Analyzing the Impacts of Public Policy on COVID-19 Transmission: A Case Study of the Role of Model and Dataset Selection Using Data from Indiana

Autor: George Mohler, Martin B. Short, Frederic Schoenberg, Daniel Sledge
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Statistics and Public Policy, Vol 8, Iss 1, Pp 1-8 (2021)
Druh dokumentu: article
ISSN: 2330-443X
2330443X
DOI: 10.1080/2330443X.2020.1859030
Popis: Dynamic estimation of the reproduction number of COVID-19 is important for assessing the impact of public health measures on virus transmission. State and local decisions about whether to relax or strengthen mitigation measures are being made in part based on whether the reproduction number, Rt, falls below the self-sustaining value of 1. Employing branching point process models and COVID-19 data from Indiana as a case study, we show that estimates of the current value of Rt, and whether it is above or below 1, depend critically on choices about data selection and model specification and estimation. In particular, we find a range of Rt values from 0.47 to 1.20 as we vary the type of estimator and input dataset. We present methods for model comparison and evaluation and then discuss the policy implications of our findings.
Databáze: Directory of Open Access Journals
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