Revealing Dengue Dynamics: A Novel Bin-Wise Gaussian Process Model for Probabilistic Forecasting

Aedes aegypti thrives in urban settings, where socio-economic and climatic factors sustain dengue transmission. This study develops a generalizable Gaussian Process model integrating these determinants to improve incidence forecasting and intervention planning.This study analyzed dengue transmission dynamics in Barranquilla, Colombia, using a dataset from 2018–2023 across 20 metropolitan areas. Monthly dengue cases were modeled against socio-environmental factors, including socio-economic strata, temperature, and rainfall. The dataset was normalized by adjusting dengue case counts relative to the population of each municipality, enabling comparisons across locations. Rainfall was modeled using an exponential distribution, introducing a novel approach. Socio-economic infrastructure indicators, temperature, and rainfall were discretized into 32 bins based on defined thresholds. A parameter-dependent function framework predicted month-to-month dengue progression, emphasizing environmental and socio-economic influences on disease transmission patterns.This study utilized GP regression to predict month-to-month variations in dengue cases, leveraging socio-economic, temperature, and rainfall data. The models classified transmission dynamics into six categories: extinction, monotonic growth, oscillations, transient pulses, low-level persistence, and high uncertainty. High-risk scenarios were associated with elevated temperatures, rainfall, and lower socioeconomic strata, while conditions limiting mosquito development predicted rapid declines in transmission. Bin-specific analysis revealed ecological feedback driving oscillatory patterns and transient outbreaks. These robust predictions informed targeted mitigation strategies, despite uncertainties in certain parameter combinations, providing nuanced insights into the dynamics of dengue.This study integrates socio-economic, environmental, and epidemiological factors into a probabilistic, adaptable model, providing a scalable framework to enhance vector-borne disease forecasting and public health decisions.