COVID-19 PREDICTION IN SOUTH AFRICA: ESTIMATING THE UNASCERTAINED CASES- THE HIDDEN PART OF THE EPIDEMIOLOGICAL ICEBERG
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Abstract
Understanding the impact of non-pharmaceutical interventions as well as acscounting for the unascertained cases remain critical challenges for epidemiological models for understanding the transmission dynamics of COVID-19 spread. In this paper, we propose a new epidemiological model (eSEIRD) that extends the widely used epidemiological models such as extended Susceptible-Infected-Removed model (eSIR) and SAPHIRE (initially developed and used for analyzing data from Wuhan). We fit these models to the daily ascertained infected (and removed) cases from March 15, 2020 to Dec 31, 2020 in South Africa that reported the largest number of confirmed COVID-19 cases and deaths from the WHO African region. Using the eSEIRD model, the COVID-19 transmission dynamics in South Africa was characterized by the estimated basic reproduction number ( R 0 ) starting at 3.22 (95%CrI: [3.19, 3.23]) then dropping below 2 following a mandatory lockdown implementation and subsequently increasing to 3.27 (95%CrI: [3.27, 3.27]) by the end of 2020. The initial decrease of effective reproduction number followed by an increase suggest the effectiveness of early interventions and the combined effect of relaxing strict interventions and emergence of a new coronavirus variant in South Africa. The low 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 on COVID-19 spread.
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SciScore for 10.1101/2020.12.10.20247361: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and Limitations: The key methodological innovation for the proposed method is revealed by a quick comparison between the schematic diagrams for eSEIRD model (Fig. 3) and SAPHIRE model (Fig. 2(b)). Broadly speaking, eSEIRD incorporates π(t), the transmission rate modifier as well as splits the ‘removed’ compartment …
SciScore for 10.1101/2020.12.10.20247361: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and Limitations: The key methodological innovation for the proposed method is revealed by a quick comparison between the schematic diagrams for eSEIRD model (Fig. 3) and SAPHIRE model (Fig. 2(b)). Broadly speaking, eSEIRD incorporates π(t), the transmission rate modifier as well as splits the ‘removed’ compartment into ‘recovered’ and ‘deaths’ while accounting for separate rates for ascertained, unascertained and hospitalized cases. Despite the superior performance and robustness exhibited by the models examined here, there are some important limitations. First, the model assumptions were elicited from previous reports from other countries because of the lack of such information for South Africa, especially the fixed values for hyper-parameters. Though the estimation of parameters and prediction of infections seem to be robust to these assumptions to some extent, the inference and prediction would be much more convincing when based on accurate information specific to South Africa. Second, the ascertained rate was assumed to follow the same distribution in a long time period in the eSEIRD model although in reality it might be time-varying depending on the accumulating knowledge and deployment of clinical resources for COVID-19, given the spatial variation within South Africa regarding the population density and movement, as well as regarding location of COVID-19 hotspots and hospital resources. Further, the population density is highly heterogeneous in different regi...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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