Semiparametric Bayesian inference for the transmission dynamics of COVID-19 with a state-space model.
Contemp Clin Trials
; 97: 106146, 2020 10.
Article
in English
| MEDLINE | ID: covidwho-758647
ABSTRACT
The outbreak of Coronavirus Disease 2019 (COVID-19) is an ongoing pandemic affecting over 200 countries and regions. Inference about the transmission dynamics of COVID-19 can provide important insights into the speed of disease spread and the effects of mitigation policies. We develop a novel Bayesian approach to such inference based on a probabilistic compartmental model using data of daily confirmed COVID-19 cases. In particular, we consider a probabilistic extension of the classical susceptible-infectious-recovered model, which takes into account undocumented infections and allows the epidemiological parameters to vary over time. We estimate the disease transmission rate via a Gaussian process prior, which captures nonlinear changes over time without the need of specific parametric assumptions. We utilize a parallel-tempering Markov chain Monte Carlo algorithm to efficiently sample from the highly correlated posterior space. Predictions for future observations are done by sampling from their posterior predictive distributions. Performance of the proposed approach is assessed using simulated datasets. Finally, our approach is applied to COVID-19 data from six states of the United States Washington, New York, California, Florida, Texas, and Illinois. An R package BaySIR is made available at https//github.com/tianjianzhou/BaySIR for the public to conduct independent analysis or reproduce the results in this paper.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Communicable Disease Control
/
Models, Statistical
/
Basic Reproduction Number
/
COVID-19
Type of study:
Observational study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
North America
Language:
English
Journal:
Contemp Clin Trials
Journal subject:
Medicine
/
Therapeutics
Year:
2020
Document Type:
Article
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