Infectious diseases, though initially tend to be limited geographically to a reservoir; a subsequent spatial variation in disease prevalence (including spread & intensity) arises from the underlying differences in physical-biological conditions that support pathogen, its vectors & reservoirs. Different factors like spatial proximity, physical & social connectivity, & local environmental conditions which add to its susceptibility influence the occurrence[2]. In Disease management, analysis of historical data over various aspects of geography, epidemiology, social structures & network dynamics need to be accounted for. Large amounts of data raise issues of data processing, storage, pattern identification, etc. In addition, identifying the source of disease occurrence & its pattern can be of immense value. ST-DM of disease data can be an effective tool for endemic preparedness[3], as it extracts implicit knowledge, spatial & temporal relationships, or other patterns inherent in such databases. Here, Core Region is defined as a set of spatial entities(eg.counties) aggregated over time, which occur frequently at places having high values in a defined region (considering areas of influence around them)[1].
Objective:
This work leverages spatio-temporal data mining (ST-DM), the MiSTIC (Mining Spatio-Temporally Invariant Cores)[1,6] method for infectious disease surveillance, by identifying a) Extent of spatial spread of disease core regions across populations-scale of disease prevalence b) Possible causes of the observed patterns-for better prediction, detection & management of infectious disease & its outbreaks.