Three machine-learning models to predict dengue vectors Breteau Index based on meteorology and biotope in Fujian China

Abstract

Background: Dengue fever's rising prevalence in China underscores the need for improved surveillance tools. The Breteau Index (BI), critical for tracking dengue transmission, currently lacks timely and precise predictions, hindering effective response strategies. This study proposes a machine learning-enhanced BI predictive model to refine dengue forecasting in Fujian China. Methods: Data Collection: In this study, data about the Breteau Index (BI), meteorological conditions, and biotope characteristics from 2015 to 2022 were systematically gathered from the Siming District in Xiamen. Model Development: A range of machine learning algorithms was employed for BI prediction, including Support Vector Regression (SVR), Step-down Linear Regression, Random Forest (RF), Decision Trees (DT), Logistic Regression Models, Deep Neural Networks (DNN), LASSO Logistic Models, and Generalized Additive Models (GAM). Evaluation Metrics: The effectiveness and fit of the predictive models were quantitatively evaluated using the Akaike Information Criterion (AIC)/Bayesian Information Criterion (BIC) and R-squared values, alongside autocorrelation analyses of residuals to ensure statistical integrity. External Validation: Models were further validated with BI data from other Fujian cities to test their generalizability. Results: This study highlights the Deep Neural Network (DNN) model's superiority over alternative forecasting methods for the Breteau Index (BI), with significant determinants being stagnant water sources like unused containers and aquatic plant bonsais. Meteorological conditions, including precipitation(7-day lag), wind speed(3, 5, 7-day lags), sunshine(2-day lag), barometric pressure(3, 4-day lags), humidity, and temperature(7-day lag), were identified as critical influencers of BI. The analysis underscores the necessity of prompt waterlogged area management post-rainfall to mitigate dengue vector breeding, aligning with findings on the ecological preferences of Aedes albopictus. Conclusions: In conclusion, the validated Deep Neural Network (DNN) model efficiently predicts the Breteau Index (BI) in Xiamen, indicating its utility in Fujian. Key strategies include controlling waterlogged containers to reduce Aedes aegypti habitats and promptly addressing weather-related BI fluctuations within three days post-rainstorm to lower dengue transmission risk.