Bayesian SIR model with change points with application to the Omicron wave in Singapore.
Sci Rep
; 12(1): 20864, 2022 Dec 02.
Article
in English
| MEDLINE | ID: covidwho-2151110
ABSTRACT
The Omicron variant has led to a new wave of the COVID-19 pandemic worldwide, with unprecedented numbers of daily confirmed new cases in many countries and areas. To analyze the impact of society or policy changes on the development of the Omicron wave, the stochastic susceptible-infected-removed (SIR) model with change points is proposed to accommodate the situations where the transmission rate and the removal rate may vary significantly at change points. Bayesian inference based on a Markov chain Monte Carlo algorithm is developed to estimate both the locations of change points as well as the transmission rate and removal rate within each stage. Experiments on simulated data reveal the effectiveness of the proposed method, and several stages are detected in analyzing the Omicron wave data in Singapore.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
/
Epidemiological Models
Type of study:
Observational study
/
Prognostic study
Topics:
Variants
Limits:
Humans
Country/Region as subject:
Asia
Language:
English
Journal:
Sci Rep
Year:
2022
Document Type:
Article
Affiliation country:
S41598-022-25473-y
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