Tropicalisation of epidemiological models in Africa: A mixed and hybrid approach to better predict COVID‐19 indicators
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».