Abstract
We develop a health informatics toolbox that enables timely analysis and evaluation of the timecourse 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.
Highlights
The outbreak of the coronavirus disease 2019 or COVID-19, originated in Wuhan, the capital city of Hubei province
Since the outbreak of the epidemic, many clinical papers (Jung et al, 2020; Chen et al, 2020; Xiang et al, 2020; Xu et al, 2020; Imai et al, 2020; Gralinski and Menachery, 2020; Luk et al, 2019; Fan et al, 2019; Hui et al, 2020; Holshue et al, 2020; Guan et al, 2020; Rothe et al, 2020; Huang et al, 2020; Zhu et al, 2020; Wang et al, 2020a) have been published to uncover limited but important knowledge of COVID-19, including that (i) COVID-19 is an infectious disease caused by SARS-CoV-2, a virus closely related to the SARS coronavirus (SARS-CoV)
We develop an epidemiological forecast model with an R software package to assess effects of interventions on the COVID-19 epidemic within Hubei and outside Hubei in China
Summary
The outbreak of the coronavirus disease 2019 or COVID-19, originated in Wuhan, the capital city of Hubei province. Our informatics toolbox is built upon a state-space model (Zhu et al, 2012; Jørgensen et al, 1999; Song, 2000; Jøsrgensen and Song, 2007) shown in Figure 1 with an extended Markov SIR model (Kermack and McKendrick, 1927), which has the following key features: (i) Our model is specified with the temporally varying probabilities of susceptible, infected and removed (recovered and death) compartments, not directly on time series of respective counts; (ii) estimation and inference are carried out and implemented using Markov Chain Monte Carlo (MCMC); (iii) it outputs various sample draws from the posteriors of the model parameters (e.g. transmission and removal rates) and the underlying probabilities of susceptible, infected and removed compartments, as well as their credible intervals The latter is of extreme importance to quantify prediction uncertainty.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
