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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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Rui J, Zheng JX, Chen J, Wei H, Yu S, Zhao Z, Wang XY, Chen MX, Xia S, Zhou Y, Chen T, Zhou XN. Optimal control strategies of SARS-CoV-2 Omicron supported by invasive and dynamic models. Infect Dis Poverty 2022; 11:115. [PMID: 36435792 PMCID: PMC9701379 DOI: 10.1186/s40249-022-01039-y] [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: 06/20/2022] [Accepted: 10/28/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND There is a raising concern of a higher infectious Omicron BA.2 variant and the latest BA.4, BA.5 variant, made it more difficult in the mitigation process against COVID-19 pandemic. Our study aimed to find optimal control strategies by transmission of dynamic model from novel invasion theory. METHODS Based on the public data sources from January 31 to May 31, 2022, in four cities (Nanjing, Shanghai, Shenzhen and Suzhou) of China. We segmented the theoretical curves into five phases based on the concept of biological invasion. Then, a spatial autocorrelation analysis was carried out by detecting the clustering of the studied areas. After that, we choose a mathematical model of COVID-19 based on system dynamics methodology to simulate numerous intervention measures scenarios. Finally, we have used publicly available migration data to calculate spillover risk. RESULTS Epidemics in Shanghai and Shenzhen has gone through the entire invasion phases, whereas Nanjing and Suzhou were all ended in the establishment phase. The results indicated that Rt value and public health and social measures (PHSM)-index of the epidemics were a negative correlation in all cities, except Shenzhen. The intervention has come into effect in different phases of invasion in all studied cities. Until the May 31, most of the spillover risk in Shanghai remained above the spillover risk threshold (18.81-303.84) and the actual number of the spillovers (0.94-74.98) was also increasing along with the time. Shenzhen reported Omicron cases that was only above the spillover risk threshold (17.92) at the phase of outbreak, consistent with an actual partial spillover. In Nanjing and Suzhou, the actual number of reported cases did not exceed the spillover alert value. CONCLUSIONS Biological invasion is positioned to contribute substantively to understanding the drivers and mechanisms of the COVID-19 spread and outbreaks. After evaluating the spillover risk of cities at each invasion phase, we found the dynamic zero-COVID strategy implemented in four cities successfully curb the disease epidemic peak of the Omicron variant, which was highly correlated to the way to perform public health and social measures in the early phases right after the invasion of the virus.
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Affiliation(s)
- Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Jin-Xin Zheng
- Department of Nephrology, Institute of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People's Republic of China
| | - Jin Chen
- National Institute of Parasitic Diseases at Chinese Center for Disease Control and Prevention, WHO Collaborating Centre for Tropical Diseases, NHC Key Laboratory of Parasites and Vectors Biology of China, Shanghai, 200025, People's Republic of China
| | - Hongjie Wei
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Shanshan Yu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, People's Republic of China
| | - Xin-Yi Wang
- National Institute of Parasitic Diseases at Chinese Center for Disease Control and Prevention, WHO Collaborating Centre for Tropical Diseases, NHC Key Laboratory of Parasites and Vectors Biology of China, Shanghai, 200025, People's Republic of China
| | - Mu-Xin Chen
- National Institute of Parasitic Diseases at Chinese Center for Disease Control and Prevention, WHO Collaborating Centre for Tropical Diseases, NHC Key Laboratory of Parasites and Vectors Biology of China, Shanghai, 200025, People's Republic of China
| | - Shang Xia
- National Institute of Parasitic Diseases at Chinese Center for Disease Control and Prevention, WHO Collaborating Centre for Tropical Diseases, NHC Key Laboratory of Parasites and Vectors Biology of China, Shanghai, 200025, People's Republic of China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People's Republic of China
| | - Ying Zhou
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People's Republic of China.
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, People's Republic of China.
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases at Chinese Center for Disease Control and Prevention, WHO Collaborating Centre for Tropical Diseases, NHC Key Laboratory of Parasites and Vectors Biology of China, Shanghai, 200025, People's Republic of China.
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People's Republic of China.
