Prediction and analysis of Coronavirus Disease 2019
Lin Jia, Kewen Li, Yu Jiang, Xin Guo, Ting zhao

TL;DR
This paper uses mathematical models to analyze and predict the COVID-19 epidemic's trend and scale in China and other regions, providing estimates for total cases, deaths, and epidemic duration.
Contribution
It applies and compares Logistic, Bertalanffy, and Gompertz models to fit COVID-19 data, validating models with SARS data and offering epidemic forecasts.
Findings
Logistic model fits COVID-19 data best among the three models.
Predicted total infections in China range from 80,261 to 85,140.
COVID-19 is expected to end in Wuhan by late April 2020.
Abstract
In December 2019, a novel coronavirus was found in a seafood wholesale market in Wuhan, China. WHO officially named this coronavirus as COVID-19. Since the first patient was hospitalized on December 12, 2019, China has reported a total of 78,824 confirmed CONID-19 cases and 2,788 deaths as of February 28, 2020. Wuhan's cumulative confirmed cases and deaths accounted for 61.1% and 76.5% of the whole China mainland , making it the priority center for epidemic prevention and control. Meanwhile, 51 countries and regions outside China have reported 4,879 confirmed cases and 79 deaths as of February 28, 2020. COVID-19 epidemic does great harm to people's daily life and country's economic development. This paper adopts three kinds of mathematical models, i.e., Logistic model, Bertalanffy model and Gompertz model. The epidemic trends of SARS were first fitted and analyzed in order to prove the…
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Taxonomy
TopicsData-Driven Disease Surveillance · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts
