Using dynamic Bayesian networks for incorporating non-traditional data sources in public health surveillance

Abstract

It is generally challenging to obtain the exact disease prevalence, as the true cases of a disease in the population level are not easy to identify. Available and relevant data sources such as administrative or clinical health data are used in public health surveillance as a proxy to estimate the disease prevalence. Traditionally, these data sources span through healthcare utilization information such as emergency department visits, pharmacy drug sales, or laboratory test orders. In addition to incompleteness, these data sources are not usually available in a timely manner. Timeliness is an important factor for prevalence estimation for some conditions such as infectious diseases, especially at the time of an epidemic. For instance, in an influenza pandemic such estimates must be obtained within a day or two. In recent years several non-clinical and non-traditional data sources have been introduced to public health with the potentials to provide signals on a disease rate or to provide a feedback on the trends of a disease. Ideally, combining these new sources with the ones routinely used should help to identify disease cases more efficiently. However, building a construct capable of incorporating data from these various sources in a coherent manner is not trivial. In this research, we consider the case of H1N1 pandemic as the infectious disease of interest and we use media reports of deaths from H1N1 on the web as a non traditional data source. We propose to use dynamic Bayesian networks from the class of probabilistic graphical models in order to combine this new data source with traditional ones through exploration of the possible probabilistic relationships between these data streams. This is an initial step towards building a framework that can potentially support aggregation of heterogeneous data for a real-time estimation of a disease prevalence. Our preliminary results show that the proposed model generalizes well.