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Rui J, Qu H, Zhang S, Liu H, Wei H, Abudunaibi B, Li K, Zhao Y, Liu Q, Fang K, Gavotte L, Frutos R, Chen T. Assessment of transmissibility and measures effectiveness of SARS in 8 regions, China, 2002-2003. Front Cell Infect Microbiol 2023; 13:1212473. [PMID: 37637464 PMCID: PMC10449464 DOI: 10.3389/fcimb.2023.1212473] [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: 04/26/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023] Open
Abstract
Background Severe acute respiratory syndrome (SARS) is a form of atypical pneumonia which took hundreds of lives when it swept the world two decades ago. The pathogen of SARS was identified as SARS-coronavirus (SARS-CoV) and it was mainly transmitted in China during the SARS epidemic in 2002-2003. SARS-CoV and SARS-CoV-2 have emerged from the SARS metapopulation of viruses. However, they gave rise to two different disease dynamics, a limited epidemic, and an uncontrolled pandemic, respectively. The characteristics of its spread in China are particularly noteworthy. In this paper, the unique characteristics of time, space, population distribution and transmissibility of SARS for the epidemic were discussed in detail. Methods We adopted sliding average method to process the number of reported cases per day. An SEIAR transmission dynamics model, which was the first to take asymptomatic group into consideration and applied indicators of R 0, Reff, Rt to evaluate the transmissibility of SARS, and further illustrated the control effectiveness of interventions for SARS in 8 Chinese cities. Results The R 0 for SARS in descending order was: Tianjin city (R 0 = 8.249), Inner Mongolia Autonomous Region, Shanxi Province, Hebei Province, Beijing City, Guangdong Province, Taiwan Province, and Hong Kong. R 0 of the SARS epidemic was generally higher in Mainland China than in Hong Kong and Taiwan Province (Mainland China: R 0 = 6.058 ± 1.703, Hong Kong: R 0 = 2.159, Taiwan: R 0 = 3.223). All cities included in this study controlled the epidemic successfully (Reff<1) with differences in duration. Rt in all regions showed a downward trend, but there were significant fluctuations in Guangdong Province, Hong Kong and Taiwan Province compared to other areas. Conclusion The SARS epidemic in China showed a trend of spreading from south to north, i.e., Guangdong Province and Beijing City being the central regions, respectively, and from there to the surrounding areas. In contrast, the SARS epidemic in the central region did not stir a large-scale transmission. There were also significant differences in transmissibility among eight regions, with R0 significantly higher in the northern region than that in the southern region. Different regions were able to control the outbreak successfully in differences time.
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Affiliation(s)
- Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
- Espace-Dev, Université de Montpellier, Montpellier, France
- French Agricultural Research Centre for International Development (CIRAD), URM 17, Intertryp, Montpellier, France
| | - Huimin Qu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Shuo Zhang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Hong Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Hongjie Wei
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Buasiyamu Abudunaibi
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Kangguo Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Yunkang Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Qiao Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Kang Fang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | | | - Roger Frutos
- French Agricultural Research Centre for International Development (CIRAD), URM 17, Intertryp, Montpellier, France
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
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Wang J, Rui J, Zhu Y, Guo X, Abudunaibi B, Zhao B, Su Y, Chen T, Hu J. Evaluation of the transmissibility of norovirus and the effectiveness of prevention and control measures for schools in Jiangsu Province. Ann Med 2023; 55:2246474. [PMID: 37604118 PMCID: PMC10444007 DOI: 10.1080/07853890.2023.2246474] [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: 05/31/2023] [Revised: 08/01/2023] [Accepted: 08/03/2023] [Indexed: 08/23/2023] Open
Abstract
OBJECTIVE This study aims to estimate the transmissibility of norovirus outbreaks in schools by different transmission routes, and to evaluate the effects of isolation, school-closure and disinfection measures under different intervention intensities, finally, scientific prevention and control suggestions are proposed. METHOD 23 outbreaks of norovirus infectious diarrhea occurring in Jiangsu Province's school from 2012-2018 were selected and fitted to the model. The data includes various types of school places and pathogen genotype. A 'SEIAQRW' model with two transmission routes was established. The transmissibility of each outbreak was assessed using effective reproduction number, the efficacy of different intervention measures and intensities were evaluated by calculating the total attack rate and peak incidence. RESULTS The mean effective reproduction number of noroviruses was estimated to be 8.92 for the human-to-human route of transmission and 2.19 for the water or food-to-human route of transmission. When all symptomatic cases were isolated, the median peak incidence for both transmission routes both being less than 1.8%. There was a smaller reduction in total attack rate compared to peak incidence, the median total attack rate for the two transmission routes decreased by 17.59% and 42.09%, respectively. When the effect of school-closure or disinfection is more than 90%, the total attack rate and peak incidence in the human-to-human route are reduced by more than 90% compared to no intervention, and the peak incidence in the water or food-to-human routes can be reduced to less than 1.4%, but the reduction in the total attack rate is only 50% or so. CONCLUSION Norovirus outbreaks have a high rate of transmission in schools. In the case of norovirus outbreaks, isolation should be complemented by other interventions, and the implementation of high-intensity school closures or disinfection of the external environment can be effective in reducing the spread of the virus.
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Affiliation(s)
- Jing Wang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of 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, People’s Republic of China
| | - Yuanzhao Zhu
- Nanjing Center for Disease Control and Prevention, Nanjing, Jiangsu Province, People’s Republic of China
| | - Xiaohao Guo
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Buasiyamu Abudunaibi
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Benhua Zhao
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Yanhua Su
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of 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, People’s Republic of China
| | - Jianli Hu
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, Jiangsu Province, People’s Republic of China
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Zhao Z, Yang M, Lv J, Hu Q, Chen Q, Lei Z, Wang M, Zhang H, Zhai X, Zhao B, Su Y, Chen Y, Zhang XS, Cui JA, Frutos R, Chen T. Shigellosis seasonality and transmission characteristics in different areas of China: A modelling study. Infect Dis Model 2022; 7:161-178. [PMID: 35662902 PMCID: PMC9144056 DOI: 10.1016/j.idm.2022.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/15/2022] [Accepted: 05/18/2022] [Indexed: 11/30/2022] Open
Abstract
Objective In China, the burden of shigellosis is unevenly distributed, notably across various ages and geographical areas. Shigellosis temporal trends appear to be seasonal. We should clarify seasonal warnings and regional transmission patterns. Method This study adopted a Logistic model to assess the seasonality and a dynamics model to compare the transmission in different areas. The next-generation matrix was used to calculate the effective reproduction number (Reff) to quantify the transmissibility. Results In China, the rate of shigellosis fell from 35.12 cases per 100,000 people in 2005 to 7.85 cases per 100,000 people in 2017, peaking in June and August. After simulation by the Logistic model, the ‘peak time’ is mainly concentrated from mid-June to mid-July. China's ‘early warning time’ is primarily focused on from April to May. We predict the ‘peak time’ of shigellosis is the 6.30th month and the ‘early warning time’ is 3.87th month in 2021. According to the dynamics model results, the water/food transfer pathway has been mostly blocked off. The transmissibility of different regions varies greatly, such as the mean Reff of Longde County (3.76) is higher than Xiamen City (3.15), higher than Chuxiong City (2.52), and higher than Yichang City (1.70). Conclusion The ‘early warning time’ for shigellosis in China is from April to May every year, and it may continue to advance in the future, such as the early warning time in 2021 is in mid-March. Furthermore, we should focus on preventing and controlling the person-to-person route of shigellosis and stratified deploy prevention and control measures according to the regional transmission.
