LloydChapman/COVID_homeless_modelling
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.
Keywords
COVID-19, SARS-CoV-2, Homelessness, Shelters, Infection control, Outbreaks, Symptom-based screening, PCR testing, Universal maskingItem Type | Dataset |
---|---|
Capture method | Simulation |
Date | 17 February 2021 |
Language(s) of written materials | English |
Creator(s) | Chapman, LAC |
LSHTM Faculty/Department | Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology (-2023) |
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 |
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- Github (Online Data Resource)