Data for Bayesian sequential approach to monitor COVID-19 variants through positivity rate from wastewater
Abstract/Contents
- Abstract
- Date used for work described in paper Bayesian sequential approach to monitor COVID-19 variants through positivity rate from wastewater
Description
| Type of resource | Dataset, text |
|---|---|
| Date created | [ca. January 2023] |
| Date modified | February 3, 2023 |
| Publication date | February 3, 2023 |
Creators/Contributors
| Author | Boehm, Alexandria |
|---|---|
| Author | White, Bradley |
| Author | Wolfe, Marlene |
| Author | Bischel, Heather |
Subjects
| Subject | SARS-CoV-2 |
|---|---|
| Subject | COVID-19 (Disease) |
| Subject | Wastewater-based epidemiology |
| Genre | Data |
| Genre | Tabular data |
| Genre | Data sets |
| Genre | Dataset |
| Genre | Tables (data) |
Bibliographic information
| Related item |
|
|---|---|
| DOI | https://doi.org/10.25740/nx203xc3559 |
| Location | https://purl.stanford.edu/nx203xc3559 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).
Preferred citation
- Preferred citation
- Boehm, A., White, B., Wolfe, M., and Bischel, H. (2023). Data for Bayesian sequential approach to monitor COVID-19 variants through positivity rate from wastewater. Stanford Digital Repository. Available at https://purl.stanford.edu/nx203xc3559. https://doi.org/10.25740/nx203xc3559.
Collection
Boehm Research Group at Stanford
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