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The Evolution of Quarantined and Suspected Cases Determines the Final Trend of the 2019-nCoV Epidemics Based on Multi-Source Data Analyses
26 Pages Posted: 13 Feb 2020
More...Abstract
Background: Since January 23rd 2020, stringent measures for controlling the novel coronavirus epidemics have been gradually enforced and strengthened in mainland China. The detection and diagnosis have been improved as well. However, the daily reported cases staying in a high level make the epidemics trend prediction difficult.
Methods: Since the traditional SEIR model does not evaluate the effectiveness of control strategies, a novel model in line with the current epidemics process and control measures was proposed, utilizing multisource datasets including cumulative number of reported, death, quarantined and suspected cases. Results show that the trend of the epidemics mainly depends on quarantined and suspected cases.
Findings: The predicted cumulative numbers of quarantined and suspected cases nearly reached static states and their inflection points have already been achieved, with the epidemics peak coming soon. The estimated effective reproduction numbers using model-free and model-based methods are decreasing, as well as new infections, while new reported cases are increasing.
Interpretation: Most infected cases have been quarantined or put in suspected class, which has been ignored in existing models. Further, uncertainty analyses reveal that the epidemics is still uncertain and it is important to continue enhancing the quarantine and isolation strategy and improving the detection rate in mainland China.
Funding: This research was funded by the National Natural Science Foundation of China (grant numbers: 11631012 (YX, ST), 61772017 (ST)), and by the Canada Research Chair Program (grant number: 230720 (JW) and the Natural Sciences and Engineering Research Council of Canada (Grant number:105588-2011 (JW).
Declaration of Interest: The authors declare no conflict of interest.
Ethical Approval: Ethical approval was waived since all data utilized are publicly available.
Keywords: coronavirus; multi-source data; mathematical model; SEIR model
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