ATD: Collaborative Research: Multi-task, Multi-Scale Point Processes for Modeling Infectious Disease Threats

This project develops new point-process based algorithms for modeling and forecasting event-level infectious disease data, such as when an epidemic is emerging, near elimination, or for contact tracing. The methods developed through the project have applications to source detection of super-spreader events, identification of case importation trends, and providing better risk assessments of emerging epidemics and future pandemics.

The methods developed through the project also have applications beyond epidemiology where point processes are used, including social media, seismology, and criminology. The project will train two PhD students in statistics and computer science. This project will support one graduate student per year at each university for each of the three years of the grant.

This project develops new point-process based algorithms for solving four important tasks that arise in modeling infectious disease threats over a range of temporal and spatial scales: 1) incorporating realistic transmission and reporting mechanisms, 2) link prediction in the transmission graph connecting separate geographic regions under surveillance, 3) source detection of the spatial-temporal and network locations of super-spreader events, and 4) modeling emerging disease epidemics over timescales of decades and spatial scales of the globe. Expectation maximization algorithms are derived to infer a probabilistic branching structure that can be used for contact tracing and source detection.

Multivariate Hawkes processes are formulated to infer cross-transmission across separate geographic regions, where new theory and methods are needed to handle reproduction above the critical threshold of 1. Point process analogs to compartmental models are developed through the project that can incorporate realistic transmission and under-reporting mechanisms (e.g. exposure period, asymptomatic cases) to improve forecasts and prevalence estimation.

Finally, this project develops models of emerging epidemic events for determining the separability of disease parameters vs. space and time and assessing the risk that an outbreak will become a pandemic.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.