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Affiliation(s)
- Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People's Republic of China
- CIRAD, UMR 17, Intertryp, Montpellier, France
| | - Meng Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People's Republic of China
| | - Jinlong Lv
- Department of Mathematics, School of Science, Beijing University of Civil Engineering and Architecture, Beijing, 102616, People's Republic of China
| | - Qingqing Hu
- Division of Public Health, School of Medicine, University of Utah, 201 Presidents Circle, Salt Lake City, 84112, Utah, USA
| | | | - Zhao Lei
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People's Republic of China
| | - Mingzhai Wang
- Xiamen Center for Disease Control and Prevention, Xiamen City, Fujian Province, People's Republic of China
| | - Hao Zhang
- Yichang Center for Disease Control and Prevention, Yichang City, Hubei Province, People's Republic of China
| | - Xiongjie Zhai
- Longde County Center for Disease Control and Prevention, Guyuan City, Ningxia Hui Autonomous Region, People's Republic of China
| | - Benhua Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People's Republic of China
| | - Yanhua Su
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, People's Republic of China
| | - Yong Chen
- Department of Stomatology, School of Medicine, Xiamen University People's Republic of China
| | | | - Jing-An Cui
- Department of Mathematics, School of Science, Beijing University of Civil Engineering and Architecture, Beijing, 102616, People's Republic of China
| | - Roger Frutos
- CIRAD, UMR 17, Intertryp, Montpellier, France
- Corresponding author. CIRAD, Intertryp, Montpellier, France.
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 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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Ai J, Zhu Y, Fu J, Cheng X, Zhang X, Ji H, Liu W, Rui J, Xu J, Yang T, Wang Y, Liu X, Yang M, Lin S, Guo X, Bao C, Li Q, Chen T. Study of Risk Factors for Total Attack Rate and Transmission Dynamics of Norovirus Outbreaks, Jiangsu Province, China, From 2012 to 2018. Front Med (Lausanne) 2022; 8:786096. [PMID: 35071268 PMCID: PMC8777030 DOI: 10.3389/fmed.2021.786096] [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: 09/29/2021] [Accepted: 12/01/2021] [Indexed: 11/23/2022] Open
Abstract
Objective: To describe the epidemiological characteristics of norovirus outbreaks in Jiangsu Province, utilize the total attack rate (TAR) and transmissibility (Runc) as the measurement indicators of the outbreak, and a statistical difference in risk factors associated with TAR and transmissibility was compared. Ultimately, this study aimed to provide scientific suggestions to develop the most appropriate prevention and control measures. Method: We collected epidemiological data from investigation reports of all norovirus outbreaks in Jiangsu Province from 2012 to 2018 and performed epidemiological descriptions, sequenced the genes of the positive specimens collected that were eligible for sequencing, created a database and calculated the TAR, constructed SEIAR and SEIARW transmission dynamic models to calculate Runc, and performed statistical analyses of risk factors associated with the TAR and Runc. Results: We collected a total of 206 reported outbreaks, of which 145 could be used to calculate transmissibility. The mean TAR in was 2.6% and the mean Runc was 12.2. The epidemiological characteristics of norovirus outbreaks showed an overall increasing trend in the number of norovirus outbreaks from 2012 to 2018; more outbreaks in southern Jiangsu than northern Jiangsu; more outbreaks in urban areas than in rural areas; outbreaks occurred mostly in autumn and winter. Most of the sites where outbreaks occurred were schools, especially primary schools. Interpersonal transmission accounted for the majority. Analysis of the genotypes of noroviruses revealed that the major genotypes of the viruses changed every 3 years, with the GII.2 [P16] type of norovirus dominating from 2016 to 2018. Statistical analysis of TAR associated with risk factors found statistical differences in all risk factors, including time (year, month, season), location (geographic location, type of settlement, type of premises), population (total number of susceptible people at the outbreak site), transmission route, and genotype (P < 0.05). Statistical analysis of transmissibility associated with risk factors revealed that only transmissibility was statistically different between sites. Conclusions: The number of norovirus outbreaks in Jiangsu Province continues to increase during the follow-up period. Our findings highlight the impact of different factors on norovirus outbreaks and identify the key points of prevention and control in Jiangsu Province.
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Affiliation(s)
- Jing Ai
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.,Key Laboratory of Enteric Pathogenic Microbiology, Ministry of Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Yuanzhao Zhu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Jianguang Fu
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.,Key Laboratory of Enteric Pathogenic Microbiology, Ministry of Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Xiaoqing Cheng
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.,Key Laboratory of Enteric Pathogenic Microbiology, Ministry of Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Xuefeng Zhang
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.,Key Laboratory of Enteric Pathogenic Microbiology, Ministry of Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Hong Ji
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.,Key Laboratory of Enteric Pathogenic Microbiology, Ministry of Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Wendong Liu
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.,Key Laboratory of Enteric Pathogenic Microbiology, Ministry of Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Jia Rui
- 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
| | - Tianlong Yang
- 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
| | - 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
| | - Shengnan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Xiaohao Guo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Changjun Bao
- Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.,Key Laboratory of Enteric Pathogenic Microbiology, Ministry of Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Qun Li
- Public Health Emergency Center, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
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Xu Y, Zhu Y, Lei Z, Rui J, Zhao Z, Lin S, Wang Y, Xu J, Liu X, Yang M, Chen H, Pan X, Lu W, Du Y, Li H, Fang L, Zhang M, Zhou L, Yang F, Chen T. Investigation and analysis on an outbreak of norovirus infection in a health school in Guangdong Province, China. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2021; 96:105135. [PMID: 34781036 DOI: 10.1016/j.meegid.2021.105135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/14/2021] [Accepted: 11/08/2021] [Indexed: 06/13/2023]
Abstract
Our objective was to describe the epidemiological features of an outbreak of norovirus infection in a health school in Guangdong province, China, to identify the cause of such a large scale outbreak of norovirus among older students, to simulate the transmission dynamics, and to evaluate the effect of intervention measures of GII.17 [P17] genotype norovirus infection. We identified all cases during the outbreak. Descriptive epidemiological, analytical epidemiological and hygiene survey methods were used to described the outbreak epidemic course and identify the cause of the outbreak of norovirus infection. We also used dynamical model to simulate the transmission dynamics of norovirus infection and evaluate the effect of intervention measures. Norovirus genotyping was assigned to the newly obtained strains, with a maximum likelihood phylogenetic analysis conducted. There were 360 cases of 42 classes in five grades with a 12.99% attack rate. Proportionally, more students were in contact with sick students and vomit in the suspected case group than the control group (χ2 = 5.535, P = 0.019 and χ2 = 5.549, P = 0.019, respectively). The basic reproduction number was 8.32 before and 0.49 after the intervention. Dynamical modeling showed that if the isolation rate was higher or case isolation began earlier, the total attack rate would decrease. Molecular characterization identified the GII.17 [P17] genotype in all stains obtained from the health school, which were clustered with high support in the phylogenetic tree. This was an outbreak of norovirus infection caused by contact transmission. The main reasons for the spread of the epidemic were the later control time, irregular treatment of vomit and no case isolation. The transmission dynamics of contact transmission was high, more efficient control measures should be employed.
