Estimated SARS-CoV-2 infection rate and fatality risk in Gauteng Province, South Africa: a population-based seroepidemiological survey

Abstract

Background: Limitations in laboratory testing capacity undermine the ability to quantify the overall burden of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection.

Methods: We undertook a population-based serosurvey for SARS-CoV-2 infection in 26 subdistricts, Gauteng Province (population 15.9 million), South Africa, to estimate SARS-CoV-2 infection, infection fatality rate (IFR) triangulating seroprevalence, recorded COVID-19 deaths and excess-mortality data. We employed three-stage random household sampling with a selection probability proportional to the subdistrict size, stratifying the subdistrict census-sampling frame by housing type and then selecting households from selected clusters. The survey started on 4 November 2020, 8 weeks after the end of the first wave (SARS-CoV-2 nucleic acid amplification test positivity had declined to <10% for the first wave) and coincided with the peak of the second wave. The last sampling was performed on 22 January 2021, which was 9 weeks after the SARS-CoV-2 resurgence. Serum SARS-CoV-2 receptor-binding domain (RBD) immunoglobulin-G (IgG) was measured using a quantitative assay on the Luminex platform.

Results: From 6332 individuals in 3453 households, the overall RBD IgG seroprevalence was 19.1% [95% confidence interval (CI): 18.1-20.1%] and similar in children and adults. The seroprevalence varied from 5.5% to 43.2% across subdistricts. Conservatively, there were 2 897 120 (95% CI: 2 743 907-3 056 866) SARS-CoV-2 infections, yielding an infection rate of 19 090 per 100 000 until 9 January 2021, when 330 336 COVID-19 cases were recorded. The estimated IFR using recorded COVID-19 deaths (n = 8198) was 0.28% (95% CI: 0.27-0.30) and 0.67% (95% CI: 0.64-0.71) assuming 90% of modelled natural excess deaths were due to COVID-19 (n = 21 582). Notably, 53.8% (65/122) of individuals with previous self-reported confirmed SARS-CoV-2 infection were RBD IgG seronegative.

Conclusions: The calculated number of SARS-CoV-2 infections was 7.8-fold greater than the recorded COVID-19 cases. The calculated SARS-CoV-2 IFR varied 2.39-fold when calculated using reported COVID-19 deaths (0.28%) compared with excess-mortality-derived COVID-19-attributable deaths (0.67%). Waning RBD IgG may have inadvertently underestimated the number of SARS-CoV-2 infections and conversely overestimated the mortality risk. Epidemic preparedness and response planning for future COVID-19 waves will need to consider the true magnitude of infections, paying close attention to excess-mortality trends rather than absolute reported COVID-19 deaths.

Keywords: COVID-19; SARS-CoV-2; coronavirus; infection-mortality risk; seroprevalence; serosurvey.

Figure 1

Flow of households and participants included in the seroprevalence survey. We illustrate the flow of participants included in survey analyses from approaching the households to negotiate participation through to specimen collection and processing. Absolute numbers are presented. The final analysis included 5584 individuals in 26 subdistricts. *Inadequate specimen refers to dried blood spots with insufficient filter-paper saturation and hence low specimen yield for serology testing.

Figure 2

SARS-CoV-2 seroprevalence by subdistrict. SARS-COV-2 seroprevalence is presented by subdistrict showing heterogeneity across the districts and subdistricts. Seroprevalence is presented in relation to the population and geographic size of each region. City of Johannesburg, the smallest in geographic size but with the largest population size, has the highest seroprevalence.

Figure 3

Subdistrict reported COVID-19 cases (through to 9 January 2021) compared with calculated SARS-CoV-2 infections. The adjusted number of infections was calculated by applying the seroprevalence to the population size at provincial, district and subdistrict levels. Across all subdistricts except for two districts, the documented COVID-19 cases significantly underestimated the population-level SARS-CoV-2 infections.