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Deng B, Niu Y, Xu J, Rui J, Lin S, Zhao Z, Yu S, Guo Y, Luo L, Chen T, Li Q. Mathematical Models Supporting Control of COVID-19. China CDC Wkly 2022; 4:895-901. [PMID: 36285321 PMCID: PMC9579983 DOI: 10.46234/ccdcw2022.186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/03/2022] [Indexed: 12/13/2022] Open
Abstract
Mathematical models have played an important role in the management of the coronavirus disease 2019 (COVID-19) pandemic. The aim of this review is to describe the use of COVID-19 mathematical models, their classification, and the advantages and disadvantages of different types of models. We conducted subject heading searches of PubMed and China National Knowledge Infrastructure with the terms "COVID-19," "Mathematical Statistical Model," "Model," "Modeling," "Agent-based Model," and "Ordinary Differential Equation Model" and classified and analyzed the scientific literature retrieved in the search. We categorized the models as data-driven or mechanism-driven. Data-driven models are mainly used for predicting epidemics, and have the advantage of rapid assessment of disease instances. However, their ability to determine transmission mechanisms is limited. Mechanism-driven models include ordinary differential equation (ODE) and agent-based models. ODE models are used to estimate transmissibility and evaluate impact of interventions. Although ODE models are good at determining pathogen transmission characteristics, they are less suitable for simulation of early epidemic stages and rely heavily on availability of first-hand field data. Agent-based models consider influences of individual differences, but they require large amounts of data and can take a long time to develop fully. Many COVID-19 mathematical modeling studies have been conducted, and these have been used for predicting trends, evaluating interventions, and calculating pathogen transmissibility. Successful infectious disease modeling requires comprehensive considerations of data, applications, and purposes.
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Affiliation(s)
- Bin Deng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Yan Niu
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jingwen Xu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Shengnan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Shanshan Yu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Yichao Guo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Li Luo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China,Tianmu Chen,
| | - Qun Li
- Chinese Center for Disease Control and Prevention, Beijing, China,Qun Li,
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Liu W, Guo Z, Abudunaibi B, Ouyang X, Wang D, Yang T, Deng B, Huang J, Zhao B, Su Y, Su C, Chen T. Model-Based Evaluation of Transmissibility and Intervention Measures for a COVID-19 Outbreak in Xiamen City, China. Front Public Health 2022; 10:887146. [PMID: 35910883 PMCID: PMC9326243 DOI: 10.3389/fpubh.2022.887146] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Background In September 2021, there was an outbreak of coronavirus disease 2019 (COVID-19) in Xiamen, China. Various non-pharmacological interventions (NPIs) and pharmacological interventions (PIs) have been implemented to prevent and control the spread of the disease. This study aimed to evaluate the effectiveness of various interventions and to identify priorities for the implementation of prevention and control measures. Methods The data of patients with COVID-19 were collected from 8 to 30 September 2021. A Susceptible-Exposed-Infectious-Recovered (SEIR) dynamics model was developed to fit the data and simulate the effectiveness of interventions (medical treatment, isolation, social distancing, masking, and vaccination) under different scenarios. The effective reproductive number (Reff) was used to assess the transmissibility and transmission risk. Results A total of 236 cases of COVID-19 were reported in Xiamen. The epidemic curve was divided into three phases (Reff = 6.8, 1.5, and 0). Notably, the cumulative number of cases was reduced by 99.67% due to the preventive and control measures implemented by the local government. In the effective containment stage, the number of cases could be reduced to 115 by intensifying the implementation of interventions. The total number of cases (TN) could be reduced by 29.66–95.34% when patients voluntarily visit fever clinics. When only two or three of these measures are implemented, the simulated TN may be greater than the actual number. As four measures were taken simultaneously, the TN may be <100, which is 57.63% less than the actual number. The simultaneous implementation of five interventions could rapidly control the transmission and reduce the number of cases to fewer than 25. Conclusion With the joint efforts of the government and the public, the outbreak was controlled quickly and effectively. Authorities could promptly cut the transmission chain and control the spread of the disease when patients with fever voluntarily went to the hospital. The ultimate effect of controlling the outbreak through only one intervention was not obvious. The combined community control and mask wearing, along with other interventions, could lead to rapid control of the outbreak and ultimately lower the total number of cases. More importantly, this would mitigate the impact of the outbreak on society and socioeconomics.
