Kinetic model reproduces Sri Lanka dengue patterns after adding climate and social factors

A mathematical modeling study developed in Sri Lanka reports that a kinetic, data-driven model reproduced reported weekly dengue incidence from 2016 through 2024 with annual accuracy ranging from 65% to 100%. The model combined dengue cases with rainfall, temperature and humidity to estimate vector density and captured seasonal patterns influenced by climatic conditions and human behavior. These findings are summarized in the research abstract, which describes the case study as a simulation rather than a clinical evaluation.

The researchers built a kinetic-based quasi-equilibrium infection-and-recovery model with data-driven components and control events. A Sugeno-type fuzzy inference system estimated per-capita vector density from climatic variables and past dengue incidence, while a Gamma distribution represented human contact patterns. Weekly reported dengue cases, rainfall, temperature and humidity were incorporated with specified time lags for model variables. The model also represented two socio-economic events: the large-scale cleaning and fogging campaign conducted in 2018 following the 2017 outbreak, and reduced mobility during COVID-19 restrictions in 2020. The abstract does not provide enough methodological detail to determine whether mobility itself was entered with a time lag.

In the case study using data from the CMC area of Sri Lanka, simulated dengue incidence patterns closely matched the reported data across 2016–2024, with the stated accuracy levels varying by year. Comparisons with a traditional kinetic susceptible-infected-recovered model showed negligible differences in estimated infected-host populations. The researchers interpreted this as qualitative and quantitative agreement between the two modeling approaches. The model also reflected seasonal trends associated with climatic data and social behavior, according to the abstract.

The study presents a framework for combining climatic, vector-related and human-behavioral information in dengue transmission modeling. Its authors propose that such integration could assist transmission prediction and planning of control strategies, particularly where vaccine coverage is limited. That application remains a proposed use rather than evidence of improved real-world control outcomes. The reported evaluation is geographically bounded to one Sri Lankan area, and the abstract does not describe validation in other locations, uncertainty intervals, performance against additional modeling methods or the effects of individual interventions separately from the combined model. It also identifies more detailed information on human movement and behavior as a possible direction for improving predictive accuracy.

Subject of Research: Kinetic modeling of dengue transmission using climate, incidence and socio-economic data

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

Article References: Hansika, S., Erandi, H., Perera, S., Attanayake, C., & Amadi, M. (2026). A robust kinetic theory-based model for dengue transmission integrating climatic and socio-economic dynamics. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14492-z

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14492-z

Keywords: dengue transmission, kinetic model, mathematical modeling, Sri Lanka, climate variables, vector density, case study, COVID-19 mobility, vector control, robust, kinetic, theory-based