We explored the impact of different non-pharmaceutical control interventions and strategies (packages and sequences of interventions) that may effectively be implemented in African countries to mitigate COVID-19 epidemics. Short of an indefinite-duration lock-down, none of the interventions would likely avert very large epidemics that result in high mortality and extreme pressure on health services. While numerous initiatives are underway to rapidly scale up COVID-19 case management capacity, e.g. by increasing oxygen availability at district hospital level, the actual coverage and effectiveness of such treatment is difficult to predict at present, given various possible rate-limiting factors (competent staff availability, infection prevention and control and steady supplies of personal protective equipment, hospital infrastructure, care-seeking preferences) . At baseline, Niger, Nigeria and Mauritius had some 7000, 101,000 and 4000 hospital beds respectively , of which presumably only a fraction could be assigned to COVID-19 care without severely compromising essential routine health services. Whether these capacities would be sufficient highly depends on the real R0 values of Covid-19 epidemics in these countries. When we assume early country-specific Rt estimates for R0, non-pharmaceutical interventions could effectively be used to maintain hospital bed demand below these levels. However, when we assume global R0 estimates for R0, the intervention strategies we explored (see below) would exceed these baseline levels even under the most stringent (and unrealistic) implementation.
Reassuringly, our analysis suggests that both self-isolation of symptomatic people and moderate physical distancing could translate into very sizable reductions in severe cases and deaths, even when higher levels of R0 are assumed. The shielding option would likely require high levels of adherence and isolation to yield appreciable reductions in health service pressure, but could have a higher potential to reduce mortality in the short-term than other interventions, as it focuses on those who experience the highest CFR. This option also promotes herd immunity through mixing of other age groups and thus carries a lesser risk of further epidemic peaks once measures are lifted.
Different shielding arrangements could be considered, ranging from individual arrangements wherever people already live in multi-room houses or compounds, to neighbours or extended family-members grouping the most vulnerable individuals in vacated houses, to larger, albeit epidemiologically riskier re-housing (e.g. in quarantined street blocks). To avoid the problem of transmission within the shielded population, all such arrangements would need to eliminate any traffic of external people in and out of shielded accommodation as much as possible ensuring basic needs are met, while also instituting infection control barriers, e.g. a designated exchange point for supplies and safe social interactions and limiting contact as well as transmission within the shielded population through distancing, hygiene measures, face coverings if appropriate, etc. While our model does not explore the micro-level dynamics of how seeding of infection into these accommodations would affect residents, we showed, as expected, that the amount of contact among high-risk shielded people matters: zero contact, equivalent to individual shielding, equates to the highest effect of the intervention, while an increase of contact from baseline, e.g. if shielded people are rehoused in more crowded conditions than in their households of origin, dampens the utility of this approach and could even lead to an increase in cases compared to the baseline of no intervention.
We next combined the above interventions into a set of strategies, with a horizon of 12 months, that countries could consider. We assumed that any strategy would feature, at a minimum, self-isolation of symptomatic cases (we caution however that promotion of self-isolation should not discourage care-seeking for other life-threatening health problems, e.g. malaria, particularly among children). Our predictions suggest that countrywide lockdowns of 2 months, if effective, would temporarily suppress and delay epidemics for around 2–3 months, as noted in Europe: this reprieve would potentially enable countries to mobilise resources and plan the implementation of the next phase of their strategy. These findings do not in themselves support lockdown measures as a universal solution, however: such measures may be ineffective (i.e. fail to achieve a contact rate reduction consistent with effective reproduction number < 1, the condition for suppression) or more harmful than beneficial, including in health terms, if they severely disrupt economies and livelihoods or encounter mistrust and community resistance. Rather, our predictions merely indicate that well-implemented lockdowns would achieve the intended effect.
If lockdowns would not have been implemented, or after they end, we predict that a combination of general physical distancing and shielding high-risk individuals could be a potentially achievable mitigation strategy for countries to consider. Physical distancing entails a difficult trade-off between reducing attack rates (and hence epidemic peak size) and extending the duration of the epidemic, which in turn increases the period over which shielding should be maintained (as individuals will require to be shielded until well after the epidemic peak has finished). While stringent physical distancing (e.g. 50% reduction in extra-household contacts) would have a large impact, such reductions may only be achieved through socio-economically damaging and potentially unacceptable restrictions to work, education and/or other forms of public life. By contrast, a 20% reduction in contacts may be more achievable and sustainable—in some settings, this could involve a combination of hygiene promotion; increased access to water, soap and other cleaning supplies (e.g. through state subsidies); face coverings; and curtailment of some gatherings outside of work and school. These moderate reductions could already result in large reductions when R0 levels in the studied countries are lower than estimated globally.
