Tropicalisation of epidemiological models in Africa: A mixed and hybrid approach to better predict COVID‐19 indicators

Context Since the outbreak of the SARS‐COV2 epidemic turned into a COVID‐19 pandemic, international bodies such as the WHO as well as governments have announced projections for morbidity and mortality indicators related to COVID‐19. Most of them indicated that the health situation would be worrying. Although using artificial intelligence with mathematical algorithms and/or neural networks, the results of the SIR models were poorly performing and not very accurate in relation to the observed reality in the African states in general and in Senegal in particular. Hence the imperative need to configure the modelling process and approach considering local contexts. Method The model implemented is a mixed prediction model based on the Bucky model developed by OCHA and adapted to the context. The construction of the mixed model was done in two steps (basic model with publicly available data, such as those from United Nations‐like organisations such as OCHA or WHO for Senegal), (adding more specific data collected through the mixed epidemiological survey). This survey was conducted in Senegal in six localities (Dakar, Thies, Diourbel, Kedougou, Saint‐Louis and Ziguinchor) chosen according to the number of confirmed cases of COVID‐19. In total, 1000 individuals distributed in proportion to the size of the regions were interviewed in April 2021. Results The projected cases in the baseline model were already considerably higher than the cases reported in April. This may be plausible, given the low detection rates throughout Senegal during this period. However, the hybrid model predicted an even higher infection rate than the baseline, perhaps mainly due to vulnerability related to food insecurity and solid cooking fuels. This may mean that there would be more unreported cases than reported. Overall, the mortality rate of both models would be considerably lower than the government‐reported mortality rate, even though the number of confirmed cases remains high. This may be an underestimate of the death rate. Conclusion An accurate and reliable prediction in times of epidemics and/or pandemics, such as COVID‐19, should be based on mixed or hybrid data integrating a quantitative and qualitative approach to enable better policymaking. The projections resulting from this approach would still be effective and would take better account of local realities and contexts, especially for developing countries.