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Zhang M, Huang JF, Kang M, Liu XC, Lin HY, Zhao ZY, Ye GQ, Lin SN, Rui J, Xu JW, Zhu YZ, Wang Y, Yang M, Tang SX, Cheng Q, Chen TM. Epidemiological Characteristics and the Dynamic Transmission Model of Dengue Fever in Zhanjiang City, Guangdong Province in 2018. Trop Med Infect Dis 2022; 7:tropicalmed7090209. [PMID: 36136620 PMCID: PMC9501079 DOI: 10.3390/tropicalmed7090209] [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: 07/22/2022] [Revised: 08/14/2022] [Accepted: 08/15/2022] [Indexed: 11/16/2022] Open
Abstract
Background: With the progress of urbanization, the mobility of people has gradually increased, which has led to the further spread of dengue fever. This study evaluated the transmissibility of dengue fever within districts and between different districts in Zhanjiang City to provide corresponding advice for cross-regional prevention and control. Methods: A mathematical model of transmission dynamics was developed to explore the transmissibility of the disease and to compare that between different regions. Results: A total of 467 DF cases (6.38 per 100,000 people) were reported in Zhanjiang City in 2018. In the model, without any intervention, the number of simulated cases in this epidemic reached about 950. The dengue fever transmissions between districts varied within and between regions. When the spread of dengue fever from Chikan Districts to other districts was cut off, the number of cases in other districts dropped significantly or even to zero. When the density of mosquitoes in Xiashan District was controlled, the dengue fever epidemic in Xiashan District was found to be significantly alleviated. Conclusions: When there is a dengue outbreak, timely measures can effectively control it from developing into an epidemic. Different prevention and control measures in different districts could efficiently reduce the risk of disease transmission.
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Affiliation(s)
- Meng Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Jie-Feng Huang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Min Kang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
- School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Xing-Chun Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Hong-Yan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Ze-Yu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Guo-Qiang Ye
- Zhanjiang Municipal Center for Disease Control and Prevention, Zhanjiang 524037, China
| | - Sheng-Nan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Jing-Wen Xu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Yuan-Zhao Zhu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Yao Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Meng Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Shi-Xing Tang
- School of Public Health, Southern Medical University, Guangzhou 510515, China
- Correspondence: (S.-X.T.); (Q.C.); (T.-M.C.); Tel.: +1-4242489768 (Q.C.); +86-13661934715 (T.-M.C.)
| | - Qu Cheng
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA 94704, USA
- Correspondence: (S.-X.T.); (Q.C.); (T.-M.C.); Tel.: +1-4242489768 (Q.C.); +86-13661934715 (T.-M.C.)
| | - Tian-Mu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
- Correspondence: (S.-X.T.); (Q.C.); (T.-M.C.); Tel.: +1-4242489768 (Q.C.); +86-13661934715 (T.-M.C.)
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Chen J, Ding RL, Liu KK, Xiao H, Hu G, Xiao X, Yue Q, Lu JH, Han Y, Bu J, Dong GH, Lin Y. Collaboration between meteorology and public health: Predicting the dengue epidemic in Guangzhou, China, by meteorological parameters. Front Cell Infect Microbiol 2022; 12:881745. [PMID: 36017372 PMCID: PMC9397942 DOI: 10.3389/fcimb.2022.881745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/08/2022] [Indexed: 12/02/2022] Open
Abstract
Background Dengue has become an increasing public health threat around the world, and climate conditions have been identified as important factors affecting the transmission of dengue, so this study was aimed to establish a prediction model of dengue epidemic by meteorological methods. Methods The dengue case information and meteorological data were collected from Guangdong Provincial Center for Disease Prevention and Control and Guangdong Meteorological Bureau, respectively. We used spatio-temporal analysis to characterize dengue epidemics. Spearman correlation analysis was used to analyze the correlation between lagged meteorological factors and dengue fever cases and determine the maximum lagged correlation coefficient of different meteorological factors. Then, Generalized Additive Models were used to analyze the non-linear influence of lagged meteorological factors on local dengue cases and to predict the number of local dengue cases under different weather conditions. Results We described the temporal and spatial distribution characteristics of dengue fever cases and found that sporadic single or a small number of imported cases had a very slight influence on the dengue epidemic around. We further created a forecast model based on the comprehensive consideration of influence of lagged 42-day meteorological factors on local dengue cases, and the results showed that the forecast model has a forecast effect of 98.8%, which was verified by the actual incidence of dengue from 2005 to 2016 in Guangzhou. Conclusion A forecast model for dengue epidemic was established with good forecast effects and may have a potential application in global dengue endemic areas after modification according to local meteorological conditions. High attention should be paid on sites with concentrated patients for the control of a dengue epidemic.
