COVID-19 prediction in South Africa: Estimating the unascertained cases - the hidden part of the epidemiological iceberg


  • Xuelin Gu Department of Biostatistics, University of Michigan, Ann Arbor, USA
  • Bhramar Mukherjee Department of Biostatistics, Department of Epidemiology, University of Michigan, Ann Arbor, USA
  • Sonali Das Department of Business Management, University of Pretoria, Pretoria, South Africa
  • Jyotishka Datta Department of Statistics, Virginia Polytechnic Institute and State University, USA


COVID-19; South Africa; forecasting; unascertained cases; underreporting factors; infection fatality ratio


Understanding the impact of non-pharmaceutical interventions as well as accounting for the unascertained cases remain critical challenges for epidemiological models for COVID-19 spread. In this paper, we propose a new epidemiological model (eSEIRD) that extends the widely used extended Susceptible-Infected-Removed model (eSIR) and SAPHIRE models. We fit these models to the daily ascertained infected and removed cases from March 15, 2020 to December 31, 2020 in South Africa, the ‘worst-hit’ country in theWHOAfrican region. Using the eSEIRD model, the COVID-19 transmission dynamics in South Africa was characterized by the estimated basic reproduction number (R0) starting at 3.22 (95%CrI: [3.19, 3.23]) then dropping below 2 (95%CrI: [1.36, 1.39]) following a mandatory lockdown implementation and subsequently increasing to 3.27 (95%CrI: [3.27, 3.27]) by the end of 2020. The estimated trajectory of R0 suggests the effect of early interventions and the subsequent relaxation and emergence of a new coronavirus variant. The estimated ascertainment rate was found to vary from 1.65% to 9.17% across models and time periods. The overall infection fatality ratio (IFR) was estimated as 0.06% (95%CrI: [0.04%, 0.22%]) accounting for unascertained cases and deaths while the reported case fatality ratio was 2.88% (95% CrI: [2.45%, 6.01%]). The models predict that from December 31, 2020, to April 1, 2021, the predicted cumulative number of infected would reach roughly 70% of total population in South Africa. Besides providing insights on the COVID-19 dynamics in South Africa, we develop powerful forecasting tools that enable estimation of ascertainment rates and IFR while quantifying the effect of intervention measures.

Journal of Statistical Research 2021, Vol. 55, No. 1, pp. 267-291





How to Cite

Gu, X. ., Mukherjee, B. ., Das, S. ., & Datta, J. (2021). COVID-19 prediction in South Africa: Estimating the unascertained cases - the hidden part of the epidemiological iceberg. Journal of Statistical Research, 55(1), 267–291. Retrieved from