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Ma Y, Xu S, Luo Y, Peng J, Guo J, Dong A, Xu Z, Li J, Lei L, He L, Wang T, Yu H, Xie J. Predicting the transmission dynamics of novel coronavirus infection in Shanxi province after the implementation of the "Class B infectious disease Class B management" policy. Front Public Health 2023; 11:1322430. [PMID: 38186702 PMCID: PMC10768892 DOI: 10.3389/fpubh.2023.1322430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 11/30/2023] [Indexed: 01/09/2024] Open
Abstract
Background China managed coronavirus disease 2019 (COVID-19) with measures against Class B infectious diseases, instead of Class A infectious diseases, in a major shift of its epidemic response policies. We aimed to generate robust information on the transmission dynamics of novel coronavirus infection in Shanxi, a province located in northern China, after the implementation of the "Class B infectious disease Class B management" policy. Methods We consolidated infection data in Shanxi province from December 6, 2022 to January 14, 2023 through a network questionnaire survey and sentinel surveillance. A dynamics model of the SEIQHCVR was developed to track the infection curves and effective reproduction number (R t ). Results Our model was effective in estimating the trends of novel coronavirus infection, with the coefficient of determination (R 2 ) above 90% in infections, inpatients, and critically ill patients. The number of infections in Shanxi province as well as in urban and rural areas peaked on December 20, 2022, with the peak of inpatients and critically ill patients occurring 2 to 3 weeks after the peak of infections. By the end of January 2023, 87.72% of the Shanxi residents were predicted to be infected, and the outbreak subsequently subsided. A small wave of COVID-19 infections may re-emerge at the end of April. In less than a month, the R t values of positive infections, inpatients and critically ill patients were all below 1.0. Conclusion The outbreak in Shanxi province is currently at a low prevalence level. In the face of possible future waves of infection, there is a strong need to strengthen surveillance and early warning.
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Affiliation(s)
- Yifei Ma
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Shujun Xu
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yuxin Luo
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Junlin Peng
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Jiaming Guo
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Ali Dong
- Shanxi Center for Disease Control and Prevention, Taiyuan, China
| | - Zhibin Xu
- Shanxi Center for Disease Control and Prevention, Taiyuan, China
| | - Jiantao Li
- School of Management, Shanxi Medical University, Taiyuan, China
| | - Lijian Lei
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Lu He
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Tong Wang
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Hongmei Yu
- School of Public Health, Shanxi Medical University, Taiyuan, China
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China
| | - Jun Xie
- Department of Biochemistry and Molecular Biology, Shanxi Key Laboratory of Birth Defect and Cell Regeneration, MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University, Taiyuan, China
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Ma Y, Xu S, Luo Y, Li J, Lei L, He L, Wang T, Yu H, Xie J. Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China. BMC Public Health 2023; 23:2400. [PMID: 38042794 PMCID: PMC10693062 DOI: 10.1186/s12889-023-17327-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/24/2023] [Indexed: 12/04/2023] Open
Abstract
BACKGROUND In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions. METHODS Given the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses. RESULTS The best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to Rt curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and Rt below 1.0, as well as reducing the number of peak cases and final affected population. CONCLUSIONS The LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus.
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Affiliation(s)
- Yifei Ma
- School of Public Health, Shanxi Medical University, Taiyuan, 030001, China
| | - Shujun Xu
- School of Public Health, Shanxi Medical University, Taiyuan, 030001, China
| | - Yuxin Luo
- School of Public Health, Shanxi Medical University, Taiyuan, 030001, China
| | - Jiantao Li
- School of Management, Shanxi Medical University, Taiyuan, 030001, China
| | - Lijian Lei
- School of Public Health, Shanxi Medical University, Taiyuan, 030001, China
| | - Lu He
- School of Public Health, Shanxi Medical University, Taiyuan, 030001, China
| | - Tong Wang
- School of Public Health, Shanxi Medical University, Taiyuan, 030001, China
| | - Hongmei Yu
- School of Public Health, Shanxi Medical University, Taiyuan, 030001, China.
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, 030001, China.
| | - Jun Xie
- Center of Reverse Microbial Etiology, Shanxi Medical University, Taiyuan, 030001, China.
