Author: Anthony, Simon J.; Epstein, Jonathan H.; Murray, Kris A.; Navarrete-Macias, Isamara; Zambrana-Torrelio, Carlos M.; Solovyov, Alexander; Ojeda-Flores, Rafael; Arrigo, Nicole C.; Islam, Ariful; Ali Khan, Shahneaz; Hosseini, Parviez; Bogich, Tiffany L.; Olival, Kevin J.; Sanchez-Leon, Maria D.; Karesh, William B.; Goldstein, Tracey; Luby, Stephen P.; Morse, Stephen S.; Mazet, Jonna A. K.; Daszak, Peter; Lipkin, W. Ian
Title: A Strategy To Estimate Unknown Viral Diversity in Mammals Document date: 2013_9_3
ID: 6lobyyj4_21
Snippet: Virus richness and sample estimation. We implemented models from the biodiversity literature that utilize incidence distributions to estimate virus richness (number of unique viruses) and, hence, to estimate the number of undetected viruses in the assemblage (60, 61) . Incidence data result where each virus detected in the assemblage is noted in each sample as either present (verified detection) or absent (not detected, which could result due to .....
Document: Virus richness and sample estimation. We implemented models from the biodiversity literature that utilize incidence distributions to estimate virus richness (number of unique viruses) and, hence, to estimate the number of undetected viruses in the assemblage (60, 61) . Incidence data result where each virus detected in the assemblage is noted in each sample as either present (verified detection) or absent (not detected, which could result due to the virus being absent or being present but not detected by the test, i.e., false absence).
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