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Affiliation(s)
- Jing Chen
- School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
- Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China
| | - Rui-Lian Ding
- Hospital for Skin Diseases (Institute of Dermatology), Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
| | - Kang-Kang Liu
- Department of Research Center for Medicine, the Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Hui Xiao
- Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China
| | - Gang Hu
- School of Agriculture, Sun Yat-sen University, Guangzhou, China
| | - Xiang Xiao
- Department of Geography, Hong Kong Baptist University, Hong Kong, China
| | - Qian Yue
- Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China
| | - Jia-Hai Lu
- NMPA Key Laboratory for Quality Monitoring and Evaluation of Vaccines and Biological Products, Sun Yat-sen University, Guangzhou, China
| | - Yan Han
- Hospital for Skin Diseases (Institute of Dermatology), Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
| | - Jin Bu
- Hospital for Skin Diseases (Institute of Dermatology), Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China
- *Correspondence: Jin Bu, ; Guang-Hui Dong, ; Yu Lin,
| | - Guang-Hui Dong
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Jin Bu, ; Guang-Hui Dong, ; Yu Lin,
| | - Yu Lin
- Guangzhou South China Biomedical Research Institute co., Ltd, Guangzhou, China
- Shenzhen Withsum Technology Limited, Shenzhen, China
- *Correspondence: Jin Bu, ; Guang-Hui Dong, ; Yu Lin,
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Shrinkage in serial intervals across transmission generations of COVID-19. J Theor Biol 2021; 529:110861. [PMID: 34390731 PMCID: PMC8356772 DOI: 10.1016/j.jtbi.2021.110861] [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: 03/13/2021] [Revised: 08/06/2021] [Accepted: 08/08/2021] [Indexed: 11/23/2022]
Abstract
One of the key epidemiological characteristics that shape the transmission of coronavirus disease 2019 (COVID-19) is the serial interval (SI). Although SI is commonly considered following a probability distribution at a population scale, recent studies reported a slight shrinkage (or contraction) of the mean of effective SI across transmission generations or over time. Here, we develop a likelihood-based statistical inference framework with truncation to explore the change in SI across transmission generations after adjusting the impacts of case isolation. The COVID-19 contact tracing surveillance data in Hong Kong are used for exemplification. We find that for COVID-19, the mean of individual SI is likely to shrink with a factor at 0.72 per generation (95%CI: 0.54, 0.96) as the transmission generation increases, where a threshold may exist as the lower boundary of this shrinking process. We speculate that one of the probable explanations for the shrinkage in SI might be an outcome due to the competition among multiple candidate infectors within the same case cluster. Thus, the nonpharmaceutical interventive strategies are crucially important to block the transmission chains, and mitigate the COVID-19 epidemic.
