Computational epidemiology: Bayesian disease surveillance

Disease monitoring plays a crucial role in the implementation of public health measures. The demographic profiles of the people and the disease prevalence in a geographic region are analyzed for inter-causal relationships. Bayesian analysis of the data identifies the pertinent characteristics of the disease under study. The vital components of control and prevention of the disease spread are identified by Bayesian learning for the efficient utilization of the limited public health resources. Bayesian computing, layered with epidemiological expertise, provides the public health personnel to utilize their available resources optimally to minimize the prevalence of the disease. Bayesian analysis is implemented using synthetic data for two different demographic and geographic scenarios for pneumonia and influenza, that exhibit similar symptoms. The analysis infers results on the effects of the demographic parameters, namely ethnicity, gender, age, and income levels, on the evidence of the prevalence of the diseases. Bayesian learning brings in the probabilistic reasoning capabilities to port the inferences derived from one region to another