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Affiliation(s)
- Weikang Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Zhinan Guo
- Xiamen Center for Disease Control and Prevention, Xiamen, China
| | - Buasiyamu Abudunaibi
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Xue Ouyang
- Xiamen Center for Disease Control and Prevention, Xiamen, China
| | - Demeng Wang
- Xiamen Center for Disease Control and Prevention, Xiamen, China
| | - Tianlong Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Bin Deng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Jiefeng Huang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Benhua Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Yanhua Su
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Chenghao Su
- Zhongshan Hospital, Fudan University (Xiamen Branch), Xiamen, China
- Chenghao Su
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- *Correspondence: Tianmu Chen
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Guo X, Guo Y, Zhao Z, Yang S, Su Y, Zhao B, Chen T. Computing R0 of dynamic models by a definition-based method. Infect Dis Model 2022; 7:196-210. [PMID: 35702140 PMCID: PMC9160772 DOI: 10.1016/j.idm.2022.05.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 11/22/2022] Open
Abstract
Objectives Computing the basic reproduction number (R0) in deterministic dynamical models is a hot topic and is frequently demanded by researchers in public health. The next-generation methods (NGM) are widely used for such computation, however, the results of NGM are usually not to be the true R0 but only a threshold quantity with little interpretation. In this paper, a definition-based method (DBM) is proposed to solve such a problem. Methods Start with the definition of R0, consider different states that one infected individual may develop into, and take expectations. A comparison with NGM has proceeded. Numerical verification is performed using parameters fitted by data of COVID-19 in Hunan Province. Results DBM and NGM give identical expressions for single-host models with single-group and interactive Rij of single-host models with multi-groups, while difference arises for models partitioned into subgroups. Numerical verification showed the consistencies and differences between DBM and NGM, which supports the conclusion that R0 derived by DBM with true epidemiological interpretations are better. Conclusions DBM is more suitable for single-host models, especially for models partitioned into subgroups. However, for multi-host dynamic models where the true R0 is failed to define, we may turn to the NGM for the threshold R0.
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Affiliation(s)
- Xiaohao Guo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of China
| | - Yichao Guo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of China
| | - Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of China
- Université de Montpellier, CIRAD, Intertryp, IES, Université de Montpellier-CNRS, Montpellier, France
| | - Shiting Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of China
| | - Yanhua Su
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of China
| | - Benhua Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 361102, Fujian Province, People's Republic of China
- Corresponding author. State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 4221-117, South Xiang'an Road, Xiang'an District, Xiamen City, Fujian Province, People's Republic of China.
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Li Z, Lin S, Rui J, Bai Y, Deng B, Chen Q, Zhu Y, Luo L, Yu S, Liu W, Zhang S, Su Y, Zhao B, Zhang H, Chiang YC, Liu J, Luo K, Chen T. An Easy-to-Use Public Health-Driven Method (the Generalized Logistic Differential Equation Model) Accurately Simulated COVID-19 Epidemic in Wuhan and Correctly Determined the Early Warning Time. Front Public Health 2022; 10:813860. [PMID: 35321194 PMCID: PMC8936678 DOI: 10.3389/fpubh.2022.813860] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 01/21/2022] [Indexed: 11/13/2022] Open
Abstract
IntroductionModeling on infectious diseases is significant to facilitate public health policymaking. There are two main mathematical methods that can be used for the simulation of the epidemic and prediction of optimal early warning timing: the logistic differential equation (LDE) model and the more complex generalized logistic differential equation (GLDE) model. This study aimed to compare and analyze these two models.MethodsWe collected data on (coronavirus disease 2019) COVID-19 and four other infectious diseases and classified the data into four categories: different transmission routes, different epidemic intensities, different time scales, and different regions, using R2 to compare and analyze the goodness-of-fit of LDE and GLDE models.ResultsBoth models fitted the epidemic curves well, and all results were statistically significant. The R2 test value of COVID-19 was 0.924 (p < 0.001) fitted by the GLDE model and 0.916 (p < 0.001) fitted by the LDE model. The R2 test value varied between 0.793 and 0.966 fitted by the GLDE model and varied between 0.594 and 0.922 fitted by the LDE model for diseases with different transmission routes. The R2 test values varied between 0.853 and 0.939 fitted by the GLDE model and varied from 0.687 to 0.769 fitted by the LDE model for diseases with different prevalence intensities. The R2 test value varied between 0.706 and 0.917 fitted by the GLDE model and varied between 0.410 and 0.898 fitted by the LDE model for diseases with different time scales. The GLDE model also performed better with nation-level data with the R2 test values between 0.897 and 0.970 vs. 0.731 and 0.953 that fitted by the LDE model. Both models could characterize the patterns of the epidemics well and calculate the acceleration weeks.ConclusionThe GLDE model provides more accurate goodness-of-fit to the data than the LDE model. The GLDE model is able to handle asymmetric data by introducing shape parameters that allow it to fit data with various distributions. The LDE model provides an earlier epidemic acceleration week than the GLDE model. We conclude that the GLDE model is more advantageous in asymmetric infectious disease data simulation.
