ClinicalTrials.gov, NCT04444674, and the Pan African Clinical Trials Registry, ACTR202006922165132.Funding Information: Grants from The Bill & Melinda Gates Foundation and the South African Medical Research Council provided funding for the study and the funders of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. Vaccines used in the study were donated by the University of Oxford. Declaration of Interests: Oxford University has entered into a partnership with AstraZeneca for further development of ChAdOx1 nCoV-19 (AZD1222). SCG is cofounder of Vaccitech, a collaborator in the early development of this vaccine candidate, and is named as an inventor on a patent covering use of ChAdOx1-vectored vaccines (PCT/GB2012/000467) and a patent application covering this SARS-CoV-2 vaccine (GB2003670.3). TL is named as an inventor on a patent application covering ChAdOx1 nCoV-19 and was a consultant to Vaccitech. All other authors declare no competing interests.Ethical Approval Statement: The study was approved by the South African Health and Pharmaceutical Products Regulatory Authority (SAHPRA) and the Human Ethics Research Committees of the various sites. Signed informed consent was obtained from all study participants. The South African Health Products Regulatory Authority (SAHPRA, reference 20200407), the Ethics committees at the University of the Witwatersrand, (HREC:200501), University of Stellenbosch (Ref: M20/06/009_Covid-19), University of Cape Town (Ref: 350/2020) and Oxford Tropical Research Ethics Committee (OxTREC ref 35-20) at the University of Oxford provided Ethics approval.

ABSTRACTBackgroundThe B.1.1.529 (Omicron BA.1) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global resurgence of coronavirus disease 2019 (Covid-19). The contribution of BA.1 infection to population immunity and its effect on subsequent resurgence of B.1.1.529 sub-lineages warrant investigation.MethodsWe conducted an epidemiologic survey to determine the sero-prevalence of SARS-CoV-2 IgG from March 1 to April 11, 2022, after the BA.1-dominant wave had subsided in Gauteng (South Africa), and prior to a resurgence of Covid-19 dominated by the BA.4 and BA.5 (BA.4/BA.5) sub-lineages. Population-based sampling included households in an earlier survey from October 22 to December 9, 2021 preceding the BA.1 dominant wave. Dried-blood-spot samples were quantitatively tested for IgG against SARS-CoV-2 spike protein and nucleocapsid protein. Epidemiologic trends in Gauteng for cases, hospitalizations, recorded deaths, and excess deaths were evaluated from the inception of the pandemic to the onset of the BA.1 dominant wave (pre-BA.1), during the BA.1 dominant wave, and for the BA.4/BA.5 dominant wave through June 6, 2022.ResultsThe 7510 participants included 2420 with paired samples from the earlier survey. Despite only 26.7% (1995/7470) of individuals having received a Covid-19 vaccine, the overall sero-prevalence was 90.9% (95% confidence interval [CI], 90.2 to 91.5), including 89.5% in Covid-19 unvaccinated individuals. Sixty-four percent (95%CI, 61.8-65.9) of individuals with paired samples had serological evidence of SARS-CoV-2 infection during the BA.1 dominant wave. Of all cumulative recorded hospitalisations and deaths, 14.1% and 5.9% were contributed by the BA.1 dominant wave, and 5.1% and 1.6% by the BA.4/BA.5 dominant wave. The SARS-CoV-2 infection fatality risk was lower in the BA.1 compared with pre-BA.1 waves for recorded deaths (0.02% vs. 0.33%) and Covid-19 attributable deaths based on excess mortality estimates (0.03% vs. 0.67%).ConclusionsGauteng province experienced high levels of infections in the BA.1 -dominant wave against a backdrop of high (73%) sero-prevalence. Covid-19 hospitalizations and deaths were further decoupled from infections during BA.4/BA.5 dominant wave than that observed during the BA.1 dominant wave.(Funded by the Bill and Melinda Gates Foundation.)

