Selected article for: "infection peak and peak value"
Author: Daniel B Larremore; Bailey K Fosdick; Kate M Bubar; Sam Zhang; Stephen M Kissler; C. Jessica E. Metcalf; Caroline Buckee; Yonatan Grad
Title: Estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys
  • Document date: 2020_4_20
    • ID: c4cs14ja_38
    Snippet: is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.15.20067066 doi: medRxiv preprint (N − 20 − θN, 10, 10, θN ), to simulate a fraction θ of recovered individuals, assumed to be immune. For each sampled value of θ, peak infection height and timing were extracted from forward-integrated timeseries. The model is described fully in Supplementary Materials. Age-structured model. A mod.....
    Document: is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.15.20067066 doi: medRxiv preprint (N − 20 − θN, 10, 10, θN ), to simulate a fraction θ of recovered individuals, assumed to be immune. For each sampled value of θ, peak infection height and timing were extracted from forward-integrated timeseries. The model is described fully in Supplementary Materials. Age-structured model. A model with 16-age-bins (0−4, 5−9, . . . 75−79) was parameterized using country-specific age-contact patterns (8, 9) and COVID-19 parameter estimates (10). The model, due to S13, included age-specific clinical fractions and varying durations of preclinical, clinical, and subclinical infectiousness, as well as a decreased infectiousness for subclinical cases. R eff for age-specific seropositivity estimates θ was calculated as the principal eigenvalue of the serology-adjusted next-generation-matrix,

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