Statistical and visual analysis of spatio-temporal dynamics of dengue fever epidemic

Public health-care data possess spatio-temporal features and it evolves with time. For example, patient count visiting an ER in a hospital evolves with time. Clustering of such input data patterns into different groups over a certain temporal duration is useful for the purpose of health-care surveillance purpose. The objective of this study is to visualize and analyze these data patterns to get an insight into data characteristics for the purpose of the health-surveillance and to correlate these findings with the possible reasons for epidemic spread. The dynamics of this spatio-temporal pattern analysis is useful for epidemic spread surveillance in large metropolitan cities consisting of multiple towns or regions as it shows both the growth and movement of disease trend patterns over a certain time which is ultimately useful for disease cure and prevention measure. We further correlate these outcomes with demographic and weather data distribution pattern over a certain period of time.