Open AccessEstimating actual COVID-19 case numbers using cumulative death count-A method of measuring effectiveness of lockdown of non-essential activities: a South African case study
Author(s) -
Laura G. Cox,
Clarence S. Yah
Publication year - 2020
Publication title -
the pan african medical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.287
H-Index - 30
ISSN - 1937-8688
DOI - 10.11604/pamj.supp.2020.35.2.24612
Subject(s) - medicine , covid-19 , case fatality rate , transmission (telecommunications) , statistics , incubation period , lag time , demography , asymptomatic , mortality rate , population , disease , surgery , infectious disease (medical specialty) , environmental health , mathematics , incubation , biochemistry , chemistry , sociology , electrical engineering , engineering , biological system , biology
Estimating the number of SARS-CoV-2 infected individuals at any specific time point is always a challenge due to asymptomatic cases, the incubation period and testing delays. Here we use an empirical analysis of cumulative death count, transmission-to-death time lag, and infection fatality rate (IFR) to evaluate and estimate the actual cases at a specific time point as a strategy of tracking the spread of COVID-19. Methods This method mainly uses death count, as COVID-19 related deaths are arguably more reliably reported than infection case numbers. Using an IFR estimate of 0.66%, we back-calculate the number of cases that would result in the cumulative number of deaths at a given time point in South Africa between 27 February and 14 April. We added the mean incubation period (6.4 days) and the onset-to-death time lag (17.8 days) to identify the estimated time lag between transmission and death (25 days, rounded up). We use the statistical programming language R to analyze the data and produce plots. Results We estimate 28,182 cases as of 14 April, compared with 3,465 reported cases. Weekly growth rate of actual cases dropped immediately after lockdown implementation and has remained steady, measuring at 51.2% as of 14 April. The timing of drop in growth rate suggests that South Africa’s infection prevention strategy may have been effective at reducing viral transmission. Conclusion Estimating the actual number of cases at a specific time point can support evidence-based policies to reduce and prevent the spread of COVID-19. Non-reported, asymptomatic, hard to reach and, mild cases are possible sources of outbreaks that could emerge after lockdown. Therefore, close monitoring, optimized screening strategy and prompt response to COVID-19 could help in stopping the spread of the virus.