rtestim: Time-varying reproduction number estimation with trend filtering

To understand the transmissibility and spread of infectious diseases, epidemiologists turn to estimates of the instantaneous reproduction number. While many estimation approaches exist, their utility may be limited. Challenges of surveillance data collection, model assumptions that are unverifiable...

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Published in:PLoS computational biology 2024-08, Vol.20 (8), p.e1012324
Main Authors: Liu, Jiaping, Cai, Zhenglun, Gustafson, Paul, McDonald, Daniel J
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Basic Reproduction Number
Communicable Diseases - epidemiology
Computational Biology - methods
Computer Simulation
Disease transmission
Epidemiological Models
Epidemiologists
Forecasts and trends
Humans
Medical research
Medicine, Experimental
Methods
Models, Statistical
Poisson Distribution
Sentinel health events
Software
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Cai, Zhenglun
Gustafson, Paul
McDonald, Daniel J
description To understand the transmissibility and spread of infectious diseases, epidemiologists turn to estimates of the instantaneous 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 approach that solves a convex optimization problem-Poisson trend filtering-using the proximal Newton method. It produces a locally adaptive estimator for instantaneous reproduction number estimation with heterogeneous smoothness. Our methodology remains accurate even under some process misspecifications and is computationally efficient, even for large-scale data. The implementation is easily accessible in a lightweight R package rtestim.
doi_str_mv 10.1371/journal.pcbi.1012324
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subjects Algorithms
Analysis
Basic Reproduction Number
Communicable Diseases - epidemiology
Computational Biology - methods
Computer Simulation
Disease transmission
Epidemiological Models
Epidemiologists
Forecasts and trends
Humans
Medical research
Medicine, Experimental
Methods
Models, Statistical
Poisson Distribution
Sentinel health events
Software
title rtestim: Time-varying reproduction number estimation with trend filtering
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