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Liu X, Liu K, Yue Y, Wu H, Yang S, Guo Y, Ren D, Zhao N, Yang J, Liu Q. Determination of Factors Affecting Dengue Occurrence in Representative Areas of China: A Principal Component Regression Analysis. Front Public Health 2021; 8:603872. [PMID: 33537277 PMCID: PMC7848178 DOI: 10.3389/fpubh.2020.603872] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 12/10/2020] [Indexed: 11/13/2022] Open
Abstract
Background: Determination of the key factors affecting dengue occurrence is of significant importance for the successful response to its outbreak. Yunnan and Guangdong Provinces in China are hotspots of dengue outbreak during recent years. However, few studies focused on the drive of multi-dimensional factors on dengue occurrence failing to consider the possible multicollinearity of the studied factors, which may bias the results. Methods: In this study, multiple linear regression analysis was utilized to explore the effect of multicollinearity among dengue occurrences and related natural and social factors. A principal component regression (PCR) analysis was utilized to determine the key dengue-driven factors in Guangzhou city of Guangdong Province and Xishuangbanna prefecture of Yunnan Province, respectively. Results: The effect of multicollinearity existed in both Guangzhou city and Xishuangbanna prefecture, respectively. PCR model revealed that the top three contributing factors to dengue occurrence in Guangzhou were Breteau Index (BI) (positive correlation), the number of imported dengue cases lagged by 1 month (positive correlation), and monthly average of maximum temperature lagged by 1 month (negative correlation). In contrast, the top three factors contributing to dengue occurrence in Xishuangbanna included monthly average of minimum temperature lagged by 1 month (positive correlation), monthly average of maximum temperature (positive correlation), monthly average of relative humidity (positive correlation), respectively. Conclusion: Meteorological factors presented stronger impacts on dengue occurrence in Xishuangbanna, Yunnan, while BI and the number of imported cases lagged by 1 month played important roles on dengue transmission in Guangzhou, Guangdong. Our findings could help to facilitate the formulation of tailored dengue response mechanism in representative areas of China in the future.
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Affiliation(s)
- Xiaobo Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, WHO Collaborating Centre for Vector Surveillance and Management, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Keke Liu
- Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yujuan Yue
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, WHO Collaborating Centre for Vector Surveillance and Management, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Haixia Wu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, WHO Collaborating Centre for Vector Surveillance and Management, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Shu Yang
- The Collaboration Unit for Field Epidemiology of State Key Laboratory of Infectious Disease Prevention and Control, Nanchang Center for Disease Control and Prevention, Nanchang, China
| | - Yuhong Guo
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, WHO Collaborating Centre for Vector Surveillance and Management, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Dongsheng Ren
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, WHO Collaborating Centre for Vector Surveillance and Management, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Ning Zhao
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, WHO Collaborating Centre for Vector Surveillance and Management, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jun Yang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, WHO Collaborating Centre for Vector Surveillance and Management, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
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Liu X, Zhang M, Cheng Q, Zhang Y, Ye G, Huang X, Zhao Z, Rui J, Hu Q, Frutos R, Chen T, Song T, Kang M. Dengue fever transmission between a construction site and its surrounding communities in China. Parasit Vectors 2021; 14:22. [PMID: 33407778 PMCID: PMC7787407 DOI: 10.1186/s13071-020-04463-x] [Citation(s) in RCA: 6] [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: 06/20/2020] [Accepted: 11/05/2020] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Due to an increase in mosquito habitats and the lack facilities to carry out basic mosquito control, construction sites in China are more likely to experience secondary dengue fever infection after importation of an initial infection, which may then increase the number of infections in the neighboring communities and the chance of community transmission. The aim of this study was to investigate how to effectively reduce the transmission of dengue fever at construction sites and the neighboring communities. METHODS The Susceptible-Exposed-Infectious/Asymptomatic-Recovered (SEIAR) model of human and SEI model of mosquitoes were developed to estimate the transmission of dengue virus between humans and mosquitoes within the construction site and within a neighboring community, as well between each of these. With the calibrated model, we further estimated the effectiveness of different intervention scenarios targeting at reducing the transmissibility at different locations (i.e. construction sites and community) with the total attack rate (TAR) and the duration of the outbreak (DO). RESULTS A total of 102 construction site-related and 131 community-related cases of dengue fever were reported in our area of study. Without intervention, the number of cases related to the construction site and the community rose to 156 (TAR: 31.25%) and 10,796 (TAR: 21.59%), respectively. When the transmission route from mosquitoes to humans in the community was cut off, the number of community cases decreased to a minimum of 33 compared with other simulated scenarios (TAR: 0.068%, DO: 60 days). If the transmission route from infectious mosquitoes in the community and that from the construction site to susceptible people on the site were cut off at the same time, the number of cases on the construction site dropped to a minimum of 74 (TAR: 14.88%, DO: 66 days). CONCLUSIONS To control the outbreak of dengue fever effectively on both the construction site and in the community, interventions needed to be made both within the community and from the community to the construction site. If interventions only took place within the construction site, the number of cases on the construction site would not be reduced. Also, interventions implemented only within the construction site or between the construction site and the community would not lead to a reduction in the number of cases in the community.
