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Sample size calculations for pathogen variant surveillance in the presence of biological and systematic biases.
Wohl, Shirlee; Lee, Elizabeth C; DiPrete, Bethany L; Lessler, Justin.
  • Wohl S; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA. Electronic address: swohl@scripps.edu.
  • Lee EC; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • DiPrete BL; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Lessler J; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; The Carolina Population Center, University of North Carolina at Cha
Cell Rep Med ; 4(5): 101022, 2023 05 16.
Article in English | MEDLINE | ID: covidwho-2306995
ABSTRACT
Tracking the emergence and spread of pathogen variants is an important component of monitoring infectious disease outbreaks. To that end, accurately estimating the number and prevalence of pathogen variants in a population requires carefully designed surveillance programs. However, current approaches to calculating the number of pathogen samples needed for effective surveillance often do not account for the various processes that can bias which infections are detected and which samples are ultimately characterized as a specific variant. In this article, we introduce a framework that accounts for the logistical and epidemiological processes that may bias variant characterization, and we demonstrate how to use this framework (implemented in a publicly available tool) to calculate the number of sequences needed for surveillance. Our framework is designed to be easy to use while also flexible enough to be adapted to various pathogens and surveillance scenarios.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Disease Outbreaks Type of study: Observational study / Prognostic study / Systematic review/Meta Analysis Topics: Variants Language: English Journal: Cell Rep Med Year: 2023 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Disease Outbreaks Type of study: Observational study / Prognostic study / Systematic review/Meta Analysis Topics: Variants Language: English Journal: Cell Rep Med Year: 2023 Document Type: Article