New Results
Jointly modeling prevalence, sensitivity and specificity for optimal sample allocation
View ORCID ProfileDaniel B. Larremore, View ORCID ProfileBailey K. Fosdick, View ORCID ProfileSam Zhang, View ORCID ProfileYonatan H. Grad
doi: https://doi.org/10.1101/2020.05.23.112649
Daniel B. Larremore
1Department of Computer Science, University of Colorado Boulder, Boulder, CO, 80309, USA
2BioFrontiers Institute, University of Colorado at Boulder, Boulder, CO, 80303, USA
Bailey K. Fosdick
3Department of Statistics, Colorado State University, Fort Collins, CO, 80523, USA
Sam Zhang
4Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, 80309, USA
Yonatan H. Grad
5Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA

Article usage
Posted May 26, 2020.
Jointly modeling prevalence, sensitivity and specificity for optimal sample allocation
Daniel B. Larremore, Bailey K. Fosdick, Sam Zhang, Yonatan H. Grad
bioRxiv 2020.05.23.112649; doi: https://doi.org/10.1101/2020.05.23.112649
Subject Area
Subject Areas
- Biochemistry
- Biochemistry (14174)
- Bioengineering (10826)
- Bioinformatics (34313)
- Biophysics (17655)
- Cancer Biology (14757)
- Cell Biology (20784)
- Clinical Trials (138)
- Developmental Biology (11182)
- Ecology (16502)
- Epidemiology (2067)
- Evolutionary Biology (20812)
- Genetics (13677)
- Genomics (19100)
- Immunology (14246)
- Microbiology (33158)
- Molecular Biology (13833)
- Neuroscience (72410)
- Paleontology (542)
- Pathology (2278)
- Pharmacology and Toxicology (3860)
- Physiology (6102)
- Plant Biology (12390)
- Synthetic Biology (3460)
- Systems Biology (8371)
- Zoology (1913)