RtEstim: Effective reproduction number estimation with trend filtering

Abstract

AbstractTo understand the transmissibility and spread of infectious diseases, epidemiologists turn to estimates of the effective reproduction number. While many estimation approaches exist, their utility may be limited. Challenges of surveillance data collection, model assumptions that are unverifiable with data alone, and computationally inefficient frameworks are critical limitations for many existing approaches. We propose a discrete spline-based approachRtEstimthat solves a convex optimization problem—Poisson trend filtering—using the proximal Newton method. It produces a locally adaptive estimator for effective reproduction number estimation with heterogeneous smoothness.RtEstimremains accurate even under some process misspecifications and is computationally efficient, even for large-scale data. The implementation is easily accessible in a lightweightRpackagertestim.