Estimation of local time-varying reproduction numbers in noisy surveillance data
Abstract
A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 15, 2022
- Source ID
- 10.1098/rsta.2021.0303
Entities
People
- Brian Gregor
- Eric D. Kolaczyk
- Katia Bulekova
- Laura Forsberg White
- Wenrui Li
Organizations
- Army Research Office
- Boston University
- National Institutes of Health