Reconciling model predictions with low reported cases of COVID-19 in Sub-Saharan Africa: insights from Madagascar - PubMed (original) (raw)
Reconciling model predictions with low reported cases of COVID-19 in Sub-Saharan Africa: insights from Madagascar
Michelle V Evans et al. Glob Health Action. 2020.
Abstract
COVID-19 has wreaked havoc globally with particular concerns for sub-Saharan Africa (SSA), where models suggest that the majority of the population will become infected. Conventional wisdom suggests that the continent will bear a higher burden of COVID-19 for the same reasons it suffers from other infectious diseases: ecology, socio-economic conditions, lack of water and sanitation infrastructure, and weak health systems. However, so far SSA has reported lower incidence and fatalities compared to the predictions of standard models and the experience of other regions of the world. There are three leading explanations, each with different implications for the final epidemic burden: (1) low case detection, (2) differences in epidemiology (e.g. low R 0 ), and (3) policy interventions. The low number of cases have led some SSA governments to relaxing these policy interventions. Will this result in a resurgence of cases? To understand how to interpret the lower-than-expected COVID-19 case data in Madagascar, we use a simple age-structured model to explore each of these explanations and predict the epidemic impact associated with them. We show that the incidence of COVID-19 cases as of July 2020 can be explained by any combination of the late introduction of first imported cases, early implementation of non-pharmaceutical interventions (NPIs), and low case detection rates. We then re-evaluate these findings in the context of the COVID-19 epidemic in Madagascar through August 2020. This analysis reinforces that Madagascar, along with other countries in SSA, remains at risk of a growing health crisis. If NPIs remain enforced, up to 50,000 lives may be saved. Even with NPIs, without vaccines and new therapies, COVID-19 could infect up to 30% of the population, making it the largest public health threat in Madagascar for the coming year, hence the importance of clinical trials and continually improving access to healthcare.
Keywords: COVID-19; Madagascar; age-structured contacts; infectious disease modelling; non-pharmaceutical interventions; outbreak response.
Conflict of interest statement
No potential conflict of interest was reported by the authors.
Figures
Figure 1.
The lower-than-expected daily incidence can be explained by detection rates of 0.1–1% or NPI efficiencies of 30% alone. Predicted epidemic trajectories for the unmitigated scenario (a), range of detection rates (b), and range of NPI efficiencies (c). Results from 100 simulations are shown in A with the black line representing the median number of cases. Shaded regions represent the 95% confidence intervals around the median in panels B and C. All simulations began on the date of the first positive imported case in Madagascar, 20 March 2020. The y-axis is plotted on the log10-scale.
Figure 2.
Low reported cases can be explained by different combinations of NPI effectiveness and detection rates. (a) The predicted number of daily cases (7 day average) that would be detected based on models of the epidemic at different combinations of NPI effectiveness and case detections rates. The dark contour line corresponds to the parameter space where the median number of predicted cases from 25 simulations equals the daily reported cases (7 day average) on June 22 (71.71 cases). High NPI effectiveness would thus require relatively high detection rates to explain the data based on these standard models. Similarly, if NPI were not effective, then the data could be explained with low detection rates. (b) Total cases after 1 year (approximating the final epidemic size) and (c) total deaths that correspond to the combination of NPI effectiveness and detection rates that explain daily cases in A. Shaded diamonds correspond to specific scenarios explored in panel D, illustrating the dynamics of detected infections, all infections, and cumulative deaths over the first year of the epidemic.
Figure 3.
The simple modeled scenarios can accurately explain early, but not later, epidemic dynamics in Madagascar. Time series of predictions from the three scenarios explored in Figure 2D are plotted here (median and 95% CI), with line-shade corresponding to the scenario. Reported case data from the Madagascar Ministry of Health are plotted in the red points.
References
- El-Sadr WM, Justman J.. Africa in the path of Covid-19. N Engl J Med. 2020;383:e11. -PubMed
- Massinga Loembé M, Tshangela A, Salyer SJ, et al. COVID-19 in Africa: the spread and response. Nature Med. 2020;26:999–9. -PubMed
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