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Affiliation(s)
- Yucheng Xu
- Futian District Center for Disease Control and Prevention, Shenzhen, People's Republic of China; Guangdong Field Epidemiology Training Program, Guangzhou, People's Republic of China
| | - Yuanzhao Zhu
- 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
| | - Zhao Lei
- 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
| | - Jia Rui
- 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
| | - Shengnan Lin
- 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
| | - Yao Wang
- 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
| | - Jingwen Xu
- 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
| | - Xingchun Liu
- 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
| | - Meng 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
| | - Hongsheng Chen
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, People's Republic of China
| | - Xuemei Pan
- Lianzhou District Center for Disease Control and Prevention, Qingyuan, People's Republic of China
| | - Wentao Lu
- Qingyuan City Center for Disease Control and Prevention, Qingyuan, People's Republic of China
| | - Yuzhong Du
- Qingyuan City Center for Disease Control and Prevention, Qingyuan, People's Republic of China
| | - Hui Li
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, People's Republic of China
| | - Ling Fang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, People's Republic of China
| | - Meng Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, People's Republic of China
| | - Lina Zhou
- Department of Nephrology, The second Hospital of Xiamen Medical college, Xiamen 361021, China
| | - Fen Yang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 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.
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Agreement between two diagnostic methods for COVID-19: preliminary data from a Brazilian clinical laboratory. REV ROMANA MED LAB 2021. [DOI: 10.2478/rrlm-2021-0026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Abstract
Objective: To investigate possible differences between laboratory profiles of symptomatic and asymptomatic patients. There are different of them available for COVID-19 diagnoses and surveillance, so this research was to evaluate the positive agreement the diagnostic methods.
Methods: For symptomatic patients swab samples from nasal and oral mucosal were collected between first and second week after symptoms onset, to perform RT-PCR, blood samples were collected 7 days after to perform antibody detection test. For asymptomatic patients, only antibody detection was performed to confirm the infection. We investigated specific humoral immune response for symptomatic and asymptomatic patients and also analyzed the positivity index and kappa agreement between immunochromatographic and ELISA assays.
Results: Most symptomatic patients presented negative RT-PCR with IgM and IgA detection. Symptomatic and asymptomatic patients have presented elevated IgM and IgA immunoglobulins, being this detection higher in symptomatic patients. The positivity index for immunochromatographic was higher than ELISA and there was no kappa agreement between IgM and IgA detection between these two methods.
Conclusion: Symptomatic patients presented higher amounts of IgM and IgA than asymptomatic, suggesting a relation between antibody quantity and severity of disease. We verified no agreement between IgM and IgA detection, and observed higher positivity index for IMMUNO when compared to ELISA. The different kinetics may cause a variation in their detection. Also, many different virus proteins can be used as antigens in these methods, being able of altering their sensibility and specificity.
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Zhao Z, Chen Q, Wang Y, Chu M, Hu Q, Hannah MN, Rui J, Liu X, Yu Y, Zhao F, Ren Z, Yu S, An R, Pan L, Chiang YC, Zhao B, Su Y, Zhao B, Chen T. Relative transmissibility of shigellosis among different age groups: A modeling study in Hubei Province, China. PLoS Negl Trop Dis 2021; 15:e0009501. [PMID: 34111124 PMCID: PMC8219151 DOI: 10.1371/journal.pntd.0009501] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 06/22/2021] [Accepted: 05/24/2021] [Indexed: 11/24/2022] Open
Abstract
Shigellosis is a heavy disease burden in China especially in children aged under 5 years. However, the age-related factors involved in transmission of shigellosis are unclear. An age-specific Susceptible-Exposed-Infectious/Asymptomatic-Recovered (SEIAR) model was applied to shigellosis surveillance data maintained by Hubei Province Centers for Disease Control and Prevention from 2005 to 2017. The individuals were divided into four age groups (≤ 5 years, 6-24 years, 25-59 years, and ≥ 60 years). The effective reproduction number (Reff), including infectivity (RI) and susceptibility (RS) was calculated to assess the transmissibility of different age groups. From 2005 to 2017, 130,768 shigellosis cases were reported in Hubei Province. The SEIAR model fitted well with the reported data (P < 0.001). The highest transmissibility (Reff) was from ≤ 5 years to the 25-59 years (mean: 0.76, 95% confidence interval [CI]: 0.34-1.17), followed by from the 6-24 years to the 25-59 years (mean: 0.69, 95% CI: 0.35-1.02), from the ≥ 60 years to the 25-59 years (mean: 0.58, 95% CI: 0.29-0.86), and from the 25-59 years to 25-59 years (mean: 0.50, 95% CI: 0.21-0.78). The highest infectivity was in ≤ 5 years (RI = 1.71), and was most commonly transmitted to the 25-59 years (45.11%). The highest susceptibility was in the 25-59 years (RS = 2.51), and their most common source was the ≤ 5 years (30.15%). Furthermore, "knock out" simulation predicted the greatest reduction in the number of cases occurred by when cutting off transmission routes among ≤ 5 years and from 25-59 years to ≤ 5 years. Transmission in ≤ 5 years occurred mainly within the group, but infections were most commonly introduced by individuals in the 25-59 years. Infectivity was highest in the ≤ 5 years and susceptibility was highest in the 25-59 years. Interventions to stop transmission should be directed at these age groups.
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Affiliation(s)
- Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Qi Chen
- Hubei Provincial Center for Disease Control and Prevention, Wuhan City, Hubei Province, People’s Republic of China
| | - Yao Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Meijie Chu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Qingqing Hu
- Division of Public Health, School of Medicine, University of Utah, Presidents Circle, Salt Lake City, Utah, United States of America
| | - Mikah Ngwanguong Hannah
- Medical College, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Xingchun Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Yunhan Yu
- School of Statistics, Beijing Normal University, Beijing City, People’s Republic of China
| | - Fuwei Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Zhengyun Ren
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Shanshan Yu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Ran An
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Lili Pan
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Yi-Chen Chiang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 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, 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, Fujian Province, People’s Republic of China
| | - Bin Zhao
- Medical Insurance Office, Xiang’an Hospital of Xiamen University, Xiamen City, 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, Fujian Province, People’s Republic of China
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Zhao Q, Wang Y, Yang M, Li M, Zhao Z, Lu X, Shen B, Luan B, Zhao Y, Cao B, Yao L, Zhao B, Su Y, Chen T. Evaluating the effectiveness of measures to control the novel coronavirus disease 2019 in Jilin Province, China. BMC Infect Dis 2021; 21:245. [PMID: 33676420 PMCID: PMC7936873 DOI: 10.1186/s12879-021-05936-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 02/25/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Based on differences in populations and prevention and control measures, the spread of new coronary pneumonia in different countries and regions also differs. This study aimed to calculate the transmissibility of coronavirus disease 2019 (COVID-19), and to evaluate the effectiveness of measures to control the disease in Jilin Province, China. METHODS The data of reported COVID-19 cases were collected, including imported and local cases from Jilin Province as of March 14, 2019. A Susceptible-Exposed-Infectious-Asymptomatic-Recovered/Removed (SEIAR) model was developed to fit the data, and the effective reproduction number (Reff) was calculated at different stages in the province. Finally, the effectiveness of the measures was assessed. RESULTS A total of 97 COVID-19 infections were reported in Jilin Province, among which 45 were imported infections (including one asymptomatic infection) and 52 were local infections (including three asymptomatic infections). The model fit the reported data well (R2 = 0.593, P < 0.001). The Reff of COVID-19 before and after February 1, 2020 was 1.64 and 0.05, respectively. Without the intervention taken on February 1, 2020, the predicted cases would have reached a peak of 177,011 on October 22, 2020 (284 days from the first case). The projected number of cases until the end of the outbreak (on October 9, 2021) would have been 17,129,367, with a total attack rate of 63.66%. Based on the comparison between the predicted incidence of the model and the actual incidence, the comprehensive intervention measures implemented in Jilin Province on February 1 reduced the incidence of cases by 99.99%. Therefore, according to the current measures and implementation efforts, Jilin Province can achieve good control of the virus's spread. CONCLUSIONS COVID-19 has a moderate transmissibility in Jilin Province, China. The interventions implemented in the province had proven effective; increasing social distancing and a rapid response by the prevention and control system will help control the spread of the disease.
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Affiliation(s)
- Qinglong Zhao
- Jilin Provincial Center for Disease Control and Prevention, Changchun, Jilin Province 130062 People’s Republic of China
| | - Yao Wang
- 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 361102 People’s Republic of China
| | - Meng Yang
- 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 361102 People’s Republic of China
| | - Meina Li
- The First Hospital of Jilin University, Changchun, Jilin Province 130021 People’s Republic of China
| | - Zeyu Zhao
- 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 361102 People’s Republic of China
| | - Xinrong Lu
- Jilin Provincial Center for Disease Control and Prevention, Changchun, Jilin Province 130062 People’s Republic of China
| | - Bo Shen
- Jilin Provincial Center for Disease Control and Prevention, Changchun, Jilin Province 130062 People’s Republic of China
| | - Bo Luan
- Jilin Provincial Center for Disease Control and Prevention, Changchun, Jilin Province 130062 People’s Republic of China
| | - Yifei Zhao
- Jilin Provincial Center for Disease Control and Prevention, Changchun, Jilin Province 130062 People’s Republic of China
| | - Bonan Cao
- Jilin Provincial Center for Disease Control and Prevention, Changchun, Jilin Province 130062 People’s Republic of China
| | - Laishun Yao
- Jilin Provincial Center for Disease Control and Prevention, Changchun, Jilin Province 130062 People’s Republic of China
| | - Benhua Zhao
- 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 361102 People’s Republic of China
| | - Yanhua Su
- 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 361102 People’s Republic of China
| | - Tianmu Chen
- 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 361102 People’s Republic of China
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9
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Abstract
The article aims to estimate and forecast the transmissibility of shigellosis and explore the association of meteorological factors with shigellosis. The mathematical model named Susceptible–Exposed–Symptomatic/Asymptomatic–Recovered–Water/Food (SEIARW) was used to explore the feature of shigellosis transmission based on the data of Wuhan City, China, from 2005 to 2017. The study applied effective reproduction number (Reff) to estimate the transmissibility. Daily meteorological data from 2008 to 2017 were used to determine Spearman's correlation with reported new cases and Reff. The SEIARW model fit the data well (χ2 = 0.00046, p > 0.999). The simulation results showed that the reservoir-to-person transmission of the shigellosis route has been interrupted. The Reff would be reduced to a transmission threshold of 1.00 (95% confidence interval (CI) 0.82–1.19) in 2035. Reducing the infectious period to 11.25 days would also decrease the value of Reff to 0.99. There was a significant correlation between new cases of shigellosis and atmospheric pressure, temperature, wind speed and sun hours per day. The correlation coefficients, although statistically significant, were very low (<0.3). In Wuhan, China, the main transmission pattern of shigellosis is person-to-person. Meteorological factors, especially daily atmospheric pressure and temperature, may influence the epidemic of shigellosis.
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Rui J, Chen Q, Chen Q, Hu Q, Hannah MN, Zhao Z, Wang Y, Liu X, Lei Z, Yu S, Chiang YC, Zhao B, Su Y, Zhao B, Chen T. Feasibility of containing shigellosis in Hubei Province, China: a modelling study. BMC Infect Dis 2020; 20:643. [PMID: 32873241 PMCID: PMC7461149 DOI: 10.1186/s12879-020-05353-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 08/17/2020] [Indexed: 12/14/2022] Open
Abstract
Background The transmission features and the feasibility of containing shigellosis remain unclear among a population-based study in China. Methods A population–based Susceptible – Exposed – Infectious / Asymptomatic – Recovered (SEIAR) model was built including decreasing the infectious period (DIP) or isolation of shigellosis cases. We analyzed the distribution of the reported shigellosis cases in Hubei Province, China from January 2005 to December 2017, and divided the time series into several stages according to the heterogeneity of reported incidence during the period. In each stage, an epidemic season was selected for the modelling and assessing the effectiveness of DIP and case isolation. Results A total of 130,770 shigellosis cases were reported in Hubei Province. The median of Reff was 1.13 (range: 0.86–1.21), 1.10 (range: 0.91–1.13), 1.09 (range: 0.92–1.92), and 1.03 (range: 0.94–1.22) in 2005–2006 season, 2010–2011 season, 2013–2014 season, and 2016–2017 season, respectively. The reported incidence decreased significantly (trend χ2 = 8260.41, P < 0.001) among four stages. The incidence of shigellosis decreased sharply when DIP implemented in three scenarios (γ = 0.1, 0.1429, 0.3333) and when proportion of case isolation increased. Conclusions Year heterogeneity of reported shigellosis incidence exists in Hubei Province. It is feasible to contain the transmission by implementing DIP and case isolation.
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Affiliation(s)
- Jia Rui
- 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
| | - Qi Chen
- Hubei Provincial Center for Disease Control and Prevention, Wuhan City, Hubei Province, People's Republic of China
| | - Qiuping Chen
- Medical Insurance Office, Xiang'an Hospital of Xiamen University, Xiamen City, Fujian Province, People's Republic of China
| | - Qingqing Hu
- Division of Public Health, School of Medicine, University of Utah, 201 Presidents Circle, Salt Lake City, UT, 84112, USA
| | - Mikah Ngwanguong Hannah
- Medical College, Xiamen University, Xiamen City, Fujian Province, People's Republic of China
| | - Zeyu Zhao
- 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
| | - Yao Wang
- 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
| | - Xingchun Liu
- 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
| | - Zhao Lei
- 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
| | - Shanshan Yu
- 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
| | - Yi-Chen Chiang
- 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
| | - Benhua Zhao
- 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
| | - Yanhua Su
- 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
| | - Bin Zhao
- Laboratory Department, Xiang'an Hospital of Xiamen University, State Key Laboratory of Molecular Vaccinology and Molecular Diagnosis, Xiamen City, Fujian Province, People's Republic of China
| | - Tianmu Chen
- 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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Surveillance of the 'bud event of norovirus-associated gastroenteritis' in schools: does it work in the prevention of norovirus infection outbreaks in Shanghai? Epidemiol Infect 2020; 148:e104. [PMID: 32381124 PMCID: PMC7315464 DOI: 10.1017/s0950268820000965] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Outbreaks of norovirus-associated gastroenteritis have been reported in schools in recent decades in China. For early warning and response to infectious disease outbreaks, the Shanghai Infectious Diseases Bud Event Surveillance System (IDBESS) was established in 2016. Bud event is a term used for the early sign of a potential infectious disease outbreak in public settings when the first few cases appear. This study aimed to describe the epidemiological characteristics of Norovirus-associated gastroenteritis bud events from June 2016 to December 2017 and to understand factors influencing the severity of events. Data were extracted from the IDBESS, supplemented by field investigations and school absence surveillance. In total, 189 bud events of Norovirus-associated gastroenteritis were reported in schools and kindergartens, affecting 3827 individuals and 52.38% happened in primary schools. The attack rate of Norovirus-associated gastroenteritis was 3.82% on average in students in the affected schools. In each event, case numbers varied between 5 and 148, with a median of 16. The duration of bud events lasted for 2 days on average. School absence happened in 47.93% (1797/3749) of affected students and the average duration of absence was 3.07 days. It was found that a longer delay before reporting was associated with a longer-lasting duration of bud event (OR = 2.25, 95% CI: 1.65, 3.07). In conclusion, ascribed to the sensitive threshold for alerting and the timely field investigation, the surveillance of bud events of Norovirus-associated gastroenteritis is effective in the control of Norovirus infection among preschool children and students in Shanghai.
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Zhao ZY, Chen Q, Zhao B, Hannah MN, Wang N, Wang YX, Xuan XF, Rui J, Chu MJ, Yu SS, Wang Y, Liu XC, An R, Pan LL, Chiang YC, Su YH, Zhao BH, Chen TM. Relative transmissibility of shigellosis among male and female individuals: a modeling study in Hubei Province, China. Infect Dis Poverty 2020; 9:39. [PMID: 32299485 PMCID: PMC7162736 DOI: 10.1186/s40249-020-00654-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 03/30/2020] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Developing countries exhibit a high disease burden from shigellosis. Owing to the different incidences in males and females, this study aims to analyze the features involved in the transmission of shigellosis among male (subscript m) and female (subscript f) individuals using a newly developed sex-based model. METHODS The data of reported shigellosis cases were collected from the China Information System for Disease Control and Prevention in Hubei Province from 2005 to 2017. A sex-based Susceptible-Exposed-Infectious/Asymptomatic-Recovered (SEIAR) model was applied to explore the dataset, and a sex-age-based SEIAR model was applied in 2010 to explore the sex- and age-specific transmissions. RESULTS From 2005 to 2017, 130 770 shigellosis cases (including 73 981 male and 56 789 female cases) were reported in Hubei Province. The SEIAR model exhibited a significant fitting effect with the shigellosis data (P < 0.001). The median values of the shigellosis transmission were 2.3225 × 108 for SARmm (secondary attack rate from male to male), 2.5729 × 108 for SARmf, 2.7630 × 10-8 for SARfm, and 2.1061 × 10-8 for SARff. The top five mean values of the transmission relative rate in 2010 (where the subscript 1 was defined as male and age ≤ 5 years, 2 was male and age 6 to 59 years, 3 was male and age ≥ 60 years, 4 was female and age ≤ 5 years, 5 was female and age 6 to 59 years, and 6 was male and age ≥ 60 years) were 5.76 × 10-8 for β61, 5.32 × 10-8 for β31, 4.01 × 10-8 for β34, 7.52 × 10-9 for β62, and 6.04 × 10-9 for β64. CONCLUSIONS The transmissibility of shigellosis differed among male and female individuals. The transmissibility between the genders was higher than that within the genders, particularly female-to-male transmission. The most important route in children (age ≤ 5 years) was transmission from the elderly (age ≥ 60 years). Therefore, the greatest interventions should be applied in females and the elderly.
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Affiliation(s)
- Ze-Yu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Qi Chen
- Hubei Provincial Center for Disease Control and Prevention, Wuhan City, Hubei Province, People's Republic of China
| | - Bin Zhao
- Laboratory Department, Xiang'an Hospital of Xiamen University, State Key Laboratory of Molecular Vaccinology and Molecular Diagnosis, Xiamen, Fujian, People's Republic of China
| | - Mikah Ngwanguong Hannah
- Medical College, Xiamen University, Xiamen City, Fujian Province, People's Republic of China
| | - Ning Wang
- Respiratory Department, Shanghai General Hospital, Shanghai, People's Republic of China
| | - Yu-Xin Wang
- Department of Nephrology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, Fujian, People's Republic of China
| | - Xian-Fa Xuan
- Department of Nephrology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, Fujian, People's Republic of China
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Mei-Jie Chu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Shan-Shan Yu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Yao Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Xing-Chun Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Ran An
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Li-Li Pan
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Yi-Chen Chiang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China
| | - Yan-Hua Su
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China.
| | - Ben-Hua Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China.
| | - Tian-Mu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, 4221-117 South Xiang'an Road, Xiang'an District, Xiamen, Fujian Province, People's Republic of China.
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Chen Q, Rui J, Hu Q, Peng Y, Zhang H, Zhao Z, Tong Y, Wu Y, Su Y, Zhao B, Guan X, Chen T. Epidemiological characteristics and transmissibility of shigellosis in Hubei Province, China, 2005 - 2017. BMC Infect Dis 2020; 20:272. [PMID: 32264846 PMCID: PMC7136996 DOI: 10.1186/s12879-020-04976-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Accepted: 03/13/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Shigellosis is one of the main diarrhea diseases in developing countries. However, the transmissibility of shigellosis remains unclear. METHODS We used the dataset of shigellosis cases reported between January 2005 and December 2017, from Hubei Province, China. A mathematical model was developed based on the natural history and the transmission mechanism of the disease. By fitting the data using the model, transmission relative rate from person to person (b) and from reservoir to person (bw), and the effective reproduction number (Reff) were estimated. To simulate the contribution of b and bw during the transmission, we performed a "knock-out" simulation in four scenarios: A) b = 0 and bw = 0; B) b = 0; C) bw = 0; D) control (no intervention). RESULTS A total of 130,770 shigellosis cases were reported in Hubei province, among which 13 cases were dead. The median annual incidence was 19.96 per 100,000 persons (range: 5.99 per 100,000 persons - 29.47 per 100,000 persons) with a decreased trend (trend χ2 = 25,470.27, P < 0.001). The mean values of b and bw were 0.0898 (95% confidence interval [CI]: 0.0851-0.0946) and 1.1264 × 10- 9 (95% CI: 4.1123 × 10- 10-1.8416 × 10- 9), respectively. The "knock-out" simulation showed that the number of cases simulated by scenario A was almost the same as scenario B, and scenario C was almost the same as scenario D. The mean value of Reff of shigellosis was 1.19 (95% CI: 1.13-1.25) and decreased slightly with a Linear model until it decreased to an epidemic threshold of 0.99 (95% CI: 0.65-1.34) in 2029. CONCLUSIONS The incidence of shigellosis is still in high level. The transmissibility of the disease is low in Hubei Province. The transmission would be interrupted in the year of 2029.
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Affiliation(s)
- Qi Chen
- Hubei Provincial Center for Disease Control and Prevention, NO.6 Zhuodaoquan North Road, Hongshan District, Wuhan City, Hubei Province People’s Republic of China
| | - Jia Rui
- 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
| | - Qingqing Hu
- Division of Public Health, School of Medicine, University of Utah, 201 Presidents Circle, Salt Lake City, UT 84112 USA
| | - Ying Peng
- Wuhan Center for Disease Control and Prevention, Wuhan City, Hubei Province People’s Republic of China
| | - Hao Zhang
- Yichang Center for Disease Control and Prevention, Yichang City, Hubei Province People’s Republic of China
| | - Zeyu Zhao
- 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
| | - Yeqing Tong
- Hubei Provincial Center for Disease Control and Prevention, NO.6 Zhuodaoquan North Road, Hongshan District, Wuhan City, Hubei Province People’s Republic of China
| | - Yang Wu
- Hubei Provincial Center for Disease Control and Prevention, NO.6 Zhuodaoquan North Road, Hongshan District, Wuhan City, Hubei Province People’s Republic of China
| | - Yanhua Su
- 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
| | - Benhua Zhao
- 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
| | - Xuhua Guan
- Hubei Provincial Center for Disease Control and Prevention, NO.6 Zhuodaoquan North Road, Hongshan District, Wuhan City, Hubei Province People’s Republic of China
| | - Tianmu Chen
- 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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14
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Yi B, Chen Y, Ma X, Rui J, Cui JA, Wang H, Li J, Chan SF, Wang R, Ding K, Xie L, Zhang D, Jiao S, Lao X, Chiang YC, Su Y, Zhao B, Xu G, Chen T. Incidence dynamics and investigation of key interventions in a dengue outbreak in Ningbo City, China. PLoS Negl Trop Dis 2019; 13:e0007659. [PMID: 31415559 PMCID: PMC6711548 DOI: 10.1371/journal.pntd.0007659] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Revised: 08/27/2019] [Accepted: 07/24/2019] [Indexed: 11/19/2022] Open
Abstract
Background The reported incidence of dengue fever increased dramatically in recent years in China. This study aimed to investigate and to assess the effectiveness of intervention implemented in a dengue outbreak in Ningbo City, Zhejiang Province, China. Methods Data of a dengue outbreak were collected in Ningbo City in China by a field epidemiological survey according to a strict protocol and case definition. Serum specimens of all cases were collected for diagnosis and the virological characteristics were detected by using polymerase chain reaction (PCR) and gene sequencing. Vector surveillance was implemented during the outbreak to collect the larva and adult mosquito densities to calculate the Breteau Index (BI) and human biting rate (HBR), respectively. Data of monthly BI and light-trap density in 2018 were built to calculate the seasonality of the vector. A transmission mathematical model was developed to dynamic the incidence of the disease. The parameters of the model were estimated by the data of the outbreak and vector surveillance data in 2018. The effectiveness of the interventions implemented during the outbreak was assessed by the data and the modelling. Results From 11 August to 8 September, 2018, a dengue outbreak was reported with 27 confirmed cases in a population of 5536-people community (community A) of Ningbo City. Whole E gene sequences were obtained from 24 cases and were confirmed as dengue virus type 1 (DENV-1). The transmission source of the outbreak was origin from community B where a dengue case having the same E gene sequence was onset on 30 July. Aedes albopictus was the only vector species in the area. The value of BI and HBR was 57.5 and 12 per person per hour respectively on 18 August, 2018 and decreased dramatically after interventions. The transmission model fitted well (χ2 = 6.324, P = 0.388) with the reported cases data. With no intervention, the total simulated number of the cases would be 1728 with a total attack rate (TAR) of 31.21% (95%CI: 29.99%– 32.43%). Case isolation and larva control (LC) have almost the same TAR and duration of outbreak (DO) as no intervention. Different levels of reducing HBR (rHBR) had different effectiveness with TARs ranging from 1.05% to 31.21% and DOs ranging from 27 days to 102 days. Adult vector control (AVC) had a very low TAR and DO. “LC+AVC” had a similar TAR and DO as that of AVC. “rHBR100%+LC”, “rHBR100%+AVC”, “rHBR100%+LC+AVC” and “rHBR100%+LC+AVC+Iso” had the same effectiveness. Conclusions Without intervention, DENV-1 could be transmitted rapidly within a short period of time and leads to high attack rate in community in China. AVC or rHBR should be recommended as primary interventions to control rapid transmission of the dengue virus at the early stage of an outbreak. Dengue has led to heavy disease burden in China. The reported incidence of the disease increased dramatically in recent years and cases have expanded from southern to central and northern part of China. In this study, the findings include that DENV-1 can transmit rapidly with a short period of time and leads to high attack rate in community, and that rHBR or AVC should be recommended as primary interventions to control rapid transmission of dengue virus at the early stage of an outbreak. Therefore, dengue outbreak is at high risk in many areas in China because of the potential high receptivity (widely distribution of Ae. albopictus) and vulnerability (high frequency of the importation) of the transmission. The high transmissibility of the virus makes it hard and urgent to control the outbreak. Delayed intervention (larvae control or case isolation) is hard to show its effectiveness and the interventions without delay are strongly recommended. Bed net or mosquito repellents were encouraged to use in the community to reduce HBR, and space spraying of insecticides were recommended to control adult vector during the outbreak.
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Affiliation(s)
- Bo Yi
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo City, Zhejiang Province, People’s Republic of China
| | - Yi Chen
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo City, Zhejiang Province, People’s Republic of China
| | - Xiao Ma
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo City, Zhejiang Province, People’s Republic of China
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Jing-An Cui
- School of Science, Beijing University of Civil Engineering and Architecture, Beijing, People's Republic of China
| | - Haibin Wang
- Haishu District Center for Disease Control and Prevention, Ningbo City, Zhejiang Province, People’s Republic of China
| | - Jia Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People’s Republic of China
| | - Soi-Fan Chan
- Center for Disease Control and Prevention, Health Bureau, Macao SAR, People’s Republic of China
| | - Rong Wang
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo City, Zhejiang Province, People’s Republic of China
| | - Keqin Ding
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo City, Zhejiang Province, People’s Republic of China
| | - Lei Xie
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo City, Zhejiang Province, People’s Republic of China
| | - Dongliang Zhang
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo City, Zhejiang Province, People’s Republic of China
| | - Shuli Jiao
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo City, Zhejiang Province, People’s Republic of China
| | - Xuying Lao
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo City, Zhejiang Province, People’s Republic of China
| | - Yi-Chen Chiang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, 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, 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, Fujian Province, People’s Republic of China
| | - Guozhang Xu
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo City, Zhejiang 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, Fujian Province, People’s Republic of China
- * E-mail:
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15
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Wu H, Wu C, Lu Q, Ding Z, Xue M, Lin J. Evaluating the effects of control interventions and estimating the inapparent infections for dengue outbreak in Hangzhou, China. PLoS One 2019; 14:e0220391. [PMID: 31393899 PMCID: PMC6687121 DOI: 10.1371/journal.pone.0220391] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 07/15/2019] [Indexed: 11/19/2022] Open
Abstract
Background The number of dengue fever (DF) cases and the number of dengue outbreaks have increased in recent years in Zhejiang Province, China. An unexpected dengue outbreak was reported in Hangzhou in 2017 and caused more than one thousand dengue cases. This study was undertaken to evaluate the effectiveness of the actual control measures, estimate the proportion of inapparent infections during this outbreak and simulate epidemic development based on different levels of control measures taking inapparent infections into consideration. Methods The epidemic data of dengue cases in Hangzhou, Zhejiang Province, in 2017 and the number of the people exposed to the outbreaks were obtained from the China Information Network System of Disease Prevention and Control. The epidemic without intervention measures was used to estimate the unknown parameters. A susceptible-exposed-infectious/inapparent-recovered (SEIAR) model was used to estimate the effectiveness of the control interventions. The inapparent infections were also evaluated at the same time. Results In total, 1137 indigenous dengue cases were reported in Hangzhou in 2017. The number of indigenous dengue cases was estimated by the SEIAR model. This number was predicted to reach 6090 by the end of November 2, 2017, if no control measures were implemented. The total number of reported cases was reduced by 81.33% in contrast to the estimated incidence without intervention. The number of average daily inapparent cases was 10.18 times higher than the number of symptomatic cases. The earlier and more rigorously the vector control interventions were implemented, the more effective they were. The results showed that implementing vector control continuously for more than twenty days was more effective than every few days of implementation. Case isolation is not sufficient enough for epidemic control and only reduced the incidence by 38.10% in contrast to the estimated incidence without intervention, even if case isolation began seven days after the onset of the first case. Conclusions The practical control interventions in the outbreaks that occurred in Hangzhou City were highly effective. The proportion of inapparent infections was large, and it played an important role in transmitting the disease during this epidemic. Early, continuous and high efficacy vector control interventions are necessary to limit the development of a dengue epidemic. Timely diagnosis and case reporting are important in the intervention at an early stage of the epidemic.
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Affiliation(s)
- Haocheng Wu
- Zhejiang Province Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China
- Key Laboratory for Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Chen Wu
- Zhejiang Province Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China
| | - Qinbao Lu
- Zhejiang Province Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China
| | - Zheyuan Ding
- Zhejiang Province Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China
| | - Ming Xue
- Hangzhou Centre for Disease Control and Prevention, Hangzhou, Zhejiang, Province, China
| | - Junfen Lin
- Zhejiang Province Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China
- Key Laboratory for Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Hangzhou, Zhejiang Province, China
- * E-mail:
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16
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Transmissibility of Norovirus in Urban Versus Rural Households in a Large Community Outbreak in China. Epidemiology 2019; 29:675-683. [PMID: 29847497 DOI: 10.1097/ede.0000000000000855] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Norovirus is a leading cause of outbreaks of acute infectious gastroenteritis worldwide, yet its transmissibility within households and associated risk factors remain unknown in developing countries. METHODS Household, demographic, and clinical data were collected from a semi-urban area in south China where an outbreak occurred in the winter of 2014. Using a Bayesian modeling framework, we assessed the transmissibility and potential risk modifiers in both urban and rural households. RESULTS In urban apartment buildings, the secondary attack rates were 84% (95% credible interval [CI] = 60%, 96%) among households of size two and 29% (95% CI = 9.6%, 53%) in larger households. In the rural village, secondary attack rate estimates were lower than the urban setting, 13% (0.51%, 54%) for households of size two and 7.3% (0.38%, 27%) for larger households. Males were 31% (95% CI = 3%, 50%) less susceptible to the disease than female. Water disinfection with chlorine was estimated to reduce environmental risk of infection by 60% (95% CI = 26%, 82%), and case isolation was estimated to reduce person-to-person transmission by 65% (95% CI = 15%, 93%). Nausea and vomiting were not associated with household transmission. CONCLUSIONS Norovirus is highly contagious within households, in particular in small households in urban communities. Our results suggest that water disinfection and case isolation are associated with reduction of outbreaks in resource-limited communities.
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Zhang L, Li X, Wu R, Chen H, Liu J, Wang Z, Xing Y, Ishaq HM, Wang J, Yu P, Xu J, Ma C. A gastroenteritis outbreak associated with drinking water in a college in northwest China. JOURNAL OF WATER AND HEALTH 2018; 16:508-515. [PMID: 30067234 DOI: 10.2166/wh.2018.202] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
An acute gastroenteritis outbreak occurred at a private college in June 2014 in northwest China. This outbreak involved two teachers and 629 students (range: 17-27 years, average 21.3 years). The main symptoms included non-bloody watery diarrhea, stomach ache, nausea, and vomiting, and the duration of illness ranged from 1 to 7 days. Eight of 18 water samples were disqualified. Thirty-four norovirus (NoV) RNA-positive samples were identified from 48 stool-related samples (genotyping results: 13 GII, 13 GI and 8 GI + GII mixture). Fourteen NoV samples were successfully characterized for genotype, including two GII.6, five GI.6, four GI.3, and three GI.1. Enteropathogenic Escherichia coli (EPEC) and enteroadherent Escherichia coli (EAEC) DNA was detected from patient stool specimens and water samples from well one; two EAEC strains and one EPEC strain were isolated from patient stool specimens. The risk ratios (RRs) associated with wells one and two were 1.66 and 1.49, respectively, and the RR associated with living in north dormitory building one was 2.59. The patients' epidemiological characteristics, symptoms, and duration of illness indicated that NoV-contaminated water might be the origin of this outbreak, and RR analysis suggested that the two wells were linked to the outbreak.
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Affiliation(s)
- Lixia Zhang
- Xi'an Jiaotong University, No. 28, Xianning West Road, Xi'an, Shaanxi 710043, China; Shaanxi Provincial People's Hospital, No. 256, Youyi West Road, Xi'an, Shaanxi 710068, China
| | - Xiu'E Li
- Xi'an No. 1 Hospital, No. 30, Fenxiang, Xi'an, Shaanxi 710002, China
| | - Rui Wu
- Xi'an Center for Disease Control and Prevention, No. 599, Xiying Road, Xi'an, Shaanxi 710054, China E-mail:
| | - Hailong Chen
- Xi'an Center for Disease Control and Prevention, No. 599, Xiying Road, Xi'an, Shaanxi 710054, China E-mail:
| | - Jifeng Liu
- Xi'an Center for Disease Control and Prevention, No. 599, Xiying Road, Xi'an, Shaanxi 710054, China E-mail:
| | - Zengguo Wang
- Xi'an Center for Disease Control and Prevention, No. 599, Xiying Road, Xi'an, Shaanxi 710054, China E-mail:
| | - Yuan Xing
- Xi'an Center for Disease Control and Prevention, No. 599, Xiying Road, Xi'an, Shaanxi 710054, China E-mail:
| | - Hafiz Muhammad Ishaq
- Xi'an Jiaotong University, No. 28, Xianning West Road, Xi'an, Shaanxi 710043, China
| | - Jingjun Wang
- Shaanxi Center for Disease Control and Prevention, No. 3, Jiandong Street, Xi'an, Shaanxi 710054, China
| | - Pengbo Yu
- Shaanxi Center for Disease Control and Prevention, No. 3, Jiandong Street, Xi'an, Shaanxi 710054, China
| | - Jiru Xu
- Xi'an Jiaotong University, No. 28, Xianning West Road, Xi'an, Shaanxi 710043, China
| | - Chaofeng Ma
- Xi'an Center for Disease Control and Prevention, No. 599, Xiying Road, Xi'an, Shaanxi 710054, China E-mail:
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18
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Chen TM, Zhang SS, Feng J, Xia ZG, Luo CH, Zeng XC, Guo XR, Lin ZR, Zhou HN, Zhou SS. Mobile population dynamics and malaria vulnerability: a modelling study in the China-Myanmar border region of Yunnan Province, China. Infect Dis Poverty 2018; 7:36. [PMID: 29704895 PMCID: PMC5924679 DOI: 10.1186/s40249-018-0423-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Accepted: 04/10/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The China-Myanmar border region presents a great challenge in malaria elimination in China, and it is essential to understand the relationship between malaria vulnerability and population mobility in this region. METHODS A community-based, cross-sectional survey was performed in five villages of Yingjiang county during September 2016. Finger-prick blood samples were obtained to identify asymptomatic infections, and imported cases were identified in each village (between January 2013 and September 2016). A stochastic simulation model (SSM) was used to test the relationship between population mobility and malaria vulnerability, according to the mechanisms of malaria importation. RESULTS Thirty-two imported cases were identified in the five villages, with a 4-year average of 1 case/year (range: 0-5 cases/year). No parasites were detected in the 353 blood samples from 2016. The median density of malaria vulnerability was 0.012 (range: 0.000-0.033). The average proportion of mobile members of the study population was 32.56% (range: 28.38-71.95%). Most mobile individuals lived indoors at night with mosquito protection. The SSM model fit the investigated data (χ2 = 0.487, P = 0.485). The average probability of infection in the members of the population that moved to Myanmar was 0.011 (range: 0.0048-0.1585). The values for simulated vulnerability increased with greater population mobility in each village. CONCLUSIONS A high proportion of population mobility was associated with greater malaria vulnerability in the China-Myanmar border region. Mobile population-specific measures should be used to decrease the risk of malaria re-establishment in China.
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Affiliation(s)
- Tian-Mu Chen
- Department of Malaria, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.,WHO Collaborating Centre for Tropical Diseases, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.,National Center for International Research on Tropical Diseases, Ministry of Science and Technology, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.,Key Laboratory of Parasite and Vector Biology, Ministry of Health, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China
| | - Shao-Sen Zhang
- Department of Malaria, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.,WHO Collaborating Centre for Tropical Diseases, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.,National Center for International Research on Tropical Diseases, Ministry of Science and Technology, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.,Key Laboratory of Parasite and Vector Biology, Ministry of Health, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China
| | - Jun Feng
- Department of Malaria, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.,WHO Collaborating Centre for Tropical Diseases, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.,National Center for International Research on Tropical Diseases, Ministry of Science and Technology, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.,Key Laboratory of Parasite and Vector Biology, Ministry of Health, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China
| | - Zhi-Gui Xia
- Department of Malaria, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.,WHO Collaborating Centre for Tropical Diseases, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.,National Center for International Research on Tropical Diseases, Ministry of Science and Technology, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.,Key Laboratory of Parasite and Vector Biology, Ministry of Health, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China
| | - Chun-Hai Luo
- Yunnan Institute of Parasitic Diseases, Puer, People's Republic of China
| | - Xu-Can Zeng
- Yunnan Institute of Parasitic Diseases, Puer, People's Republic of China
| | - Xiang-Rui Guo
- Yingjiang County Center for Disease Control and Prevention, Dehong, People's Republic of China
| | - Zu-Rui Lin
- Yunnan Institute of Parasitic Diseases, Puer, People's Republic of China
| | - Hong-Ning Zhou
- Yunnan Institute of Parasitic Diseases, Puer, People's Republic of China
| | - Shui-Sen Zhou
- Department of Malaria, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China. .,WHO Collaborating Centre for Tropical Diseases, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China. .,National Center for International Research on Tropical Diseases, Ministry of Science and Technology, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China. .,Key Laboratory of Parasite and Vector Biology, Ministry of Health, 207 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.
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19
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Chen T, Zhao B, Liu R, Zhang X, Xie Z, Chen S. Simulation of key interventions for seasonal influenza outbreak control at school in Changsha, China. J Int Med Res 2018; 48:300060518764268. [PMID: 29569977 PMCID: PMC7113490 DOI: 10.1177/0300060518764268] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
Objective To use a mathematical model to simulate an influenza outbreak in a school in
order to assess the effectiveness of isolation (Iso), antiviral
therapeutics, antiviral prophylactics (P), vaccination prior to the
outbreak, and school closure (for 1 [S1w], 2 or 3 weeks). Methods This study developed a susceptible–exposed–infectious/asymptomatic–recovered
model to estimate the effectiveness of commonly used interventions for
seasonal influenza outbreaks in school. Results The most effective single-intervention strategy was isolation with a total
attack rate of 1.99% and an outbreak duration of 30 days. The additional
effectiveness of antiviral therapeutics and prophylactics and vaccination
(prior to the outbreak) strategies were not obvious. Although Iso+P,
P+Iso+S1w, four-, and five-combined intervention strategies had commendable
effectiveness, total attack rate decreased only slightly, and outbreak
duration was shortened by 9 days maximum, compared with the
single-intervention isolation strategy. School closure for 1, 2 or 3 weeks
was futile or even counterproductive. Conclusion Isolation, as a single intervention, was the most effective in terms of
reducing the total attack rate and the duration of the outbreak.
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Affiliation(s)
- Tianmu Chen
- Changsha Centre for Disease Control and Prevention, Changsha, Hunan Province, China
| | - Bin Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian Province, China
| | - Ruchun Liu
- Changsha Centre for Disease Control and Prevention, Changsha, Hunan Province, China
| | - Xixing Zhang
- Changsha Centre for Disease Control and Prevention, Changsha, Hunan Province, China
| | - Zhi Xie
- Changsha Centre for Disease Control and Prevention, Changsha, Hunan Province, China
| | - Shuilian Chen
- Changsha Centre for Disease Control and Prevention, Changsha, Hunan Province, China
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Xu W, Chen T, Dong X, Kong M, Lv X, Li L. Outbreak detection and evaluation of a school-based influenza-like-illness syndromic surveillance in Tianjin, China. PLoS One 2017; 12:e0184527. [PMID: 28886143 PMCID: PMC5590954 DOI: 10.1371/journal.pone.0184527] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 08/25/2017] [Indexed: 11/23/2022] Open
Abstract
School-based influenza-like-illness (ILI) syndromic surveillance can be an important part of influenza community surveillance by providing early warnings for outbreaks and leading to a fast response. From September 2012 to December 2014, syndromic surveillance of ILI was carried out in 4 county-level schools. The cumulative sum methods(CUSUM) was used to detect abnormal signals. A susceptible-exposed-infectious/asymptomatic-recovered (SEIAR) model was fit to the influenza outbreak without control measures and compared with the actual influenza outbreak to evaluate the effectiveness of early control efforts. The ILI incidence rates in 2014 (14.51%) was higher than the incidence in 2013 (5.27%) and 2012 (3.59%). Ten school influenza outbreaks were detected by CUSUM. Each outbreak had high transmissibility with a median Runc of 4.62. The interventions in each outbreak had high effectiveness and all Rcon were 0. The early intervention had high effectiveness within the school-based ILI syndromic surveillance. Syndromic surveillance within schools can play an important role in controlling influenza outbreaks.
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Affiliation(s)
- Wenti Xu
- Tianjin Centers for Disease Control and Prevention, Tianjin, China
- * E-mail:
| | - Tianmu Chen
- Changsha Center for Disease Control and Prevention, Changsha, China
| | - Xiaochun Dong
- Tianjin Centers for Disease Control and Prevention, Tianjin, China
| | - Mei Kong
- Tianjin Centers for Disease Control and Prevention, Tianjin, China
| | - Xiuzhi Lv
- Hangu Center for Disease Control and Prevention, Binhai New Area, Tianjin, China
| | - Lin Li
- Tianjin Centers for Disease Control and Prevention, Tianjin, China
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