Bayesian Modeling of COVID-19 Positivity Rate -- the Indiana experience
| dc.contributor.author | Boukai, Ben | |
| dc.contributor.author | Wang, Jiayue | |
| dc.contributor.department | Mathematical Sciences, School of Science | en_US |
| dc.date.accessioned | 2020-08-04T16:16:10Z | |
| dc.date.available | 2020-08-04T16:16:10Z | |
| dc.date.issued | 2020-07-09 | |
| dc.description.abstract | In this short technical report we model, within the Bayesian framework, the rate of positive tests reported by the the State of Indiana, accounting also for the substantial variability (and overdispeartion) in the daily count of the tests performed. The approach we take, results with a simple procedure for prediction, a posteriori, of this rate of ’positivity’ and allows for an easy and a straightforward adaptation by any agency tracking daily results of COVID-19 tests. The numerical results provided herein were obtained via an updatable R Markdown document. | en_US |
| dc.identifier.citation | Boukai, B., & Wang, J. (2020). Bayesian Modeling of COVID-19 Positivity Rate—The Indiana experience. ArXiv. http://arxiv.org/abs/2007.06541 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1805/23526 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | arXiv | en_US |
| dc.relation.journal | arXiv | en_US |
| dc.source | ArXiv | en_US |
| dc.subject | COVID-19 | en_US |
| dc.subject | Testing | en_US |
| dc.subject | Positivity Rate | en_US |
| dc.subject | Bayesian | en_US |
| dc.subject | Indiana | en_US |
| dc.title | Bayesian Modeling of COVID-19 Positivity Rate -- the Indiana experience | en_US |
| dc.type | Preprint | en_US |