A Hybrid Deep Learning-Mechanistic Modeling Framework for Dengue Transmission Dynamics in Guangdong, China
Notice bibliographique
Résumé
Abstract Dengue fever is a mosquito-borne viral disease with strong seasonality, periodicity, and spatial heterogeneity, posing a persistent global public health threat. We present a hybrid modeling framework that couples a recurrent neural network (RNN) with a typical Susceptible–Infected–Recovered (SIR) compartmental model to investigate dengue transmission dynamics in Guangdong, China, from 2016 to 2024. By incorporating mosquito surveillance data, meteorological variables (temperature, humidity, and precipitation), public health intervention intensity, and reported dengue cases, our model captures the complex interactions among climate, vector density, intervention policies, and disease spread. After evaluating multiple RNN architectures, the Long Short-Term Memory (LSTM) model was selected for its superior performance in predicting the mosquito ovitrap index (MOI) from climatic variables, providing a data-driven proxy for vector abundance used in the SIR model. The hybrid model is constructed upon a partially observed Markov process (POMP) and calibrated using iterated filtering for parameter optimization. Model results identify climate variables as dominant drivers of dengue transmission, while non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic significantly suppressed case numbers. Scenario analysis indicates that moderate NPIs could effectively reduce outbreak magnitude, with the model estimating that approximately 46,120 dengue cases were averted during the pandemic period. This study highlights the utility of integrating deep learning with mechanistic modeling to improve understanding and forecasting of vector-borne disease dynamics. The proposed framework offers a robust and interpretable approach for developing climate- and vector-informed early warning systems and informing data-driven public health decision-making. Author Summary Dengue fever is a mosquito-borne disease that causes large outbreaks in many tropical and subtropical regions, including southern China. Predicting when and where outbreaks will occur is difficult because dengue transmission is influenced by many factors, such as climate, mosquito populations, and public health measures. In this study, we developed a hybrid modeling framework that integrates artificial intelligence with mechanistic disease modeling. Specifically, we used a deep learning method to predict mosquito abundance from climate conditions and then linked to a mathematical model for dengue transmission. Applying this approach to data from Guangdong Province, China between 2016 and 2024, we found that climate strongly influences mosquito biting behavior, which in turn drives dengue transmission. We also showed that non-pharmaceutical interventions during the COVID-19 pandemic substantially reduced dengue cases, with our model estimating that more than 46,000 infections were prevented. Our innovative approach demonstrates how combining modern machine learning with mechanistic models can improve our ability to understand and forecast outbreaks of mosquito-borne diseases. Our framework may help inform early warning systems and guide public health strategies to reduce the burden of dengue and other vector-borne diseases.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| 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 ».