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Forecasting the development of the COVID-19 epidemic by nowcasting: when did things start to get better?
Quantitative Biology ; 9(1):93-99, 2021.
Article in English | ProQuest Central | ID: covidwho-1876233
ABSTRACT

Background:

Now the coronavirus disease 2019 (COVID-19) epidemic becomes a global phenomenon and its development concerns billions of peoples’ lives. The development of the COVID-19 epidemic in China could be used as a reference for the other countries’ control strategy.

Methods:

We used a classical susceptible-infected-recovered (SIR) model to forecast the development of the COVID-19 epidemic in China by nowcasting. The linear regression analyses were employed to predict the COVID-19 epidemic’s inflexion point. Finally, we used a susceptible-exposed-infected-recovered (SEIR) model to simulate the development of the COVID-19 epidemic in China throughout 2020.

Results:

Our nowcasts show that the COVID-19 transmission rate started to slow down on January 30. The linear regression analyses further show that the inflexion point of this epidemic would arrive between February 17 and 18. The final SEIR model simulation forecasted that the COVID-19 epidemic would probably infect about 82,000 people and last throughout 2020 in China. We also applied our method to USA’s and global COVID-19 data and the nowcasts show that the development of COVID-19 pandemic is not optimistic in the rest of 2020.

Conclusion:

The COVID-19 epidemic’s scale in China is much smaller than the previous estimations. After implemented strict control and prevention measures, such as city lockdown, it took a week to slow down the COVID-19 transmission and about four weeks to really mitigate the COVID-19 prevalence in China.
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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Language: English Journal: Quantitative Biology Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Language: English Journal: Quantitative Biology Year: 2021 Document Type: Article