Human Biometeorological Forecasting of Climate-Sensitive Childhood Pneumonia in Bojonegoro, Indonesia

Introduction: Indonesia is currently facing increasing climate-related health risks, with childhood pneumonia remaining a key climate sensitive disease that reflects strong biometeorological influences of temperature, humidity, and rainfall. The 3,968 reported cases of childhood pneumonia in Bojonegoro for 2024 highlight the necessity for tailored forecasting instruments to enhance climate-responsive health strategies. This study aimed to develop and evaluate a Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX)-based model to forecast climate-sensitive childhood pneumonia in Bojonegoro. Methods: We analyzed monthly childhood pneumonia data alongside temperature, humidity, and rainfall from 2020–2024, supported by data characterization, correlation testing, and stability assessments. A climate- based predictive model—adjusted with a pandemic lockdown dummy—was then selected based on fit, accuracy, and residual diagnostics to generate forecasts for 2025–2029. Results and Discussion: From 2020 to 2024, Bojonegoro documented 21,032 childhood pneumonia cases, with a notable rise following periods of elevated humidity and rainfall. The SARIMAX (0,1,1)(0,1,1)[3] model, adjusted for lockdown effects, yielded robust forecasts indicating mid-year peaks aligned with monsoon transitions, followed by gradual declines, with humidity (–3.682) and rainfall (–0.124) serving as significant negative predictors of airborne transmission. It reduces pneumonia risk through enhanced droplet settling, decreased pathogen stability, and rainfall-driven washout effects. Conclusion: In conclusion, the lockdown-adjusted SARIMAX model identifies humidity and rainfall as reliable predictors for childhood pneumonia trends in Bojonegoro. It supports the development of climate-resilient health planning under the national guidelines, facilitating early-warning systems and targeted pneumonia prevention in vulnerable villages/sub districts.