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Using time-dependent reproduction number to predict turning points of COVID-19 outbreak in Dalian, Liaoning province, China.
- Source :
- BMC Infectious Diseases; 12/10/2022, Vol. 22 Issue 1, p1-8, 8p
- Publication Year :
- 2022
-
Abstract
- Objectives: To forecast the development trend of current outbreak in Dalian, mainly to predict turning points of COVID-19 outbreak in Dalian, Liaoning province, China, the results can be used to provide a scientific reference for timely adjustment of prevention and control strategies. Methods: During the outbreak, Bayesian framework was used to calculated the time-dependent reproduction number ( R t ), and then above acquired R t and exponential trend equation were used to establish the prediction model, through the model, predict the R t value of following data and know when R t smaller than 1. Results: From July 22 to August 5, 2020, and from March 14 to April 2, 2022, 92 and 632 confirmed cases and asymptomatic infected cases of COVID-19 were reported (324 males and 400 females) in Dalian. The R square for exponential trend equation were 0.982 and 0.980, respectively which fit the R t with illness onset between July 19 to July 28, 2020 and between March 5 to March 17, 2022. According to the result of prediction, under the current strength of prevention and control, the R t of COVID-19 will drop below 1 till August 2, 2020 and March 26, 2022, respectively in Dalian, one day earlier or later than the actual date. That is, the turning point of the COVID-19 outbreak in Dalian, Liaoning province, China will occur on August 2, 2020 and March 26, 2022. Conclusions: Using time-dependent reproduction number values to predict turning points of COVID-19 outbreak in Dalian, Liaoning province, China was effective and reliable on the whole, and the results can be used to establish a sensitive early warning mechanism to guide the timely adjustment of COVID-19 prevention and control strategies. [ABSTRACT FROM AUTHOR]
- Subjects :
- COVID-19 pandemic
PREDICTION models
PROVINCES
COVID-19
FORECASTING
Subjects
Details
- Language :
- English
- ISSN :
- 14712334
- Volume :
- 22
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- BMC Infectious Diseases
- Publication Type :
- Academic Journal
- Accession number :
- 160703809
- Full Text :
- https://doi.org/10.1186/s12879-022-07911-4