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Thakkar K, Spinardi JR, Yang J, Kyaw MH, Ozbilgili E, Mendoza CF, Oh HML. Impact of vaccination and non-pharmacological interventions on COVID-19: a review of simulation modeling studies in Asia. Front Public Health 2023; 11:1252719. [PMID: 37818298 PMCID: PMC10560858 DOI: 10.3389/fpubh.2023.1252719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 09/07/2023] [Indexed: 10/12/2023] Open
Abstract
Introduction Epidemiological modeling is widely used to offer insights into the COVID-19 pandemic situation in Asia. We reviewed published computational (mathematical/simulation) models conducted in Asia that assessed impacts of pharmacological and non-pharmacological interventions against COVID-19 and their implications for vaccination strategy. Methods A search of the PubMed database for peer-reviewed, published, and accessible articles in English was performed up to November 2022 to capture studies in Asian populations based on computational modeling of outcomes in the COVID-19 pandemic. Extracted data included model type (mechanistic compartmental/agent-based, statistical, both), intervention type (pharmacological, non-pharmacological), and procedures for parameterizing age. Findings are summarized with descriptive statistics and discussed in terms of the evolving COVID-19 situation. Results The literature search identified 378 results, of which 59 met criteria for data extraction. China, Japan, and South Korea accounted for approximately half of studies, with fewer from South and South-East Asia. Mechanistic models were most common, either compartmental (61.0%), agent-based (1.7%), or combination (18.6%) models. Statistical modeling was applied less frequently (11.9%). Pharmacological interventions were examined in 59.3% of studies, and most considered vaccination, except one study of an antiviral treatment. Non-pharmacological interventions were also considered in 84.7% of studies. Infection, hospitalization, and mortality were outcomes in 91.5%, 30.5%, and 30.5% of studies, respectively. Approximately a third of studies accounted for age, including 10 that also examined mortality. Four of these studies emphasized benefits in terms of mortality from prioritizing older adults for vaccination under conditions of a limited supply; however, one study noted potential benefits to infection rates from early vaccination of younger adults. Few studies (5.1%) considered the impact of vaccination among children. Conclusion Early in the COVID-19 pandemic, non-pharmacological interventions helped to mitigate the health burden of COVID-19; however, modeling indicates that high population coverage of effective vaccines will complement and reduce reliance on such interventions. Thus, increasing and maintaining immunity levels in populations through regular booster shots, particularly among at-risk and vulnerable groups, including older adults, might help to protect public health. Future modeling efforts should consider new vaccines and alternative therapies alongside an evolving virus in populations with varied vaccination histories.
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Affiliation(s)
- Karan Thakkar
- Vaccine Medical Affairs, Emerging Markets, Pfizer Inc., Singapore, Singapore
| | | | - Jingyan Yang
- Vaccine Global Value and Access, Pfizer Inc., New York, NY, United States
| | - Moe H. Kyaw
- Vaccine Medical Affairs, Emerging Markets, Pfizer Inc., Reston, VA, United States
| | - Egemen Ozbilgili
- Asia Cluster Medical Affairs, Emerging Markets, Pfizer Inc., Singapore, Singapore
| | | | - Helen May Lin Oh
- Department of Infectious Diseases, Changi General Hospital, Singapore, Singapore
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Ma Y, Xu S, Luo Y, Qin Y, Li J, Lei L, He L, Wang T, Yu H, Xie J. Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis. Front Public Health 2023; 11:1175869. [PMID: 37415698 PMCID: PMC10321150 DOI: 10.3389/fpubh.2023.1175869] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/01/2023] [Indexed: 07/08/2023] Open
Abstract
Background On September 28, 2022, the first case of Omicron subvariant BF.7 was discovered among coronavirus disease 2019 (COVID-19) infections in Hohhot, China, and then the epidemic broke out on a large scale during the National Day holiday. It is imminently necessary to construct a mathematical model to investigate the transmission dynamics of COVID-19 in Hohhot. Methods In this study, we first investigated the epidemiological characteristics of COVID-19 cases in Hohhot, including the spatiotemporal distribution and sociodemographic distribution. Then, we proposed a time-varying Susceptible-Quarantined Susceptible-Exposed-Quarantined Exposed-Infected-Asymptomatic-Hospitalized-Removed (SQEIAHR) model to derive the epidemic curves. The next-generation matrix method was used to calculate the effective reproduction number (Re). Finally, we explored the effects of higher stringency measures on the development of the epidemic through scenario analysis. Results Of the 4,889 positive infected cases, the vast majority were asymptomatic and mild, mainly concentrated in central areas such as Xincheng District. People in the 30-59 age group primarily were affected by the current outbreak, accounting for 53.74%, but females and males were almost equally affected (1.03:1). Community screening (35.70%) and centralized isolation screening (26.28%) were the main ways to identify positive infected cases. Our model predicted the peak of the epidemic on October 6, 2022, the dynamic zero-COVID date on October 15, 2022, a number of peak cases of 629, and a cumulative number of infections of 4,963 (95% confidential interval (95%CI): 4,692 ~ 5,267), all four of which were highly consistent with the actual situation in Hohhot. Early in the outbreak, the basic reproduction number (R0) was approximately 7.01 (95%CI: 6.93 ~ 7.09), and then Re declined sharply to below 1.0 on October 6, 2022. Scenario analysis of higher stringency measures showed the importance of decreasing the transmission rate and increasing the quarantine rate to shorten the time to peak, dynamic zero-COVID and an Re below 1.0, as well as to reduce the number of peak cases and final affected population. Conclusion Our model was effective in predicting the epidemic trends of COVID-19, and the implementation of a more stringent combination of measures was indispensable in containing the spread of the virus.
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Affiliation(s)
- Yifei Ma
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Shujun Xu
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yuxin Luo
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yao Qin
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Jiantao Li
- School of Management, Shanxi Medical University, Taiyuan, China
| | - Lijian Lei
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Lu He
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Tong Wang
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Hongmei Yu
- School of Public Health, Shanxi Medical University, Taiyuan, China
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China
| | - Jun Xie
- Center of Reverse Microbial Etiology, Shanxi Medical University, Taiyuan, China
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Abudunaibi B, Liu W, Guo Z, Zhao Z, Rui J, Song W, Wang Y, Chen Q, Frutos R, Su C, Chen T. A comparative study on the three calculation methods for reproduction numbers of COVID-19. Front Med (Lausanne) 2023; 9:1079842. [PMID: 36687425 PMCID: PMC9849755 DOI: 10.3389/fmed.2022.1079842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/05/2022] [Indexed: 01/06/2023] Open
Abstract
Objective This study uses four COVID-19 outbreaks as examples to calculate and compare merits and demerits, as well as applicational scenarios, of three methods for calculating reproduction numbers. Method The epidemiological characteristics of the COVID-19 outbreaks are described. Through the definition method, the next-generation matrix-based method, and the epidemic curve and serial interval (SI)-based method, corresponding reproduction numbers were obtained and compared. Results Reproduction numbers (R eff ), obtained by the definition method of the four regions, are 1.20, 1.14, 1.66, and 1.12. Through the next generation matrix method, in region H R eff = 4.30, 0.44; region P R eff = 6.5, 1.39, 0; region X R eff = 6.82, 1.39, 0; and region Z R eff = 2.99, 0.65. Time-varying reproduction numbers (R t ), which are attained by SI of onset dates, are decreasing with time. Region H reached its highest R t = 2.8 on July 29 and decreased to R t < 1 after August 4; region P reached its highest R t = 5.8 on September 9 and dropped to R t < 1 by September 14; region X had a fluctuation in the R t and R t < 1 after September 22; R t in region Z reached a maximum of 1.8 on September 15 and decreased continuously to R t < 1 on September 19. Conclusion The reproduction number obtained by the definition method is optimal in the early stage of epidemics with a small number of cases that have clear transmission chains to predict the trend of epidemics accurately. The effective reproduction number R eff , calculated by the next generation matrix, could assess the scale of the epidemic and be used to evaluate the effectiveness of prevention and control measures used in epidemics with a large number of cases. Time-varying reproduction number R t , obtained via epidemic curve and SI, can give a clear picture of the change in transmissibility over time, but the conditions of use are more rigorous, requiring a greater sample size and clear transmission chains to perform the calculation. The rational use of the three methods for reproduction numbers plays a role in the further study of the transmissibility of COVID-19.
