Tropicalisation of epidemiological models in Africa: A mixed and hybrid approach to better predict COVID‐19 indicators
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".