This proposal outlines new approaches to epidemiological surveillance and outbreak management that fully integrate ideas from mathematical modeling for infectious diseases and Bayesian statistics. For surveillance problems, we develop hierarchical mixture models for outlier identification that take into account the evolution of the epidemic and its spatial distribution. In these models, the components in the mixture correspond to alternative dynamics consistent with different diseases sharing a common set of symptoms. For outbreak management, we develop simulation-based algorithms for sequential optimal design under uncertainty that can be used for optimal prospective design of interventions in epidemics. The algorithm is described in the more general setting of partially observed Markov decision problems, and can potentially be applied in other areas such as clinical trial design, control and economics.
The emergence of new diseases, either naturally or as the result of a biological attack, is one of the most important threats to national security; early detection and adequate intervention are key to saving lives and minimizing the damage caused by such an outbreak. However, early detection can be extremely difficult when symptoms are similar to those of diseases already endemic in the population (for example, fever and sore throats which are common symptoms of colds and influenza, are also symptoms of respiratory Anthrax), and a detailed analysis of the space-time patterns is necessary in order to be able to separate the presence of a new disease from the random fluctuations inherent to the dynamics of the preexisting disease. Similarly, the design of interventions (vaccination, quarantine and culling, just to mention three possibilities) is impaired in this setting by the lack of knowledge about the infectiousness of the new disease and the efficacy of available vaccines. This research develops new algorithms for disease monitoring and outbreak management under uncertainty that have the potential to greatly improve the ability of government and international agencies to identify and intervene in outbreaks of emerging infectious diseases.
The emergence of new diseases, either naturally or as the result of biological attack, is one of the most important threats to national security; early detection and adequate intervention are key to saving lives and minimizing the damage caused by such an outbreak. However, early detection can be extremely difficult when symptoms are similar to those of diseases already endemic in the population (for example, fever and sore throats which are common symptoms of colds and influenza, are also symptoms of respiratory Anthrax), and a detailed analysis of the space-time patterns is necessary in order to be able to separate the presence of a new disease from the random fluctuations inherent to the dynamics of the preexisting disease. Similarly, the design of interventions (vaccination, quarantine and culling, just to mention three possibilities) is impaired in this setting by the lack of knowledge about the infectiousness of the new disease and the efficacy of available vaccines. This project has developed new algorithms for disease monitoring and outbreak management that combine ideas from mathematical modeling (which help us understand the random nature of the time dynamics of the epidemic) with recent developments in Bayesian statistical models, which allow us to more accurately identify emerging diseases and design more effective interventions. Some of the contributions from this project includes new statistical model that allow us to capture and predict the variability of disease rates across space and time simultaneously for multiple diseases as well as novel classification techniques that are capable of identifying classes that are not present in the set of examples used to train the algorithm. Furthermore, the research supported by this award has made contributions both to general statistics methodology. These contributions are important because these methods are applicable in a wide range of applications beyond those covered in this award. The research developed under this award has had a direct impact on our defense and national security capability by providing more powerful tools for monitoring disease outbreaks. In addition, it has had a positive impact in the training of a modern STEM workforce by partially supporting the PhD dissertation work of four graduate students. Finally, we have disseminated our results widely by publishing 14 manuscripts in peer-reviewed publications and making over 20 presentations at scientific meetings and academic departments in the U.S. and abroad.
