Estimating the basic reproduction number at the beginning of an outbreak.
| Title: | Estimating the basic reproduction number at the beginning of an outbreak. |
|---|---|
| Authors: | Boonpatcharanon S; Department of Statistics, Chulalongkorn Business School, Chulalongkorn University, Bangkok, Thailand.; Heffernan JM; Mathematics & Statistics, York University, Toronto, Canada.; Centre for Disease Modelling, York University, Toronto, Canada.; Jankowski H; Mathematics & Statistics, York University, Toronto, Canada.; Centre for Disease Modelling, York University, Toronto, Canada. |
| Source: | PloS one [PLoS One] 2022 Jun 17; Vol. 17 (6), pp. e0269306. Date of Electronic Publication: 2022 Jun 17 (Print Publication: 2022). |
| Publication Type: | Case Reports; Journal Article; Research Support, Non-U.S. Gov't |
| Language: | English |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Francisco, CA : Public Library of Science |
| MeSH Terms: | COVID-19*/epidemiology ; Epidemics*; Canada/epidemiology ; Basic Reproduction Number ; Disease Outbreaks ; Humans |
| Abstract: | We compare several popular methods of estimating the basic reproduction number, R0, focusing on the early stages of an epidemic, and assuming weekly reports of new infecteds. We study the situation when data is generated by one of three standard epidemiological compartmental models: SIR, SEIR, and SEAIR; and examine the sensitivity of the estimators to the model structure. As some methods are developed assuming specific epidemiological models, our work adds a study of their performance in both a well-specified (data generating model and method model are the same) and miss-specified (data generating model and method model differ) settings. We also study R0 estimation using Canadian COVID-19 case report data. In this study we focus on examples of influenza and COVID-19, though the general approach is easily extendable to other scenarios. Our simulation study reveals that some estimation methods tend to work better than others, however, no singular best method was clearly detected. In the discussion, we provide recommendations for practitioners based on our results. |
| Competing Interests: | The authors have declared that no competing interests exist. |
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| Entry Date(s): | Date Created: 20220617 Date Completed: 20220621 Latest Revision: 20220716 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC9205483 |
| DOI: | 10.1371/journal.pone.0269306 |
| PMID: | 35714080 |
| Database: | MEDLINE |
Case Reports; Journal Article; Research Support, Non-U.S. Gov't