Likelihood-Based Inference for Stochastic Epidemic Models with Application to High-Resolution Contact Tracking Data

Stochastic epidemic models such as the Susceptible-Infectious-Removed (SIR) model are widely used to model the spread of disease at the population level, but fitting these models present significant challenges when missing data or latent variables are present. In particular, the likelihood function has long been considered intractable in such settings. We will discuss recent advances using generating function techniques that enable likelihood computations without model simplifications in the presence of missing infection and recovery times. Motivated by a study of influenza transmission with social contact tracking data, we then present a data-augmented MCMC algorithm for fitting parameters of the SIR model when the underlying contact network evolves through time and is dependent on individuals' disease statuses. We demonstrate how accounting for the dynamics of the epidemic and network models jointly is crucial for valid inference, and apply the method to analyze data from the eX-FLU study of influenza on a college campus.

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About Jason Xu

I am an Assistant Professor in the Department of Statistical Science at Duke University. Prior to joining the department, I worked with Ken Lange at the University of California Los Angeles with support from the NSF Mathematical Sciences Postdoctoral Research Fellowship. I completed my PhD in Statistics at the University of Washington advised by Vladimir Minin, where my work was funded by an NDSEG Fellowship.
I grew up in Tucson, where I received a BS in Mathematics from the University of Arizona in 2012. There I was mentored by Kevin Lin and William Vélez, supported by the Flinn Foundation Scholarship.