Big Data in Public Health: Real-Time Epidemiology Using Mobility and Environmental Data to Predict Outbreaks

Abstract

The recent booming growth of Big Data analytics has revolutionized the contemporary surveillance of the health of human populations by providing real-time epidemiology that can spot outbreaks more quickly than conventional surveillance mechanisms. This paper examines how mobility data and environmental data can be integrated to better predict the outbreak of infectious diseases at hand. Based on massive data sets obtained through the movements of mobile phones, satellite derived environmental signals, and sensor generated weather forecasts, we have created a spatial-temporal predictive model based on the state-of-the-art machine learning models. The approach included the preprocessing of the heterogeneous data, the development of the outbreak risk models, and the assessment of the predictive performance in the form of the accuracy, sensitivity, and the space correlation measures. Findings indicate that changes in population mobility have a strong relationship with the dynamics of disease transmission, and the environmental factors especially temperature, humidity, and air quality are effective modulating factors of an outbreak. Together, such data sources enhance accuracy in prediction and contribute to the creation of real-time outbreak risk maps. These results demonstrate how Big Data, real-time epidemiology, and predictive analytics can enhance the quality of decisions in the field of public health, improve resource distribution, and contribute to proactive control. This paper finds that mobility and environmental data integration offer a powerful base upon which the next generation system of public health surveillance could be built.