We develop a health informatics toolbox that enables public health workers to timely analyze and evaluate the time-course dynamics of the novel coronavirus (COVID-19) infection using the public 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 emitted from the underlying infection dynamics governed by a Markov 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 inspection measures. We develop a calibration procedure for under-reported infected cases. This toolbox provides forecast, in both online and offline forms, of turning points of interest, including the time when daily infected proportion becomes smaller than the previous ones and the time when daily infected proportions becomes smaller than that of daily removed proportion, as well as the ending time of the epidemic. An R software 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.
### Competing Interest Statement
The authors have declared no competing interest.
### Funding Statement
### Author Declarations
All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.
All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.
We used the publicly available data from the China CDC, which can be accessed at DXY.cn.
Song Peter X, Wang Lili, Zhou Yiwang, He Jie, Zhu Bin, Wang Fei, Tang Lu, Eisenberg Marisa. (2020). An epidemiological forecast model and software assessing interventions on COVID-19 epidemic in China. Cold Spring Harbor Laboratory Press