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Affiliation(s)
- Xingchun Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian People’s Republic of China
| | - Meng Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong People’s Republic of China
| | - Qu Cheng
- Division of Environmental Health Sciences School of Public Health, University of California, Berkeley, CA 94720 USA
| | - Yingtao Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong People’s Republic of China
| | - Guoqiang Ye
- Zhanjiang Municipal Center for Disease Control and Prevention, Zhanjiang, Guangdong People’s Republic of China
| | - Xiqing Huang
- Zhanjiang Municipal Center for Disease Control and Prevention, Zhanjiang, Guangdong People’s Republic of China
| | - Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, 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, Fujian 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
| | | | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian People’s Republic of China
| | - Tie Song
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong People’s Republic of China
| | - Min Kang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong People’s Republic of China
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Wu D, Zhang X, Deng A, Zhang H, Zhang Y, Tan Q, Peng Z, Li J, Song T. Dengue Fever Outbreaks Caused by Varied Serotype Dengue Virus - Guangdong Province, China, 2019. China CDC Wkly 2020; 2:740-743. [PMID: 34594750 PMCID: PMC8422221 DOI: 10.46234/ccdcw2020.137] [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: 12/09/2019] [Accepted: 04/16/2020] [Indexed: 11/14/2022] Open
Abstract
What is already known about this topic? Dengue fever (DF) outbreaks affect hundreds of millions of people worldwide and have increased significantly in Guangdong Province in 2019. What is added by this report? This paper described briefly DF outbreaks were attributed to several types of dengue virus (DENV) including DENV-1, DENV-2, and DENV-3 in 2019 in Guangdong, tracked the sources of viruses through phylogenetic analysis and epidemiological investigation, and primarily revealed the epidemiological links among the outbreaks. What are the implications for public health practice? The introduction of DENV from DF endemic areas increased pressure on the prevention and control of DF in Guangdong. Early detection of suspected cases and typing and genotyping of circulating viruses should be prioritized and enhanced to promptly assess the likelihood of local transmission, of introduction, and of subsequent sustained local transmission of the virus to implement optimal prevention and control strategies and measures.
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Affiliation(s)
- De Wu
- Institute of Pathogenic Microbiology, Center for Disease Control and Prevention of Guangdong, Guangzhou, Guangdong, China
| | - Xin Zhang
- Institute of Pathogenic Microbiology, Center for Disease Control and Prevention of Guangdong, Guangzhou, Guangdong, China
| | - Aiping Deng
- Institute for Prevention and Control of Infectious Diseases, Center for Disease Control and Prevention of Guangdong, Guangzhou, Guangdong, China
| | - Huan Zhang
- Institute of Pathogenic Microbiology, Center for Disease Control and Prevention of Guangdong, Guangzhou, Guangdong, China
| | - Yintao Zhang
- Institute for Prevention and Control of Infectious Diseases, Center for Disease Control and Prevention of Guangdong, Guangzhou, Guangdong, China
| | - Qiqi Tan
- Institute of Pathogenic Microbiology, Center for Disease Control and Prevention of Guangdong, Guangzhou, Guangdong, China
| | - Zhiqiang Peng
- Institute for Prevention and Control of Infectious Diseases, Center for Disease Control and Prevention of Guangdong, Guangzhou, Guangdong, China
| | - Jiandong Li
- Institute for Viral Disease Control and Prevention. Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tie Song
- Institute for Prevention and Control of Infectious Diseases, Center for Disease Control and Prevention of Guangdong, Guangzhou, Guangdong, China
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Zhao S, Tang X, Liang X, Chong MKC, Ran J, Musa SS, Yang G, Cao P, Wang K, Zee BCY, Wang X, He D, Wang MH. Modelling the Measles Outbreak at Hong Kong International Airport in 2019: A Data-Driven Analysis on the Effects of Timely Reporting and Public Awareness. Infect Drug Resist 2020; 13:1851-1861. [PMID: 32606834 PMCID: PMC7308762 DOI: 10.2147/idr.s258035] [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] [Received: 04/13/2020] [Accepted: 05/20/2020] [Indexed: 11/24/2022] Open
Abstract