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Affiliation(s)
- Zhuoyang Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Shengnan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Yao Bai
- Department of Infection Disease Control and Prevention, Xi'an Center for Disease Prevention and Control, Xi'an, China
| | - Bin Deng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Qiuping Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- Université de Montpellier, Montpellier, France
- CIRAD, Intertryp, Montpellier, France
- IES, Université de Montpellier-CNRS, Montpellier, France
| | - Yuanzhao Zhu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Li Luo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Shanshan Yu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Weikang Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Shi Zhang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Yanhua Su
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Benhua Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Hao Zhang
- Yichang Center for Disease Control and Prevention, Yichang, China
| | - Yi-Chen Chiang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- Yi-Chen Chiang
| | - Jianhua Liu
- Yichang Center for Disease Control and Prevention, Yichang, China
- Jianhua Liu
| | - Kaiwei Luo
- Hunan Provincial Center for Disease Control and Prevention, Changsha, China
- Kaiwei Luo
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- *Correspondence: Tianmu Chen
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9
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Promising Antiviral Activity of Agrimonia pilosa Phytochemicals against Severe Acute Respiratory Syndrome Coronavirus 2 Supported with In Vivo Mice Study. Pharmaceuticals (Basel) 2021; 14:ph14121313. [PMID: 34959713 PMCID: PMC8709118 DOI: 10.3390/ph14121313] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/08/2021] [Accepted: 12/13/2021] [Indexed: 12/21/2022] Open
Abstract
The global emergence of the COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has focused the entire world’s attention toward searching for a potential remedy for this disease. Thus, we investigated the antiviral activity of Agrimonia pilosa ethanol extract (APEE) against SARS-CoV-2 and it exhibited a potent antiviral activity with IC50 of 1.1 ± 0.03 µg/mL. Its mechanism of action was elucidated, and it exhibited a virucidal activity and an inhibition of viral adsorption. Moreover, it presented an immunomodulatory activity as it decreased the upregulation of gene expression of COX-2, iNOS, IL-6, TNF-α, and NF-κB in lipopolysaccharide (LPS)-induced peripheral blood mononuclear cells. A comprehensive analysis of the phytochemical fingerprint of APEE was conducted using LC-ESI-MS/MS technique for the first time. We detected 81 compounds and most of them belong to the flavonoid and coumarin classes. Interestingly, isoflavonoids, procyanidins, and anthocyanins were detected for the first time in A. pilosa. Moreover, the antioxidant activity was evidenced in DPPH (IC50 62.80 µg/mL) and ABTS (201.49 mg Trolox equivalents (TE)/mg) radical scavenging, FRAP (60.84 mg TE/mg), and ORAC (306.54 mg TE/g) assays. Furthermore, the protective effect of APEE was investigated in Lipopolysaccharides (LPS)-induced acute lung injury (ALI) in mice. Lung W/D ratio, serum IL-6, IL-18, IL-1β, HO-1, Caspase-1, caspase-3, TLR-4 expression, TAC, NO, MPO activity, and histopathological examination of lung tissues were assessed. APEE induced a marked downregulation in all inflammation, oxidative stress, apoptosis markers, and TLR-4 expression. In addition, it alleviated all histopathological abnormalities confirming the beneficial effects of APEE in ALI. Therefore, APEE could be a potential source for therapeutic compounds that could be investigated, in future preclinical and clinical trials, in the treatment of patients with COVID-19.
