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
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, fal...
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Taylor & Francis Group
2021
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oai:doaj.org-article:76cee0c7054a43d6bbd867b812ca76f52021-11-26T11:19:50ZAnalyzing the Impacts of Public Policy on COVID-19 Transmission: A Case Study of the Role of Model and Dataset Selection Using Data from Indiana2330-443X10.1080/2330443X.2020.1859030https://doaj.org/article/76cee0c7054a43d6bbd867b812ca76f52021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/2330443X.2020.1859030https://doaj.org/toc/2330-443XDynamic 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.George MohlerMartin B. ShortFrederic SchoenbergDaniel SledgeTaylor & Francis Grouparticlebranching point processcovid-19nonparametric estimationreproduction numberPolitical institutions and public administration (General)JF20-2112Probabilities. Mathematical statisticsQA273-280ENStatistics and Public Policy, Vol 8, Iss 1, Pp 1-8 (2021) |
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DOAJ |
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DOAJ |
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EN |
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branching point process covid-19 nonparametric estimation reproduction number Political institutions and public administration (General) JF20-2112 Probabilities. Mathematical statistics QA273-280 |
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branching point process covid-19 nonparametric estimation reproduction number Political institutions and public administration (General) JF20-2112 Probabilities. Mathematical statistics QA273-280 George Mohler Martin B. Short Frederic Schoenberg Daniel Sledge 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 |
| description |
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. |
| format |
article |
| author |
George Mohler Martin B. Short Frederic Schoenberg Daniel Sledge |
| author_facet |
George Mohler Martin B. Short Frederic Schoenberg Daniel Sledge |
| author_sort |
George Mohler |
| title |
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 |
| title_short |
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 |
| title_full |
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 |
| title_fullStr |
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 |
| title_full_unstemmed |
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 |
| title_sort |
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 |
| publisher |
Taylor & Francis Group |
| publishDate |
2021 |
| url |
https://doaj.org/article/76cee0c7054a43d6bbd867b812ca76f5 |
| work_keys_str_mv |
AT georgemohler analyzingtheimpactsofpublicpolicyoncovid19transmissionacasestudyoftheroleofmodelanddatasetselectionusingdatafromindiana AT martinbshort analyzingtheimpactsofpublicpolicyoncovid19transmissionacasestudyoftheroleofmodelanddatasetselectionusingdatafromindiana AT fredericschoenberg analyzingtheimpactsofpublicpolicyoncovid19transmissionacasestudyoftheroleofmodelanddatasetselectionusingdatafromindiana AT danielsledge analyzingtheimpactsofpublicpolicyoncovid19transmissionacasestudyoftheroleofmodelanddatasetselectionusingdatafromindiana |
| _version_ |
1718409480269463552 |