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Affiliation(s)
- Buasiyamu Abudunaibi
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Weikang Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Zhinan Guo
- Xiamen Center for Disease Control and Prevention, Xiamen, Fujian, China
| | - Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
- Cirad, UMR 17, Intertryp, Université de Montpellier, Montpellier, France
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
- Cirad, UMR 17, Intertryp, Université de Montpellier, Montpellier, France
| | - Wentao Song
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Yao Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Qiuping Chen
- Cirad, UMR 17, Intertryp, Université de Montpellier, Montpellier, France
| | - Roger Frutos
- Cirad, UMR 17, Intertryp, Université de Montpellier, Montpellier, France
| | - Chenghao Su
- Zhongshan Hospital, Fudan University (Xiamen Branch), Xiamen, Fujian, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
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Liu Z, Zhou H, Ding N, Jia J, Su X, Ren H, Hou X, Zhang W, Liu C. Modeling the effects of vaccination, nucleic acid testing, and face mask wearing interventions against COVID-19 in large sports events. Front Public Health 2022; 10:1009152. [PMID: 36438220 PMCID: PMC9682230 DOI: 10.3389/fpubh.2022.1009152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 10/24/2022] [Indexed: 11/11/2022] Open
Abstract
The transmission of SARS-CoV-2 leads to devastating COVID-19 infections around the world, which has affected both human health and the development of industries dependent on social gatherings. Sports events are one of the subgroups facing great challenges. The uncertainty of COVID-19 transmission in large-scale sports events is a great barrier to decision-making with regard to reopening auditoriums. Policymakers and health experts are trying to figure out better policies to balance audience experiences and COVID-19 infection control. In this study, we employed the generalized SEIR model in conjunction with the Wells-Riley model to estimate the effects of vaccination, nucleic acid testing, and face mask wearing on audience infection control during the 2021 Chinese Football Association Super League from 20 April to 5 August. The generalized SEIR modeling showed that if the general population were vaccinated by inactive vaccines at an efficiency of 0.78, the total number of infectious people during this time period would decrease from 43,455 to 6,417. We assumed that the general population had the same odds ratio of entering the sports stadiums and becoming the audience. Their infection probabilities in the stadium were further estimated by the Wells-Riley model. The results showed that if all of the 30,000 seats in the stadium were filled by the audience, 371 audience members would have become infected during the 116 football games in the 2021 season. The independent use of vaccination and nucleic acid testing would have decreased this number to 79 and 118, respectively. The combined use of nucleic acid testing and vaccination or face mask wearing would have decreased this number to 14 and 34, respectively. The combined use of all three strategies could have further decreased this number to 0. According to the modeling results, policymakers can consider the combined use of vaccination, nucleic acid testing, and face mask wearing to protect audiences from infection when holding sports events, which could create a balance between audience experiences and COVID-19 infection control.
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Affiliation(s)
- Zeting Liu
- Department of Mathematic Science, School of Sport Engineering, Beijing Sport University, Beijing, China
| | - Huixuan Zhou
- Department of Physical Fitness and Health, School of Sport Science, Beijing Sport University, Beijing, China,Key Laboratory of Sports and Physical Health, Ministry of Education, Beijing Sport University, Beijing, China,*Correspondence: Huixuan Zhou
| | - Ningxin Ding
- School of Government, Wellington School of Business and Government, Victoria University of Wellington, Wellington, New Zealand
| | - Jihua Jia
- Department of Physical Fitness and Health, School of Sport Science, Beijing Sport University, Beijing, China
| | - Xinhua Su
- Department of Mathematic Science, School of Sport Engineering, Beijing Sport University, Beijing, China
| | - Hong Ren
- Department of Physical Fitness and Health, School of Sport Science, Beijing Sport University, Beijing, China
| | - Xiao Hou
- Department of Physical Fitness and Health, School of Sport Science, Beijing Sport University, Beijing, China,Key Laboratory of Sports and Physical Health, Ministry of Education, Beijing Sport University, Beijing, China
| | - Wei Zhang
- Department of Chemical Drug Control, China National Institute for Food and Drug Control, Beijing, China
| | - Chenzhe Liu
- Department of Physical Fitness and Health, School of Sport Science, Beijing Sport University, Beijing, China
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