Hybrid neural–mechanistic modeling of leptospirosis transmission with environmental drivers: Evidence from Thailand

Abstract

Accurate infectious-disease forecasts are essential for timely public health decision-making. In this study, we develop a hybrid modeling framework that combines compartmental models with Long Short-Term Memory (LSTM) networks to estimate a key time-varying epidemiological parameter as a case study for leptospirosis in Thailand. Our framework uses an LSTM-ODE model trained on environmental covariates (rainfall, flooding, and temperature) and infected human cases to infer the transmission rate, which shows strong seasonal and environmental dependencies. The results demonstrate that including flooding, temperature, and human cases improves the prediction of infected individuals (MSE = 35.41). Our findings suggest that the integrated hybrid framework offers a more precise solution by improving the estimation of a key epidemiological parameter. The model accommodates multiple input features and, once trained, enables inference suitable for forecasting. Its ability to generate predictions using environmental covariates, particularly when epidemiological surveillance data are incomplete or delayed.