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Flattening the Curve after the Initial Outbreak of Coronavirus Disease 2019: A Data-Driven Modeling Analysis for the Omicron Pandemic in China
Sun, Jiaqi1; Li, Yusi2; Xiao, Lin Fan2; Shao, Ning Yi2,3; Liu, Miao4
2023-05-22
Source PublicationVaccines
ISSN2076-393X
Volume11Issue:5Pages:1009
Abstract

China is relaxing COVID-19 measures from the “dynamic zero tolerance” (DZT) level. The “flatten-the-curve” (FTC) strategy, which decreases and maintains the low rate of infection to avoid overwhelming the healthcare system by adopting relaxed nonpharmaceutical interventions (NPIs) after the outbreak, has been perceived as the most appropriate and effective method in preventing the spread of the Omicron variant. Hence, we established an improved data-driven model of Omicron transmission based on the age-structured stochastic compartmental susceptible-latent-infectious-removed-susceptible model constructed by Cai to deduce the overall prevention effect throughout China. At the current level of immunity without the application of any NPIs, more than 1.27 billion (including asymptomatic individuals) were infected within 90 days. Moreover, the Omicron outbreak would result in 1.49 million deaths within 180 days. The application of FTC could decrease the number of deaths by 36.91% within 360 days. The strict implementation of FTC policy combined with completed vaccination and drug use, which only resulted in 0.19 million deaths in an age-stratified model, will help end the pandemic within about 240 days. The pandemic would be successfully controlled within a shorter period of time without a high fatality rate; therefore, the FTC policy could be strictly implemented through enhancement of immunity and drug use.

KeywordCovid-19 Data-driven Modeling Analysis Flatten-the-curve Policy
DOI10.3390/vaccines11051009
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaImmunology ; Research & Experimental Medicine
WOS SubjectImmunology ; Medicine, Research & Experimental
WOS IDWOS:000997487800001
PublisherMDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
Scopus ID2-s2.0-85160306237
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionMinistry of Education Frontiers Science Center for Precision Oncology, University of Macau
Faculty of Health Sciences
DEPARTMENT OF MATHEMATICS
DEPARTMENT OF BIOMEDICAL SCIENCES
Co-First AuthorSun, Jiaqi
Corresponding AuthorShao, Ning Yi; Liu, Miao
Affiliation1.Department of Mathematics, Faculty of Science and Technology, University of Macau, Taipa, Macao
2.Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Taipa, Macao
3.MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macao
4.Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, 02115, United States
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Health Sciences;  University of Macau
Recommended Citation
GB/T 7714
Sun, Jiaqi,Li, Yusi,Xiao, Lin Fan,et al. Flattening the Curve after the Initial Outbreak of Coronavirus Disease 2019: A Data-Driven Modeling Analysis for the Omicron Pandemic in China[J]. Vaccines, 2023, 11(5), 1009.
APA Sun, Jiaqi., Li, Yusi., Xiao, Lin Fan., Shao, Ning Yi., & Liu, Miao (2023). Flattening the Curve after the Initial Outbreak of Coronavirus Disease 2019: A Data-Driven Modeling Analysis for the Omicron Pandemic in China. Vaccines, 11(5), 1009.
MLA Sun, Jiaqi,et al."Flattening the Curve after the Initial Outbreak of Coronavirus Disease 2019: A Data-Driven Modeling Analysis for the Omicron Pandemic in China".Vaccines 11.5(2023):1009.
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