Background Measles, a highly contagious disease, still poses a huge burden worldwide. Lately, a trend of resurgence threatened the developed countries. A measles outbreak occurred in the Hong Kong International Airport (HKIA) between March and April 2019, which infected 29 airport staff. During the outbreak, multiple measures were taken including daily situation updates, setting up a public enquiry platform on March 23, and an emergent vaccination program targeting unprotected staff. The outbreak was put out promptly. The effectiveness of these measures was unclear. Methods We quantified the transmissibility of outbreak in HKIA by the effective reproduction number, Reff(t), and basic reproduction number, R0(t). The reproduction number was modelled as a function of its determinants that were statistically examined, including lags in hospitalization, situation update, and level of public awareness. Then, we considered a hypothetical no-measure scenario when improvements in reporting and public enquiry were absent and calculated the number of infected airport staff. Results Our estimated average R0 is 10.09 (95% CI: 1.73−36.50). We found that R0(t) was positively associated with lags in hospitalization and situation update, while negatively associated with the level of public awareness. The average predicted basic reproduction number, r0, was 14.67 (95% CI: 9.01−45.32) under the no-measure scenario, which increased the average R0 by 77.57% (95% CI: 1.71−111.15). The total number of infected staff would be 179 (IQR: 90−339, 95% CI: 23−821), namely the measure induced 8.42-fold (95% CI: 0.21−42.21) reduction in the total number of infected staff. Conclusion Timely reporting on outbreak situation and public awareness measured by the number of public enquiries helped to control the outbreak.
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Affiliation(s)
- Shi Zhao
- Division of Biostatistics, JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, People's Republic of China.,Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, People's Republic of China
| | - Xiujuan Tang
- Shenzhen Center for Disease Control and Prevention, Shenzhen, People's Republic of China
| | - Xue Liang
- Department of Hematology, The 989th Hospital of the Joint Logistics Support Force of Chinese PLA, Luoyang 471031, People's Republic of China
| | - Marc K C Chong
- Division of Biostatistics, JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, People's Republic of China.,Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, People's Republic of China
| | - Jinjun Ran
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, People's Republic of China
| | - Salihu S Musa
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, People's Republic of China
| | - Guangpu Yang
- Department of Orthopaedics and Traumatology, Chinese University of Hong Kong, Hong Kong, People's Republic of China
| | - Peihua Cao
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Kai Wang
- Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, People's Republic of China
| | - Benny C Y Zee
- Division of Biostatistics, JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, People's Republic of China.,Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, People's Republic of China
| | - Xin Wang
- Shenzhen Center for Disease Control and Prevention, Shenzhen, People's Republic of China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, People's Republic of China
| | - Maggie H Wang
- Division of Biostatistics, JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, People's Republic of China.,Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, People's Republic of China
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8
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Xiong Y, Luo Y, Li H, Wu W, Ruan X, Mu X. Rapid visual detection of dengue virus by combining reverse transcription recombinase-aided amplification with lateral-flow dipstick assay. Int J Infect Dis 2020; 95:406-412. [PMID: 32272263 DOI: 10.1016/j.ijid.2020.03.075] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 03/26/2020] [Accepted: 03/26/2020] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVES Dengue caused by infection with the dengue virus (DENV) is endemic in the tropical and subtropical regions of the world and of greatest public health concern. With more large outbreaks in rural areas, the purpose of this study was to develop a point-of-care test using recombinase-aided amplification and lateral-flow dipsticks for rapidly detecting DENV in low-resource settings. METHODS The primers for the recombinase-aided amplification (RAA) assay were designed based on 3' UTR of the DENV genome and screened. The RAA temperature, time and the concentration of primers were then optimized, as well as the lateral-flow dipstick assay (LFD) time. Finally, the diagnostic performance of the reverse transcription (RT)-RAA-LFD assay was evaluated using blood samples from 247 patients who were clinically suspected to be infected with DENV. RESULTS The RAA primer pair F1/R2 was the optimal combination for detecting DENV. The RT-RAA was performed in an incubator block at 37°C for 20minutes, and the amplicons were visible in the flow dipsticks from a naked eye within 3minutes. The detection limit of the developed RT-RAA-LFD assay was 10 copies/μL with high specificity for DENV. Compared with commercial reverse transcription quantitative PCR assay, the kappa value of RT-RAA-LFD in the 247 clinical samples was 0.957. CONCLUSIONS In this study, a rapid and visual point-of-care test based on RT-RAA and LFD assay was developed. It was found to be suitable for reliable detection of DENV in low-resource settings with limited laboratory capabilities and optimal storage conditions.
