Regional variation and epidemiological insights in malaria underestimation in Cameroon
Notice bibliographique
Résumé
Abstract Malaria, caused by Plasmodium parasites and transmitted by female Anopheles mosquitoes, is most common in tropical regions, especially in Sub-Saharan Africa. Despite significant global effort to control and eradicate the disease, many cases and deaths are still reported yearly. These efforts are hindered by several factors, including the severe underestimation of cases and deaths, especially in Africa, making it difficult to assess the disease burden accurately. We used a mathematical model of malaria, incorporating the underestimation of cases and seasonality in mosquito biting rate, to study the disease dynamics in Cameroon. Using a Bayesian inference framework, we calibrated our model to the monthly reported malaria cases in ten regions of Cameroon from January 2019 to December 2021 to quantify the underestimation of cases and estimate other important epidemiological parameters. We performed Hierarchical Clustering on Principal Components analysis to understand regional disparities, looking at underestimation rates, population sizes, healthcare personnel, and healthcare facilities per 1,000 people. We found varying levels of underestimation of cases across regions, with the East region having the lowest underestimation (14%) and the Northwest region with the highest (70%). The mosquito biting rate peaks once every year in most of the regions, except in the Northwest region where it peaks every 6.02 months and in Littoral every 15 months. We estimated a median mosquito biting rate of over five bites per day for most of the regions with Littoral having the highest (9.86 bites/day). Two regions have rates below five bites per day: Adamawa (4.78 bites/day) and East (4.64 bites/day). The notably low estimation of malaria cases in Cameroon underscore the pressing requirement to bolster reporting and surveillance systems. Regions in Cameroon display a range of unique features, which may contribute to the differing levels of malaria underestimation. These distinctions should be considered when evaluating the efficacy of community-based interventions. Author summary We used a deterministic mathematical model of malaria that incorporated the underestimation of cases and seasonality in the biting rate of mosquitoes to retroactively study the dynamics of the disease in Cameroon from January 2019 to December 2021. We found varying levels of underestimation of malaria cases across regions in Cameroon, with the East region having 14% underestimation and the Northwest region having 70%. We found consistent malaria-induced death rates and natural immunity duration across Cameroon. We estimated that the mosquito biting rate for the Northwest region oscillated with a period of 6.02 months, while those of the remaining regions had a period of 12 months or more. Most regions had median mosquito biting rates exceeding five bites per day, with the Littoral having the highest (9.86 bites/day). In comparison, two regions had rates below five bites per day: Adamawa (4.78 bites/day) and East (4.64 bites/day). We clustered the ten regions into four major groups using the case underestimation rate, population size, total healthcare human resources per 1,000, and total healthcare facilities per 1,000.
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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 | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».