LloydChapman/COVID_homeless_modelling

Chapman, LAC (2021). LloydChapman/COVID_homeless_modelling. [Dataset]. Github. https://github.com/LloydChapman/COVID_homeless_modelling
Copy

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 masking

No files available. Please consult associated links.


Atom BibTeX OpenURL ContextObject in Span Multiline CSV OpenURL ContextObject Dublin Core (with Type as Type) MPEG-21 DIDL Data Cite XML EndNote HTML Citation JSON METS MODS RDF+N3 RDF+N-Triples RDF+XML Reference Manager Refer Simple Metadata ASCII Citation EP3 XML
Export

Downloads