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Affiliation(s)
- Yufeng Xiong
- Department of Clinical laboratory, Guangdong Women and Children Hospital, Guangzhou, Guangdong, China
| | - Yasha Luo
- Department of Clinical laboratory, Guangdong Women and Children Hospital, Guangzhou, Guangdong, China
| | - Huan Li
- Department of Clinical laboratory, Guangdong Women and Children Hospital, Guangzhou, Guangdong, China
| | - Weixiang Wu
- Department of Clinical laboratory, Guangdong Women and Children Hospital, Guangzhou, Guangdong, China
| | - Xiaolin Ruan
- Department of Clinical laboratory, Guangdong Women and Children Hospital, Guangzhou, Guangdong, China
| | - Xiaoping Mu
- Department of Clinical laboratory, Guangdong Women and Children Hospital, Guangzhou, Guangdong, China.
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He D, Zhao S, Lin Q, Musa SS, Stone L. New estimates of the Zika virus epidemic attack rate in Northeastern Brazil from 2015 to 2016: A modelling analysis based on Guillain-Barré Syndrome (GBS) surveillance data. PLoS Negl Trop Dis 2020; 14:e0007502. [PMID: 32348302 PMCID: PMC7213748 DOI: 10.1371/journal.pntd.0007502] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 05/11/2020] [Accepted: 03/16/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Between January 2015 and August 2016, two epidemic waves of Zika virus (ZIKV) disease swept the Northeastern (NE) region of Brazil. As a result, two waves of Guillain-Barré Syndrome (GBS) were observed concurrently. The mandatory reporting of ZIKV disease began region-wide in February 2016, and it is believed that ZIKV cases were significantly under-reported before that. The changing reporting rate has made it difficult to estimate the ZIKV infection attack rate, and studies in the literature vary widely from 17% to > 50%. The same applies to other key epidemiological parameters. In contrast, the diagnosis and reporting of GBS cases were reasonably reliable given the severity and easy recognition of the disease symptoms. In this paper, we aim to estimate the real number of ZIKV cases (i.e., the infection attack rate) and their dynamics in time, by scaling up from GBS surveillance data in NE Brazil. METHODOLOGY A mathematical compartmental model is constructed that makes it possible to infer the true epidemic dynamics of ZIKV cases based on surveillance data of excess GBS cases. The model includes the possibility that asymptomatic ZIKV cases are infectious. The model is fitted to the GBS surveillance data and the key epidemiological parameters are inferred by using a plug-and-play likelihood-based estimation. We make use of regional weather data to determine possible climate-driven impacts on the reproductive number [Formula: see text], and to infer the true ZIKV epidemic dynamics. FINDINGS AND CONCLUSIONS The GBS surveillance data can be used to study ZIKV epidemics and may be appropriate when ZIKV reporting rates are not well understood. The overall infection attack rate (IAR) of ZIKV is estimated to be 24.1% (95% confidence interval: 17.1%-29.3%) of the population. By examining various asymptomatic scenarios, the IAR is likely to be lower than 33% over the two ZIKV waves. The risk rate from symptomatic ZIKV infection to develop GBS was estimated as ρ = 0.0061% (95% CI: 0.0050%-0.0086%) which is significantly less than current estimates. We found a positive association between local temperature and the basic reproduction number, [Formula: see text]. Our analysis revealed that asymptomatic infections affect the estimation of ZIKV epidemics and need to also be carefully considered in related modelling studies. According to the estimated effective reproduction number and population wide susceptibility, we comment that a ZIKV outbreak would be unlikely in NE Brazil in the near future.
