Space-time covid-19 Bayesian SIR modeling in South Carolina

The Covid-19 pandemic has spread across the world since the beginning of 2020. Many regions have experienced its effects. The state of South Carolina in the USA has seen cases since early March 2020 and a primary peak in early April 2020. A lockdown was imposed on April 6th but lifting of restrictio...

Full description

Saved in:
Bibliographic Details
Published inPloS one Vol. 16; no. 3; p. e0242777
Main Authors Lawson, Andrew B, Kim, Joanne
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 17.03.2021
Public Library of Science (PLoS)
Subjects
Asymptomatic
Asymptomatic Infections - epidemiology
Bayes Theorem
Bayesian analysis
Bayesian statistical decision theory
Bioinformatics
Computer programs
Coronaviruses
COVID-19
COVID-19 - epidemiology
Data models
Disease transmission
Distribution
Drafting software
Earth Sciences
Editing
Epidemic models
Epidemics
Fatalities
Health aspects
Health risks
Health sciences
Humans
Infections
Influenza
Mathematical models
Medicine and Health Sciences
Pandemics
Pandemics - prevention & control
People and places
Physical Distancing
Physical Sciences
Population
Public health
Quarantine - methods
Research and Analysis Methods
SARS-CoV-2 - pathogenicity
Social aspects
Social Sciences
Software
South Carolina
South Carolina - epidemiology
Spacetime
Time series
Visualization
South Carolina
United States > US
Online AccessGet full text

Cover

More Information
Summary:The Covid-19 pandemic has spread across the world since the beginning of 2020. Many regions have experienced its effects. The state of South Carolina in the USA has seen cases since early March 2020 and a primary peak in early April 2020. A lockdown was imposed on April 6th but lifting of restrictions started on April 24th. The daily case and death data as reported by NCHS (deaths) via the New York Times GitHUB repository have been analyzed and approaches to modeling of the data are presented. Prediction is also considered and the role of asymptomatic transmission is assessed as a latent unobserved effect. Two different time periods are examined and one step prediction is provided. The results suggest that both socio-economic disadvantage, asymptomatic transmission and spatial confounding are important ingredients in any model pertaining to county level case dynamics.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Competing Interests: The authors received no specific funding for this work and have no conflicts of interest.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0242777