It is unknown at present whether shielding is at all feasible and can attain our suggested target of 80% contact reduction between high- and low-risk people for 80% of high-risk people. We know of no precedent for this intervention, but instances of its implementation in different countries (Yemen, Ethiopia) are underway; preliminary qualitative research findings from various settings suggest that risk awareness is a barrier and that within-household shielding is preferable to other solutions (F Checchi, pers. comm.). Even at lower effectiveness levels, however, shielding would still offer benefits, particularly in terms of mortality, and accordingly need not be discounted as an option, particularly if it can be designed and led by communities themselves  with reference to cultural and religious norms around protection of the elderly and vulnerable, thereby requiring fewer resources than a top-down intervention. Complementary measures to protect high-risk people could include maintenance of high-adherence treatment for NCDs, tuberculosis and HIV; cash transfers to offset loss of income and facilitate isolation arrangements; and mobile medical services to bring routine healthcare to those shielding. To avoid stigma and cross-infection, people living with HIV and active TB cases may need to shield individually, and measures should be introduced to address protection risks, such as intimate partner violence .
Both distancing and shielding would be strongly dependent on the local R0: even moderate reductions in general contacts, and some degree of self-isolation when symptomatic, could achieve very large effects if transmissibility is lower than has hitherto been observed in Europe or China, e.g. in rural parts of Africa, where this intervention would therefore be relatively more cost-effective; by contrast, shielding effectiveness is relatively insensitive to R0, but would have to be sustained for a longer period if the epidemic is protracted, as one would expect in low transmissibility scenarios. Our analysis showed that, even at lower levels of R0, shielding would be beneficial to complement distancing, albeit with a lower marginal impact as would be observed in higher transmission settings, as might be expected in urban settings.
We only show estimates for the first 12 months in our analysis to make short-term predictions of the impact of different intervention strategies. There are still many unknowns about how SARS-CoV-2 would behave in in different contexts in Africa (rural, urban, peri-urban, displacement settings, etc.) and how people will respond to policy measures; hence, policy makers will continuously need to revisit the strategy to take current developments into account. Our analysis does not necessarily reflect the total epidemic size, as additional severe and critical cases could accrue during the second year, especially under strategies that focus heavily on physical distancing or if natural immunity to infection is short-lived .
Comparison with other studies
Although several studies have looked at the spread of COVID-19 in African countries [39,40,41,42,43,44], we are only aware of one other modelling study which considers the impact of different interventions on the spread of COVID-19 in Africa. Walker et al.  used a similar SEIR model and predicted a near 90% reduction in cases for sub-Saharan Africa assuming a 75% reduction in contacts starting at an incidence of 0.2 deaths per 100,000 population per week and sustained over the first 250 days of an epidemic. Comparisons between these studies are complicated due to different time periods of models and strategies investigated, but both point to a large unmitigated epidemic which can be reduced substantially due to strong physical distancing measures. However, even with these measures in place, all models suggest a high burden of disease and mortality across Africa.
While SEIR models have successfully been used to model COVID-19 epidemics to date in Europe, our predictions for African countries are subject to potential inaccuracy. Transmissibility may vary considerably across Africa, and it is possible that countries with very concentrated urban populations would see an acute exponential rise in cases, with a secondary, flatter curve affecting outlying rural regions: models accounting for these very distinct settlement types may be more useful for national planning. Consequently, the duration of time that countries will be affected by COVID-19 according to our model should be treated with caution. To date, estimates based on reported deaths  or cases [47, 48] suggest that African countries had transmissibility levels that may have been lower as observed in Europe and elsewhere. However, most African countries adopted stringent containment measures early on in the epidemic, meaning that even early Rt estimates as used in our analysis may not reflect a truly unmitigated epidemic, and underestimate the actual transmission potential of Covid-19 in the countries studied.
