A novel framework for calibration and prediction of stochastic compartmental transmission dynamic models of novel pathogens

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During the period of initial emergence of novel pathogens, the accurate estimation of key epidemic parameters and prediction of future epidemic trajectory is challenging because observations only partially reflect the true state of the epidemic. Stochastic transmission dynamic models are especially useful for inference and projections during the emergence of novel pathogens given the importance of chance events when the number of infectious individuals is small. My research objective is to develop and evaluate a framework for real-time calibration of stochastic compartmental models using observed, but likely imperfect, epidemic data. This framework will allow me to estimate key epidemic parameters such as the number of secondary cases or the mean duration of infectiousness as well as the number of currently infected persons. Additionally, our framework will permit both short-term predictions (e.g the expected diagnoses in the next week or cumulative diagnoses over the next three weeks) and longer-term predictions (e.g the overall attack rate). My framework for real-time calibration is based on an objective function that I developed in a Systems Biology context and successfully applied to highly nonlinear stochastic signaling pathways. In current work I have been doing together with my academic host, I incorporated this objective function into a Bayesian framework and used simulation studies to investigate its capacity to estimate key epidemic parameters and to predict the epidemic time course. I have submitted a manuscript that compares my approach with state of the art methods. I was able to show that my method outperforms these current benchmark approaches for both inference and prediction. If funded, I will: 1) apply this framework to recent influenza surveillance data and perform a retrospective calibration and evaluate the predictive ability of this method. This will allow us to evaluate the accuracy of the method on real data; 2) compare my framework's performance to other state-of-the-art calibration and prediction methods on the same data sets by retrospective prediction; and 3) use the framework for real-time forecast on next season's (i.e. 2016-2017) influenza epidemic by participating in, the Epidemic Prediction Initiative Competition hosted by the US Center for Disease Control and Prevention (US CDC). While we have written this project with a specific application for influenza, our framework is not limited to a specific pathogen. The frequency at which the global community has been forced to confront the emergence or reemergence of infectious pathogens suggests a large public health and economic impact of improved approaches for early and accurate prediction of epidemic behavior. Such methods will improve the capacity of policy makers to better use existing resources to control epidemics and to balance the risk of major outbreaks with the social and economic costs interventions.