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10
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Ramzi ZS. Hospital readmissions and post-discharge all-cause mortality in COVID-19 recovered patients; A systematic review and meta-analysis. Am J Emerg Med 2021; 51:267-279. [PMID: 34781153 PMCID: PMC8570797 DOI: 10.1016/j.ajem.2021.10.059] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVE The present study aimed to perform a systematic review and meta-analysis on the prevalence of one-year hospital readmissions and post-discharge all-cause mortality in recovered COVID-19 patients. Moreover, the country-level prevalence of the outcomes was investigated. METHODS An extensive search was performed in Medline (PubMed), Embase, Scopus, and Web of Science databases until the end of August 3rd, 2021. A manual search was also performed in Google and Google Scholar search engines. Cohort and cross-sectional studies were included. Two independent reviewers screened the papers, collected data, and assessed the risk of bias and level of evidence. Any disagreement was resolved through discussion. RESULTS 91 articles were included. 48 studies examined hospital readmissions; nine studies assessed post-discharge all-cause mortality, and 34 studies examined both outcomes. Analyses showed that the prevalence of hospital readmissions during the first 30 days, 90 days, and one-year post-discharge were 8.97% (95% CI: 7.44, 10.50), 9.79% (95% CI: 8.37, 11.24), and 10.34% (95% CI: 8.92, 11.77), respectively. The prevalence of post-discharge all-cause mortality during the 30 days, 90 days and one-year post-discharge was 7.87% (95% CI: 2.78, 12.96), 7.63% (95% CI: 4.73, 10.53) and 7.51% (95% CI, 5.30, 9.72), respectively. 30-day hospital readmissions and post-discharge mortality were 8.97% and 7.87%, respectively. The highest prevalence of hospital readmissions was observed in Germany (15.5%), Greece (15.5%), UK (13.5%), Netherlands (11.7%), China (10.8%), USA (10.0%) and Sweden (9.9%). In addition, the highest prevalence of post-discharge all-cause mortality belonged to Italy (12.7%), the UK (11.8%), and Iran (9.2%). Sensitivity analysis showed that the prevalence of one-year hospital readmissions and post-discharge all-cause mortality in high-quality studies were 10.38% and 4.00%, respectively. CONCLUSION 10.34% of recovered COVID-19 patients required hospital readmissions after discharge. Most cases of hospital readmissions and mortality appear to occur within 30 days after discharge. The one-year post-discharge all-cause mortality rate of COVID-19 patients is 7.87%, and the majority of patients' readmission and mortality happens within the first 30 days post-discharge. Therefore, a 30-day follow-up program and patient tracking system for discharged COVID-19 patients seems necessary.
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Affiliation(s)
- Zhian Salah Ramzi
- Department of Family and Community Medicine, College of Medicine, University of Sulaimani, Sulaimani, Iraq.
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11
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Niu Y, Rui J, Wang Q, Zhang W, Chen Z, Xie F, Zhao Z, Lin S, Zhu Y, Wang Y, Xu J, Liu X, Yang M, Zheng W, Chen K, Xia Y, Xu L, Zhang S, Ji R, Jin T, Chen Y, Zhao B, Su Y, Song T, Chen T, Hu G. Containing the Transmission of COVID-19: A Modeling Study in 160 Countries. Front Med (Lausanne) 2021; 8:701836. [PMID: 34485337 PMCID: PMC8416347 DOI: 10.3389/fmed.2021.701836] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/20/2021] [Indexed: 12/26/2022] Open
Abstract
Background: It is much valuable to evaluate the comparative effectiveness of the coronavirus disease 2019 (COVID-19) prevention and control in the non-pharmacological intervention phase of the pandemic across countries and identify useful experiences that could be generalized worldwide. Methods: In this study, we developed a susceptible-exposure-infectious-asymptomatic-removed (SEIAR) model to fit the daily reported COVID-19 cases in 160 countries. The time-varying reproduction number (R t ) that was estimated through fitting the mathematical model was adopted to quantify the transmissibility. We defined a synthetic index (I AC ) based on the value of R t to reflect the national capability to control COVID-19. Results: The goodness-of-fit tests showed that the SEIAR model fitted the data of the 160 countries well. At the beginning of the epidemic, the values of R t of countries in the European region were generally higher than those in other regions. Among the 160 countries included in the study, all European countries had the ability to control the COVID-19 epidemic. The Western Pacific Region did best in continuous control of the epidemic, with a total of 73.76% of countries that can continuously control the COVID-19 epidemic, while only 43.63% of the countries in the European Region continuously controlled the epidemic, followed by the Region of Americas with 52.53% of countries, the Southeast Asian Region with 48% of countries, the African Region with 46.81% of countries, and the Eastern Mediterranean Region with 40.48% of countries. Conclusion: Large variations in controlling the COVID-19 epidemic existed across countries. The world could benefit from the experience of some countries that demonstrated the highest containment capabilities.
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Affiliation(s)
- Yan Niu
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Qiupeng Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
- School of Journalism and Communication, Peking University, Beijing, China
| | - Wei Zhang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Zhiwei Chen
- Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, China
| | - Fang Xie
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Shengnan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Yuanzhao Zhu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Yao Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Jingwen Xu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Xingchun Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Meng Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Wei Zheng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Kaixin Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Yilan Xia
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Lijuan Xu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Shi Zhang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Rongrong Ji
- Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, China
| | - Taisong Jin
- Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, China
| | - Yong Chen
- Department of Stomatology, School of Medicine, Xiamen University, Xiamen, China
| | - Benhua Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Yanhua Su
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Tie Song
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Guoqing Hu
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China
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