An epidemiological forecast model and software assessing interventions on the COVID-19 epidemic in China
Journal of Data Science
; 18(3):409-432, 2020.
Article
in English
| Airiti Library | ID: covidwho-918465
ABSTRACT
We develop a health informatics toolbox that enables timely analysis and evaluation of the time-course dynamics of a range of infectious disease epidemics. As a case study, we examine the novel coronavirus (COVID-19) epidemic using the publicly available data from the China CDC. This toolbox is built upon a hierarchical epidemiological model in which two observed time series of daily proportions of infected and removed cases are generated from the underlying infection dynamics governed by a Markov Susceptible-Infectious-Removed (SIR) infectious disease process. We extend the SIR model to incorporate various types of time-varying quarantine protocols, including government-level 'macro' isolation policies and community-level 'micro' social distancing (e.g. self-isolation and self-quarantine) measures. We develop a calibration procedure for underreported infected cases. This toolbox provides forecasts, in both online and offline forms, as well as simulating the overall dynamics of the epidemic. An R software package is made available for the public, and examples on the use of this software are illustrated. Some possible extensions of our novel epidemiological models are discussed.
Full text:
Available
Collection:
Databases of international organizations
Database:
Airiti Library
Type of study:
Experimental Studies
/
Observational study
Language:
English
Journal:
Journal of Data Science
Year:
2020
Document Type:
Article
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