We were not able to reliably estimate country-specific R0 levels, due to relatively low levels of reported cases and deaths (Mauritius) and unknown levels of testing, underreporting of cases or impact of already implemented interventions within all countries. Instead, we conducted two separate analyses showing results using early levels of country-specific Rt estimates and global estimates of R0. The country-specific Rt estimates are generally lower than the globally estimated R0, but the median estimates of the former do fall within 95% uncertainty intervals of the latter. Whereas assumed R0 values have a large effect on the total size of the epidemic, they do not substantially change the qualitative decision of comparing different non-pharmaceutical interventions. However, shielding strategies do not significantly affect transmission in the overall population and may therefore not be preferred in low transmission settings, where moderate nondisruptive levels of distancing and self-isolation could be sufficient to bring R0 levels below 1.
While we accounted for age distributions, we did not have country-specific data on contact patterns among age groups. Instead, we used synthetic contact matrices extrapolated from European data by using local data on household, workplace and school composition in the African settings considered. A sensitivity analysis with empirical African contact pattern data suggested broadly similar intervention effects, but higher overall epidemic sizes; these matrices were collected in specific areas and may not be representative of contact patterns within the entire country or indeed Africa as a whole.
There is no clear consensus on the impact of asymptomatic infections on transmission. In accordance with other studies [19, 20], we assumed asymptomatic infections to be 50% less infectious. Our sensitivity analysis shows that this assumption did significantly affect the estimated impact of self-isolation strategies. We assumed that individuals who self-isolate would do so on the day of symptom onset, but not when pre-symptomatic. We made no assumptions about the effect of testing and behaviour change on individual behaviour, nor did we assume a potential delay between onset of symptoms and start of self-isolation.
Our results are also affected by disease severity assumptions. We applied age-specific risks of developing severe disease per infection, as estimated using data from China and the Diamond Princess outbreak, but shifted these to earlier life by a decade to represent plausible differences in biological age in Africa resulting from life-course exposures. This crude approach may be confounded by differences in the age-specific prevalence of co-morbidities in African countries, as well as inter-country differences in comorbidity prevalence. Specifically, conditions with potential (tuberculosis ) and as yet undocumented (HIV, undernutrition, sickle-cell disease) interactions with SARS-CoV-2 infection are far more prevalent in Africa than China and affect relatively young age groups : these could increase COVID-19 severity overall and shift the distribution of severe cases to younger age. Additionally, the proportion of detected and correctly managed cases of non-communicable diseases of known import to COVID-19 progression (cardiovascular disease, diabetes, chronic obstructive pulmonary disease, chronic kidney disease) is far lower in most of Africa than in China and Europe: this may further exacerbate disease severity. These differing patterns of co-morbidity may also affect the proportion of patients requiring intensive care and the CFR. Overall, our sensitivity analysis shows that uncertainty on these parameters has modest effects on the relative impact of interventions, but can have large effects on model predictions of absolute epidemic size. Our findings will thus need to be refined as more evidence accrues on the virus’ severity and CFR in African settings. Whereas these assumptions have large effects on the estimated total epidemic sizes, their impact on the relative effectiveness of different interventions is only minor.
Generally, our modelling framework is unsuited to incorporating information on underlying vulnerability factors such as population density, mobility within the country, armed conflict intensity, etc. , which may affect transmissibility, severity and/or the feasibility and effectiveness of response interventions. Our model would instead need to be updated with empirical evidence from each country, as it accumulates. For example, countries’ ability to scale up case management services (at least non-invasive respiratory support) from a nearly universally low baseline  may affect the actual CFR. Moreover, models that can track epidemics across space and time at a sub-national level would be preferable, with a structure (e.g. individual-based modelling) that can incorporate granular information about any mobility and behavioural data, the local age distribution of co-morbidities, treatment availability, the timing and uptake of specific interventions or, conversely, propagation (superspreading) events such as mass gatherings.
Severity and CFR assumptions mostly affect the usefulness of the shielding option. Shielding criteria should include a diagnosis of co-morbidity, and as such our findings, based solely on an age criterion, are under-estimates insofar as they exclude younger people with known comorbidities. However, in practice the low non-communicable disease treatment coverage in Africa means the age criterion would largely define who is shielded: lowering this threshold, e.g., to 50 years, would capture a larger fraction of undiagnosed co-morbidities. Tiered shielding approaches, whereby middle-aged moderate-risk people benefit from partial distancing measures (e.g. support to stay home from work), may also be worth considering.