This repository contains the code required to reproduce the results from the study Chan et al. 2024. The original code, developed by Hart et al. 2022, was translated from MATLAB to R.
For this demonstration, we are not sharing the US household data. Instead, we used the UK household data from the original study. This demonstration was conducted using 1,000 iterations for simplification.
In the folder R/Results, we have uploaded the posterior distributions generated from 1,000,000 iterations, as presented using the US household data.
R/main.R contains the functions to estimate generation time across all data stratifications in parallel, while R/main_scenario.R demonstrates estimation for each data stratification. The estimation process consists of parameter assumption, Bayesian data augmentation Markov Chain Monte Carlo (MCMC), and plotting posterior distribution.
The household data are available upon reasonable request and upon completion of required approvals. The R code for estimating the generation time is available at https://github.com/CDCgov/influenza-generation_time-us.
The conclusions, findings, and opinions expressed by authors contributing to this article do not necessarily reflect the official position of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions.
Louis Yat Hin Chan, PhD, MSc
CDC Steven M. Teutsch Prevention Effectiveness (PE) Fellow – Analytics and Modeling Track, Class of 2023
Applied Research and Modeling (ARM) Team
Epidemiology and Prevention Branch (EPB)
Influenza division (ID)
National Center for Immunization and Respiratory Diseases (NCIRD)
Centers for Disease Control and Prevention (CDC)
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