Neural parameter calibration for dengue outbreak forecasting.

ABSTRACT: Dengue fever poses a growing public health challenge in tropical and subtropical regions, with transmission driven by complex interactions among viral and host. Computational models, often expressed as ordinary differential equations (ODEs), are widely used to understand complex systems such as dengue fever transmission dynamics. However, traditional parameter estimation methods such as Markov chain Monte Carlo (MCMC) often require complex setups and are computationally expensive. In this study, we choose a compartmental model extended to human and mosquito populations, estimate its parameters using neural parameter calibration (NPC), and validate the approach using datasets collected from South America and Southeast Asia. The extended compartment model (ECM) is expressed using seven ODEs,

SUBMITTER: Pham HV 

PROVIDER: S-EPMC12935225 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature