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Wang Y, Zhao Z, Rui J, Chen T. Theoretical Epidemiology Needs Urgent Attention in China. China CDC Wkly 2024; 6:499-502. [PMID: 38854461 PMCID: PMC11154108 DOI: 10.46234/ccdcw2024.096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 03/19/2024] [Indexed: 06/11/2024] Open
Abstract
The mathematical method to which theoretical epidemiology belongs is one of the three major methodologies in epidemiology. It is of great value in diagnosing infectious disease epidemic trends and evaluating the effectiveness of prevention and control measures. This paper aims to summarize the brief history of the development of theoretical epidemiology, common types of mathematical models, and key steps to develop a mathematical model. It also provides some thoughts and perspectives on the development and application of theoretical epidemiology in China.
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Affiliation(s)
- Yao Wang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Zeyu Zhao
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Jia Rui
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Tianmu Chen
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
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Zheng W, Li H, Yang X, Wang L, Shi Y, Shan H, He L, Liu J, Chen H, Wang G, Zhao Y, Han C. Trends and prediction in the incidence rate of hepatitis C in Shandong Province in China from 2004 to 2030. Prev Med 2023; 177:107749. [PMID: 37918447 DOI: 10.1016/j.ypmed.2023.107749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 10/28/2023] [Accepted: 10/30/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND Hepatitis C threatens human health and brings a heavy economic burden. Shandong Province is the second most populous province in China and has uneven regional economic development. Therefore, we analyzed the incidence rate trend and regional differences of hepatitis C in Shandong Province from 2004 to 2021. METHODS The monthly and annual incidence rates of hepatitis C in Shandong Province from 2022 to 2030 were predicted by fitting Autoregressive Integrated Moving Average model (ARIMA), Long Short-Term Memory (LSTM) and ARIMA-LSTM combined model. RESULTS From 2004 to 2021, annual new cases of hepatitis C in Shandong Province increased from 635 to 5834, with a total of 61,707 cases. The incidence rate increased from 0.69/100 thousand in 2004 to 6.40/100 thousand in 2019, with a slight decrease in 2020 and 2021. The average annual incidence rate was 3.47/100 thousand. In terms of regional distribution, the hepatitis C incidence rate in Shandong Province was generally high in the west and low in the east. It is estimated that the hepatitis C incidence rate in Shandong Province will be 9.21 per 100 thousand in 2030. CONCLUSION The hepatitis C incidence rate in Shandong Province showed an increasing trend from 2004 to 2019 and a decreasing trend in 2020 and 2021. Significant regional variations in incidence rate existed. An upward trend in incidence rate is predicted from 2022 to 2030. It is necessary to strengthen the prevention and control of hepatitis C to achieve the goal of eliminating viral hepatitis by 2030.
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Affiliation(s)
- Wanying Zheng
- School of Public Health and Management, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Hongyu Li
- School of Public Health and Management, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Xingguang Yang
- Shandong Center for Disease Control and Prevention, Jinan, Shandong 250013, China
| | - Luyang Wang
- School of Public Health and Management, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Yukun Shi
- School of Public Health and Management, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Haifeng Shan
- Zibo Mental Health Center, Zibo, Shandong, 255100, China
| | - Lianping He
- School of medicine, Taizhou University, Taizhou, Zhejiang 318000, China
| | - Junyan Liu
- School of Public Health and Management, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Haotian Chen
- School of Public Health and Management, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Guangcheng Wang
- School of Public Health and Management, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Yang Zhao
- The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia; Digital Health and Stroke Program, The George Institute for Global Health, Beijing, China.
| | - Chunlei Han
- School of Public Health and Management, Binzhou Medical University, Yantai, Shandong 264003, China.
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Woyesa SB, Amente KD. Hepatitis C Virus Dynamic Transmission Models Among People Who Inject Drugs. Infect Drug Resist 2023; 16:1061-1068. [PMID: 36845020 PMCID: PMC9951810 DOI: 10.2147/idr.s403133] [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: 12/30/2022] [Accepted: 02/14/2023] [Indexed: 02/22/2023] Open
Abstract
Background Transmission dynamic model is a concrete structure to describe and investigate the complex system of host-pathogen interactions. Hepatitis C virus (HCV) is a blood-borne virus that is transmitted from infectious to susceptible individuals when they come into contact with HCV-contaminated equipment. Injecting drug use is the most known transmission route, and about 80% of new HCV cases have been confirmed as having acquired HCV infection via drug injection. Objective The main objective of this review paper was to review the importance of HCV dynamic transmission model, that enables the readers to understand the mechanism how HCV is transmissible from infectious to susceptible hosts and the effective controlling strategies. Methods PubMed Central, Google Scholar, and Web of Science electronic databases have been used to search data by using key terms like "HCV transmission model among people who inject drug (PWID)", HCV potential herd immunity", and "basic reproductive number for HCV transmission in PWID." Data from research findings other than English version have been excluded from being used, and the most recently published data have been considered to be included. Conclusion HCV belongs to the Hepacivirus genus within the Flaviviridae family. HCV infection is acquired when the susceptible individuals in populations come into contact with medical equipment such as shared syringes and needles, or swabs contaminated with infected blood. Construction of HCV transmission dynamic model is very significant in order to predict the duration and magnitude of its epidemic and to evaluate the potential impact of intervention. Comprehensive harm reduction and care/support service strategies are the best approach for intervention regarding HCV infection transmission among PWID.
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Wang Y, Zhao Z, Zhang H, Lin Q, Wang N, Ngwanguong Hannah M, Rui J, Yang T, Li P, Mao S, Lin S, Liu X, Zhu Y, Xu J, Yang M, Luo L, Liu C, Li Z, Deng B, Huang J, Liu W, Zhao B, Su Y, Chen T. Estimating the transmissibility of hepatitis C: A modelling study in Yichang City, China. J Viral Hepat 2021; 28:1464-1473. [PMID: 34314082 DOI: 10.1111/jvh.13582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 06/22/2021] [Accepted: 06/26/2021] [Indexed: 12/09/2022]
Abstract
Yichang is a city in central China in the Hubei Province. This study aimed to estimate the dynamics of the transmissibility of hepatitis C using a mathematical model and predict the transmissibility of hepatitis C in 2030. Data of hepatitis C cases from 13 counties or districts (cities) in Yichang from 2008 to 2016 were collected. A susceptible-infectious-chronic-recovered (SICR) model was developed to fit the data. The transmissibility of hepatitis C at the counties or districts was calculated based on new infections (including infected or chronically infected cases) reported monthly in the city caused by one infectious individual (MNI). The trend of the MNI was fitted and predicted using 11 models, with the coefficient of determination (R2 ) was being used to test the goodness of fit of these models. A total of 3065 cases of hepatitis C were reported in Yichang from 2008 to 2016. The median MNI of Yichang was 0.0768. According to the fitting results and analysis, the trend of transmissibility of hepatitis C in Yichang City conforms with the logarithmic (R2 = 0.918, p < 0.001):MNI = 0.265-0.108 log(t) and exponential (R2 = 0.939, p < 0.001): MNI = 0.344e(-0.278t) models. Hence, the transmission of hepatitis C virus at the county level has a downward trend. In conclusion, the transmissibility of hepatitis C in Yichang has a downward trend. With the current preventive and control measures in place, the spread of hepatitis C can be controlled.
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Affiliation(s)
- Yao Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Hao Zhang
- Yichang municipal Center for Disease Control and Prevention, Yichang City, China
| | - Qin Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Ning Wang
- Shenzhen Heng Sheng Hospital, Shenzhen City, China
| | | | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Tianlong Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Peihua Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Siying Mao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Shengnan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Xingchun Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Yuanzhao Zhu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Jingwen Xu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Meng Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Li Luo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Chan Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Zhuoyang Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Bin Deng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Jiefeng Huang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Weikang Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Benhua Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Yanhua Su
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, China
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