High variation expected in the pace and burden of SARS-CoV-2 outbreaks across sub-Saharan Africa

Abstract

A surprising feature of the SARS-CoV-2 pandemic to date is the low burdens reported in sub-Saharan Africa (SSA) countries relative to other global regions. Potential explanations (e.g., warmer environments¹, younger populations^2-4) have yet to be framed within a comprehensive analysis accounting for factors that may offset the effects of climate and demography. Here, we synthesize factors hypothesized to shape the pace of this pandemic and its burden as it moves across SSA, encompassing demographic, comorbidity, climatic, healthcare and intervention capacity, and human mobility dimensions of risk. We find large scale diversity in probable drivers, such that outcomes are likely to be highly variable among SSA countries. While simulation shows that extensive climatic variation among SSA population centers has little effect on early outbreak trajectories, heterogeneity in connectivity is likely to play a large role in shaping the pace of viral spread. The prolonged, asynchronous outbreaks expected in weakly connected settings may result in extended stress to health systems. In addition, the observed variability in comorbidities and access to care will likely modulate the severity of infection: We show that even small shifts in the infection fatality ratio towards younger ages, which are likely in high risk settings, can eliminate the protective effect of younger populations. We highlight countries with elevated risk of 'slow pace', high burden outbreaks. Empirical data on the spatial extent of outbreaks within SSA countries, their patterns in severity over age, and the relationship between epidemic pace and health system disruptions are urgently needed to guide efforts to mitigate the high burden scenarios explored here.

Figure 1 ∣. Hypothesized modulators of relative SARS-CoV-2 epidemic risk in sub-Saharan Africa

Factors hypothesized to increase (red) or decrease (blue) mortality burden or epidemic pace within sub-Saharan Africa, relative to global averages, are grouped in six categories or dimensions of risk (A-F). In this framework, epidemic pace is determined by person to person transmissibility (which can be defined as the time-varying effective reproductive number, R_t) and introduction and geographic spread of the virus via human mobility. SARS-CoV-2 mortality (determined by the infection fatality ratio, IFR) is modulated by demography, comorbidities (e.g., non-communicable diseases (NCDs)), and access to care. Overall burden is a function of direct burden and indirect effects due to, for example, disruptions in health services such as vaccination and infectious disease control. Table S2 contains details and the references used as a basis to draw the hypothesized modulating pathways.

Figure 2 ∣. Variation among sub-Saharan African countries in select determinants of SARS-CoV-2 risk

A-D: At right, SSA countries are ranked from least to greatest for each indicator; bar color shows population age structure (% of the population above age 50). Solid horizontal lines show the global mean value; dotted lines show the mean among SSA countries. At left, boxplots show median and interquartile range, grouped by geographic region, per WHO: sub-Saharan Africa (SSA); Americas Region (AMR); Eastern Mediterranean Region (EMR); Europe Region (EUR); Southeast Asia Region (SEA); Western Pacific Region (WPR). E-F: Dot size shows mean household (HH) size for HHs with individuals over age 50; dashed lines show median value among SSA countries; quadrants of greatest risk are outlined in red (e.g., fewer physicians and greater age standardized Chronic Obstructive Pulmonary Disease (COPD) mortality). See Table S3, Figure S3, and the [SSA-SARS-CoV-2-tool] for full description and visualization of all variables.

Figure 3 ∣. Variation in expected burden for SARS-CoV-2 outbreaks in sub-Saharan Africa

A: Expected mortality in a scenario where cumulative infection reaches 20% across age groups and the infection fatality ratio (IFR) curve is fit to existing age-stratified IFR estimates (see methods, Table S4). B: National level variation in comorbidity and access to care variables, for e.g., diabetes prevalence among adults and the number of hospital beds per 100,000 population for sub-Saharan African countries. C: The range in mortality per 100,000 population expected in scenarios where cumulative infection rate is 20% and IFR per age is the baseline (black) or shifted 2, 5, or 10 years younger (gray). Inset, the IFR by age curves for each scenario. D-E: Select national level indicators; estimates of reduced access to care (e.g., fewer hospitals) or increased comorbidity burden (e.g., higher prevalence of raised blood pressure) shown with darker red for higher risk quartiles (see Figure S4 for all indicators). Countries missing data for an indicator (NA) are shown in gray. For comparison between countries, estimates are age-standardized where applicable (see Table S3 for details). See the [SSA-SARS-CoV-2-tool] for high resolution maps for each variable and scenario.

Figure 4 ∣. Variation in connectivity and climate in sub-Saharan Africa and expected effects on SARS-CoV-2

A: International travelers to sub-Saharan Africa (SSA) from January to April 2020, as inferred from the number of passenger seats on arriving aircraft. B: For the four countries with the most arrivals, the proportion of arrivals by month coming from countries with 0, 1-100, 101-1000, and 1000+ reported SARS-CoV-2 infections at the time of travel (see Table S5 for all others). C: Connectivity within SSA countries as inferred from average population weighted mean travel time to the nearest urban area greater than 50,000 population. D: Mean travel time at the national level and variation in the fraction of the population expected to be infected (I/N) in the first year from stochastic simulations (see methods). E: Climate variation across SSA as shown by seasonal range in specific humidity, q (g/kg) (max average q - min average q). Circles show peak proportion infected. F: The effect of local seasonality in SSA cities on outbreaks (I/N over time) in susceptible populations beginning in March 2020 (see methods).

Figure 5 ∣. Expected pace versus expected burden at the national level in SARS-CoV-2 outbreaks in sub-Saharan Africa

Countries are colored by with respect to indicators of their expected epidemic pace (using as an example subnational connectivity in terms of travel time to nearest city) and potential burden (using as an example the proportion of the population over age 50). A: In pink, countries with less connectivity (i.e., less synchronous outbreaks) relative to the median among SSA countries; in blue, countries with more connectivity; darker colors show countries with older populations (i.e., a greater proportion in higher risk age groups). B: Dotted lines show the median; in the upper right, in dark pink, countries are highlighted due to their increased potential risk for an outbreak to be prolonged (see metapopulation model methods) and high burden (see burden estimation methods).