Reconciling model predictions with low reported cases of COVID-19 in Sub-Saharan Africa: insights from Madagascar

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.

What Can COVID-19 Teach Us about Using AI in Pandemics?

“…The data sources could be numerous and include governmental data, social media, and collateral data from mobile devices or public surveillance systems [ 17 , 37 , 38 , 41 ]. The relative scarcity of data for training constrains but does not prohibit AI development and deployment even when resources are limited [ 42 ]. Furthermore, the pre-training of AI can help counteract the fact that data are only limited.…”

“…AI can couple existing large databases of living area characteristics (e.g., type of housing, population, and movement of people) with dynamics of the outbreak. Subsequently, AI provides even more accurate prediction of the disease spread, privacy concerns about collecting and sharing this data notwithstanding [ 22 , 24 , 25 , 42 ]. AI’s potential to analyze at the level of a city, district, and neighborhood is a powerful feature of this technology [ 10 , 25 ].…”

“…Rapid identification of treatment and vaccine candidates Erroneous delegation of decisions to AI with insufficient human oversight, e.g., of clinical trials or the role of social disparities [41][42][43]45,46,48,73] Not surprisingly, the notion of incorporating AI decision-making in healthcare stirs considerable controversy, including about responsibility and ethics [13,32]. However, the point here is not that AI will make more and better decisions, but rather that it could facilitate more targeted and ethical decision-making.…”

Integrating Health Systems and Science to Respond to COVID-19 in a Model District of Rural Madagascar

“…As of May 2021, COVID-19 has infected over 160 million people globally and killed more than 3 million, with the highest reported per capita tolls in Europe, North America, and South America in the first year, with the worst surge of the second year occurring in India ( 1 ). One of the great mysteries of the pandemic has been its relatively smaller impact on the World Health Organization (WHO) African Region, which endures high burdens of other infectious diseases, including respiratory infections, for reasons that are also relevant to COVID-19 epidemiology: crowding inside and outside of the home, inadequate water and sanitation infrastructure, and under-resourced health systems ( 2 , 3 ). While Sub-Saharan Africa (SSA) is home to 16% of the world's population, its share of reported COVID-19 cases is 3% of the global case count, more than a year after the first COVID-19 cases were diagnosed in China ( Figure 1A ) ( 1 ).…”

“…While Sub-Saharan Africa (SSA) is home to 16% of the world's population, its share of reported COVID-19 cases is 3% of the global case count, more than a year after the first COVID-19 cases were diagnosed in China ( Figure 1A ) ( 1 ). This could be explained by underlying differences in the effectiveness and timing of control measures, immunology, age distribution, contact structure, or low rates of testing and under-reporting ( 2 , 3 ).…”