A Perspectives on Proximate System and 4D Programming in Local Outbreak Detection through Weather Analysis for Epidemic Prediction

Abstract

This study introduces a novel predictive framework integrating the Proximate System and 4D Programming to forecast local disease outbreaks from short-term weather anomalies. The model transcends conventional epidemiological and AI-based approaches by simulating the cause–event–effect relationship between meteorological fluctuations and disease emergence through a probabilistic, time-dependent structure. In the Proximate System, weather parameters such as temperature, humidity, precipitation, and wind speed act as causal vectors, dynamically linked to epidemiological effects within a continuously evolving volumetric space. 4D Programming extends this model by defining time as an active rotational force, transforming outbreak probability into a dynamic, multi-reality construct. The integrated system autonomously analyzes local weather data, quantifies outbreak tendencies in real time, and supports multi-disease prediction across infectious, zoonotic, and cardiovascular domains. This interdisciplinary fusion of meteorology, mathematics, and computational modeling provides a transformative approach to epidemic prediction, shifting public health from reactive surveillance to proactive, environment-driven prevention.