Omicron lineages BA.4 and BA.5 drove a fifth wave of COVID-19 cases in South Africa. Here, we use the presence/absence of the S-gene target as a proxy for SARS-CoV-2 variant/lineage for infections diagnosed using the TaqPath PCR assay between 1 October 2021 and 26 April 2022. We link national COVID-19 individual-level data including case, laboratory test and hospitalisation data. We assess 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 (aOR 1.24, 95% CI 0.98-1.55) and severe outcome (aOR 0.72, 95% CI 0.41-1.26) compared to BA.1-infected individuals. Newly emerged Omicron lineages BA.4/BA.5 showed similar severity to the BA.1 lineage and continued to show reduced clinical severity compared to the Delta variant.

Background Assessment of disease severity associated with a novel pathogen or variant provides crucial information needed by public health agencies and governments to develop appropriate responses. The SARS-CoV-2 omicron variant of concern (VOC) spread rapidly through populations worldwide before robust epidemiological and laboratory data were available to investigate its relative severity. Here we develop a set of methods that make use of non-linked, aggregate data to promptly estimate the severity of a novel variant, compare its characteristics with those of previous VOCs, and inform data-driven public health responses.Methods Using daily population-level surveillance data from the National Institute for Communicable Diseases in South Africa (March 2, 2020, to Jan 28, 2022), we determined lag intervals most consistent with time from case ascertainment to hospital admission and within-hospital death through optimisation of the distance correlation coefficient in a time series analysis. We then used these intervals to estimate and compare age-stratified case -hospitalisation and case-fatality ratios across the four epidemic waves that South Africa has faced, each dominated by a different variant.Findings A total of 3 569 621 cases, 494 186 hospitalisations, and 99 954 deaths attributable to COVID-19 were included in the analyses. We found that lag intervals and disease severity were dependent on age and variant. At an aggregate level, fluctuations in cases were generally followed by a similar trend in hospitalisations within 7 days and deaths within 15 days. We noted a marked reduction in disease severity throughout the omicron period relative to previous waves (age-standardised case-fatality ratios were consistently reduced by >50%), most substantial for age strata with individuals 50 years or older.Interpretation This population-level time series analysis method, which calculates an optimal lag interval that is then used to inform the numerator of severity metrics including the case-hospitalisation and case-fatality ratio, provides useful and timely estimates of the relative effects of novel SARS-CoV-2 VOCs, especially for application in settings where resources are limited.

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.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) first reported in Wuhan, China in December 2019 is a global pandemic that is threatening the health and wellbeing of people worldwide. To date there have been more than 274 million reported cases and 5.3 million deaths. The Omicron variant first documented in the City of Tshwane, Gauteng Province, South Africa on 9 November 2021 led to exponential increases in cases and a sharp rise in hospital admissions. The clinical profile of patients admitted at a large hospital in Tshwane is compared with previous waves. METHODS: 466 hospital COVID-19 admissions since 14 November 2021 were compared to 3962 admissions since 4 May 2020, prior to the Omicron outbreak. Ninety-eight patient records at peak bed occupancy during the outbreak were reviewed for primary indication for admission, clinical severity, oxygen supplementation level, vaccination and prior COVID-19 infection. Provincial and city-wide daily cases and reported deaths, hospital admissions and excess deaths data were sourced from the National Institute for Communicable Diseases, the National Department of Health and the South African Medical Research Council. RESULTS: For the Omicron and previous waves, deaths and ICU admissions were 4.5% vs 21.3% (p < 0.00001), and 1% vs 4.3% (p < 0.00001) respectively; length of stay was 4.0 days vs 8.8 days; and mean age was 39 years vs 49,8 years. Admissions in the Omicron wave peaked and declined rapidly with peak bed occupancy at 51% of the highest previous peak during the Delta wave. Sixty two (63%) patients in COVID-19 wards had incidental COVID-19 following a positive SARS-CoV-2 PCR test . Only one third (36) had COVID-19 pneumonia, of which 72% had mild to moderate disease. The remaining 28% required high care or ICU admission. Fewer than half (45%) of patients in COVID-19 wards required oxygen supplementation compared to 99.5% in the first wave. The death rate in the face of an exponential increase in cases during the Omicron wave at the city and provincial levels shows a decoupling of cases and deaths compared to previous waves, corroborating the clinical findings of decreased severity of disease seen in patients admitted to the Steve Biko Academic Hospital. CONCLUSION: There was decreased severity of COVID-19 disease in the Omicron-driven fourth wave in the City of Tshwane, its first global epicentre. (C) 2021 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.