@article{4356405710504828ac2e0ed4760da3f9,
title = "Estimating sars-cov-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys",
abstract = "Establishing how many people have been infected by SARS-CoV-2 remains an urgent priority for controlling the COVID-19 pandemic. Serological tests that identify past infection can be used to estimate cumulative incidence, but the relative accuracy and robustness of various sampling strategies have been unclear. We developed a flexible framework that integrates uncertainty from test characteristics, sample size, and heterogeneity in seroprevalence across subpopulations to compare estimates from sampling schemes. Using the same framework and making the assumption that seropositivity indicates immune protection, we propagated estimates and uncertainty through dynamical models to assess uncertainty in the epidemiological parameters needed to evaluate public health interventions and found that sampling schemes informed by demographics and contact networks outperform uniform sampling. The framework can be adapted to optimize serosurvey design given test characteristics and capacity, population demography, sampling strategy, and modeling approach, and can be tailored to support decision-making around introducing or removing interventions.",
author = "Larremore, {Daniel B.} and Fosdick, {Bailey K.} and Bubar, {Kate M.} and Sam Zhang and Kissler, {Stephen M.} and Metcalf, {C. Jessica E.} and Buckee, {Caroline O.} and Grad, {Yonatan H.}",
note = "Funding Information: The authors thank Nicholas Davies, Laurent H{\'e}bert-Dufresne, Johan Ugander, Arjun Seshadri, and the BioFrontiers Institute IT HPC group. The work was supported in part by the Morris-Singer Fund for the Center for Communicable Disease Dynamics at the Harvard T.H. Chan School of Public Health. DBL and YHG were supported in part by the SeroNet program of the National Cancer Institute (1U01CA261277-01). Reproduction code is open source and provided by the authors at github. com/LarremoreLab/covid_serological_sampling (Larremore, 2021; copy archived at swh:1:rev: 262fb34c19c4bb48bdc74dad1470e4bf8bbe5a69). Funding Information: The authors thank Nicholas Davies, Laurent H?bert-Dufresne, Johan Ugander, Arjun Seshadri, and the BioFrontiers Institute IT HPC group. The work was supported in part by the Morris-Singer Fund for the Center for Communicable Disease Dynamics at the Harvard T.H. Chan School of Public Health. DBL and YHG were supported in part by the SeroNet program of the National Cancer Institute (1U01CA261277-01). Reproduction code is open source and provided by the authors at github. com/LarremoreLab/covid_serological_sampling (Larremore, 2021; copy archived at swh:1:rev: 262fb34c19c4bb48bdc74dad1470e4bf8bbe5a69). Publisher Copyright: {\textcopyright} Larremore et al.",
year = "2021",
month = mar,
doi = "10.7554/eLife.64206",
language = "English (US)",
volume = "10",
journal = "eLife",
issn = "2050-084X",
publisher = "eLife Sciences Publications",
}