Chapman, LAC. 2021. LloydChapman/COVID_homeless_modelling. [Online]. Github. Available from: https://github.com/LloydChapman/COVID_homeless_modelling
Chapman, LAC. LloydChapman/COVID_homeless_modelling [Internet]. Github; 2021. Available from: https://github.com/LloydChapman/COVID_homeless_modelling
Chapman, LAC (2021). LloydChapman/COVID_homeless_modelling. [Data Collection]. Github. https://github.com/LloydChapman/COVID_homeless_modelling
Description
This repository contains Approximate Bayesian Computation (ABC) code and simulation code for the analyses in 'Comparison of infection control strategies to reduce COVID-19 outbreaks in homeless shelters in the United States: a simulation study' [1]. The code implements a discrete-time stochastic SEIR simulation model of COVID-19 transmission in a closed environment (here a homeless shelter) with importation of infection from the local community. The model is fitted to data on numbers of PCR-positive and negative individuals from outbreaks in 5 homeless shelters in San Francisco, Boston and Seattle, and used to predict the impact of different intervention strategies on the probability of averting an outbreak over 30 days in a representative homeless shelter into which a single latently infected individual is introduced.
| Data capture method | Simulation |
|---|---|
| Date (Date published in a 3rd party system) | 17 February 2021 |
| Language(s) of written materials | English |
| Data Creators | Chapman, LAC |
|---|---|
| LSHTM Faculty/Department | Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology |
| Participating Institutions | London School of Hygiene & Tropical Medicine, London, United Kingdom |
| Date Deposited | 23 Jul 2021 10:11 |
|---|---|
| Last Modified | 23 Jul 2021 10:11 |
| Publisher | Github |
