Residential College | false |
Status | 已發表Published |
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 Publication | Vaccines |
ISSN | 2076-393X |
Volume | 11Issue: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. |
Keyword | Covid-19 Data-driven Modeling Analysis Flatten-the-curve Policy |
DOI | 10.3390/vaccines11051009 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Immunology ; Research & Experimental Medicine |
WOS Subject | Immunology ; Medicine, Research & Experimental |
WOS ID | WOS:000997487800001 |
Publisher | MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND |
Scopus ID | 2-s2.0-85160306237 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau Faculty of Health Sciences DEPARTMENT OF MATHEMATICS DEPARTMENT OF BIOMEDICAL SCIENCES |
Co-First Author | Sun, Jiaqi |
Corresponding Author | Shao, Ning Yi; Liu, Miao |
Affiliation | 1.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 Affilication | Faculty of Science and Technology |
Corresponding Author Affilication | Faculty 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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