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Background: The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. In the early stage of the outbreak, the most important question concerns some meaningful milepost moments, including the time when the number of daily confirmed cases decreases, the time when the number of daily confirmed cases becomes smaller than that of the daily removed (recovered and death), and the time when the number of daily confirmed cases and patients treated in hospital, which can be called “active cases”, becomes zero. Unfortunately, it is extremely difficult to make right and precise prediction due to the limited amount of available data at the early stage of the outbreak. To address it, in this paper, we propose a flexible framework incorporating the effectiveness of the government control to forecast the whole process of a new unknown infectious disease in its early-outbreak. Methods: We first establish the iconic indicators to characterize the extent of epidemic spread. Then we develop the tracking and forecasting procedure with mild and reasonable assumptions. Finally we apply it to analyze and evaluate the COVID-19 outbreak using the public available data for mainland China beyond Hubei Province from the China Centers for Disease Control (CDC) during the period of Jan 29th, 2020, to Feb 29th, 2020, which shows the effectiveness of the proposed procedure. Results: Forecasting results indicate that the number of newly confirmed cases will become zero in the mid-early March, and the number of patients treated in the hospital will become zero between mid-March and mid-April in mainland China beyond Hubei Province. Conclusions: The framework proposed in this paper can help people get a general understanding of the epidemic trends in countries where COVID-19 are raging as well as any other outbreaks of new and unknown infectious diseases in the future.
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10.12688/f1000research.23107.2
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document_parses/pdf_json/b52c53c6065fec7d77d25723073cc27dcaff6f54.json
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document_parses/pmc_json/PMC7737706.xml.json
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Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19
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