Trend Filtering Deconvolution Methods in Epidemiology

Reported cases provide a delayed and incomplete view of infection incidence and so make it difficult to recover the true timing and magnitude of an epidemic. To use such surveillance data to estimate latent infections, we develop a trend filtering deconvolution approach. We use this to retrospectively estimate daily COVID-19 infections across U.S. states prior to the Omicron period using seroprevalence data in an antibody prevalence model. We adapt it for the Omicron period by using viral shedding and wastewater concentration data that allow for infection estimation when case and seroprevalence surveillance data become less reliable. Then we study the statistical properties of the estimator by extending trend filtering theory to the case where the design arises from delay distributions as a Toeplitz convolution operator and develop a backfitting extension to handle multiple overlapping infection curves, such as those from different variants.

To join this seminar virtually, please request Zoom connection details from hr.ops@stat.ubc.ca.