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Affiliation(s)
- Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Shi Zhao
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
- Division of Biostatistics, JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
- Clinical Trials and Biostatistics Lab, Shenzhen Research Institute, Chinese University of Hong Kong, Shenzhen, China
| | - Qianying Lin
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Salihu S. Musa
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Lewi Stone
- Mathematical Science, School of Science, RMIT University, Melbourne, Victoria, Australia
- Biomathematics Unit, School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
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Zhao S, Musa SS, Hebert JT, Cao P, Ran J, Meng J, He D, Qin J. Modelling the effective reproduction number of vector-borne diseases: the yellow fever outbreak in Luanda, Angola 2015-2016 as an example. PeerJ 2020; 8:e8601. [PMID: 32149023 PMCID: PMC7049463 DOI: 10.7717/peerj.8601] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 01/19/2020] [Indexed: 01/02/2023] Open
Abstract
The burden of vector-borne diseases (Dengue, Zika virus, yellow fever, etc.) gradually increased in the past decade across the globe. Mathematical modelling on infectious diseases helps to study the transmission dynamics of the pathogens. Theoretically, the diseases can be controlled and eventually eradicated by maintaining the effective reproduction number, (R eff ), strictly less than 1. We established a vector-host compartmental model, and derived (R eff ) for vector-borne diseases. The analytic form of the (R eff ) was found to be the product of the basic reproduction number and the geometric average of the susceptibilities of the host and vector populations. The (R eff ) formula was demonstrated to be consistent with the estimates of the 2015-2016 yellow fever outbreak in Luanda, and distinguished the second minor epidemic wave. For those using the compartmental model to study the vector-borne infectious disease epidemics, we further remark that it is important to be aware of whether one or two generations is considered for the transition "from host to vector to host" in reproduction number calculation.
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Affiliation(s)
- Shi Zhao
- School of Nursing, Hong Kong Polytechnic University, Hong Kong, China
- Division of Biostatistics, JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
- Clinical Trials and Biostatistics Lab, Shenzhen Research Institute, Chinese University of Hong Kong, Shenzhen, China
| | - Salihu S. Musa
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Jay T. Hebert
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Peihua Cao
- Department of Hepatobiliary Surgery II, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jinjun Ran
- School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - Jiayi Meng
- School of Economics and Finance, Xi’an International Studies University, Xi’an, China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Jing Qin
- School of Nursing, Hong Kong Polytechnic University, Hong Kong, China
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Large-scale Lassa fever outbreaks in Nigeria: quantifying the association between disease reproduction number and local rainfall. Epidemiol Infect 2020; 148:e4. [PMID: 31918780 PMCID: PMC7019145 DOI: 10.1017/s0950268819002267] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Lassa fever (LF) is increasingly recognised as an important rodent-borne viral haemorrhagic fever presenting a severe public health threat to sub-Saharan West Africa. In 2017–18, LF caused an unprecedented epidemic in Nigeria and the situation was worsening in 2018–19. This work aims to study the epidemiological features of epidemics in different Nigerian regions and quantify the association between reproduction number (R) and state rainfall. We quantify the infectivity of LF by the reproduction numbers estimated from four different growth models: the Richards, three-parameter logistic, Gompertz and Weibull growth models. LF surveillance data are used to fit the growth models and estimate the Rs and epidemic turning points (τ) in different regions at different time periods. Cochran's Q test is further applied to test the spatial heterogeneity of the LF epidemics. A linear random-effect regression model is adopted to quantify the association between R and state rainfall with various lag terms. Our estimated Rs for 2017–18 (1.33 with 95% CI 1.29–1.37) was significantly higher than those for 2016–17 (1.23 with 95% CI: (1.22, 1.24)) and 2018–19 (ranged from 1.08 to 1.36). We report spatial heterogeneity in the Rs for epidemics in different Nigerian regions. We find that a one-unit (mm) increase in average monthly rainfall over the past 7 months could cause a 0.62% (95% CI 0.20%–1.05%)) rise in R. There is significant spatial heterogeneity in the LF epidemics in different Nigerian regions. We report clear evidence of rainfall impacts on LF epidemics in Nigeria and quantify the impact.
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