A robust kinetic theory-based model for dengue transmission integrating climatic  and socio-economic dynamics

Background : Dengue is one of the most critical mosquito-borne viral diseases that has rapidly spread in recent years. Despite the fact that the first dengue vaccine was licensed in 2015, the vaccine is still in the experimental stage. As a result, the primary prevention strategies are vector density control and interruption of human-vector contact. To control the spread of disease, it is critical to first understand the spreading trend of the disease and then design effective prevention and control strategies. Methods : In this study, we focus on developing a kinetic-based, data-driven quasi-equilibrium IR model incorporating controlling events along with weekly reported dengue cases, rainfall, temperature, and humidity, with specific time lags for each variable. As a case study, data from the CMC area, Sri Lanka, have been used. To capture realistic disease dynamics, two socioeconomic events have been included: the large-scale cleaning and fogging campaign in 2018 following the 2017 outbreak, and reduced mobility during the COVID-19 restrictions in 2020. Results : The simulated dengue incidence patterns closely matched reported data from 2016 to 2024, with accuracy levels ranging from 65% to 100% across different years. Comparison with a traditional kinetic SIR model showed negligible differences in infected host populations, indicating robust qualitative and quantitative agreement. The Gamma distribution effectively captured human contact patterns, and the model reflected seasonal trends influenced by climatic data and social behavior. Conclusions : The proposed model offers a reliable framework for predicting dengue transmission by integrating climatic, vector-related, and human behavioral factors. Its high accuracy highlights its usefulness for planning effective control strategies, especially in settings where vaccine coverage remains limited. Future improvements could include incorporating more detailed information on human movement and behavior to enhance prediction accuracy further and support efficient resource allocation.