Author: Nicholas G Davies; Petra Klepac; Yang Liu; Kiesha Prem; Mark Jit; Rosalind M Eggo
Title: Age-dependent effects in the transmission and control of COVID-19 epidemics Document date: 2020_3_27
ID: 8f76vhyz_81
Snippet: (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03. 24.20043018 doi: medRxiv preprint / the total population of each capital city from the R package maps . For each city, we scaled u i to result in an average R 0 = 2 in Birmingham, UK, and used the same setting for u i for all cities, so that the realised R 0 would change according to the contact matrices and demographics for each city. We simul.....
Document: (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03. 24.20043018 doi: medRxiv preprint / the total population of each capital city from the R package maps . For each city, we scaled u i to result in an average R 0 = 2 in Birmingham, UK, and used the same setting for u i for all cities, so that the realised R 0 would change according to the contact matrices and demographics for each city. We simulated 20 outbreaks in each city, drawing the age-specific clinical fraction y i from the posterior of the estimated overall clinical fraction from our line list analysis (Fig. 2) , and analysed the time to the peak incidence of the epidemic, the peak clinical and subclinical incidence of infection, and the total number of clinical and subclinical infections. We took the first third and the last third of clinical cases in each city to compare the early and late stages of the epidemic.
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