Response strategies for COVID-19 epidemics in African settings: a mathematical modelling study

Abstract

Background: The health impact of COVID-19 may differ in African settings as compared to countries in Europe or China due to demographic, epidemiological, environmental and socio-economic factors. We evaluated strategies to reduce SARS-CoV-2 burden in African countries, so as to support decisions that balance minimising mortality, protecting health services and safeguarding livelihoods.

Methods: We used a Susceptible-Exposed-Infectious-Recovered mathematical model, stratified by age, to predict the evolution of COVID-19 epidemics in three countries representing a range of age distributions in Africa (from oldest to youngest average age: Mauritius, Nigeria and Niger), under various effectiveness assumptions for combinations of different non-pharmaceutical interventions: self-isolation of symptomatic people, physical distancing and 'shielding' (physical isolation) of the high-risk population. We adapted model parameters to better represent uncertainty about what might be expected in African populations, in particular by shifting the distribution of severity risk towards younger ages and increasing the case-fatality ratio. We also present sensitivity analyses for key model parameters subject to uncertainty.

Results: We predicted median symptomatic attack rates over the first 12 months of 23% (Niger) to 42% (Mauritius), peaking at 2-4 months, if epidemics were unmitigated. Self-isolation while symptomatic had a maximum impact of about 30% on reducing severe cases, while the impact of physical distancing varied widely depending on percent contact reduction and R₀. The effect of shielding high-risk people, e.g. by rehousing them in physical isolation, was sensitive mainly to residual contact with low-risk people, and to a lesser extent to contact among shielded individuals. Mitigation strategies incorporating self-isolation of symptomatic individuals, moderate physical distancing and high uptake of shielding reduced predicted peak bed demand and mortality by around 50%. Lockdowns delayed epidemics by about 3 months. Estimates were sensitive to differences in age-specific social mixing patterns, as published in the literature, and assumptions on transmissibility, infectiousness of asymptomatic cases and risk of severe disease or death by age.

Conclusions: In African settings, as elsewhere, current evidence suggests large COVID-19 epidemics are expected. However, African countries have fewer means to suppress transmission and manage cases. We found that self-isolation of symptomatic persons and general physical distancing are unlikely to avert very large epidemics, unless distancing takes the form of stringent lockdown measures. However, both interventions help to mitigate the epidemic. Shielding of high-risk individuals can reduce health service demand and, even more markedly, mortality if it features high uptake and low contact of shielded and unshielded people, with no increase in contact among shielded people. Strategies combining self-isolation, moderate physical distancing and shielding could achieve substantial reductions in mortality in African countries. Temporary lockdowns, where socioeconomically acceptable, can help gain crucial time for planning and expanding health service capacity.

Keywords: Africa; COVID-19; Control; Coronavirus; Low-income; Mathematical model; Response; SARS-CoV-2.

Fig. 1

Projected incidence of symptomatic COVID-19 cases over time for simulations of an unmitigated epidemic, by country. The green line shows the run that was closest to the median total number of cases across all model runs using global R₀ estimates. The black line shows the run that was closest to the median total number of cases across all model runs using country-specific R_t estimates. Grey lines show individual stochastic model runs, where R₀ in each run was sampled from the respective distribution

Fig. 2

Estimated reduction in severe cases following a self-isolation of symptomatic individuals and b population-wide physical distancing, using synthetic contact matrices. Medians (circles), 75% (lighter shaded areas) and 50% (darker shaded area) quantiles for the percentage reduction in severe cases during the first 12 months of the epidemic for different levels of compliance, for each country, across all model runs in each scenario. Quantiles are calculated across all simulations representing different stochastic runs and using different R₀ values in each run, drawn from a distribution of global R₀ estimates or from a distribution of country-specific R_t estimates. Estimates for reductions where no point is available are interpolated

Fig. 3

Estimated reduction in severe cases when shielding high-risk individuals, by country, using synthetic contact matrices. Medians (dashed lines) and 75% quantiles (shaded areas) of the percentage reduction in severe cases during the first 12 months of the epidemic for different levels of reduction in contacts between shielded and unshielded people (x axis), different level of contacts among shielded people (facet rows), and for different percentages of people ≥ 60 years old shielded (see legend), for each country, across all model runs in each scenario

Fig. 4

Estimated daily number of deaths during the first 18 months of the epidemic, under different strategies. Black lines show results using country R_t estimates, while coloured lines show results using global R₀ estimates. Thick solid lines show the run which was closest to the median total number of deaths after 12 months across all model runs. Dashed lines are runs closest to the lower and upper 95% quantiles, while dotted lines are runs closest to the lower and upper 50% quantiles of total number of deaths, calculated over 600 model runs. Except for the unmitigated scenario, all scenarios assume 50% self-isolation during the symptomatic period of all symptomatic cases throughout the entire course of the epidemic. Other interventions start when daily incidence of symptomatic cases reaches 1 case per 10,000 people. Distancing strategies assume 20% or 50% reduction in all contacts outside of the household. Shielding strategies assume shielding of 80% of the population aged ≥ 60 years, irrespective of underlying comorbidities, with an 80% reduction in contacts between the shielded and unshielded population, and no change in contacts within the shielded population. Estimates for bed demand over time are given in Figure S2