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Shi B, Yang S, Tan Q, Zhou L, Liu Y, Zhou X, Liu J. Bayesian inference for the onset time and epidemiological characteristics of emerging infectious diseases. Front Public Health 2024; 12:1406566. [PMID: 38827615 PMCID: PMC11140066 DOI: 10.3389/fpubh.2024.1406566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 05/07/2024] [Indexed: 06/04/2024] Open
Abstract
Background Emerging infectious diseases pose a significant threat to global public health. Timely detection and response are crucial in mitigating the spread of such epidemics. Inferring the onset time and epidemiological characteristics is vital for accelerating early interventions, but accurately predicting these parameters in the early stages remains challenging. Methods We introduce a Bayesian inference method to fit epidemic models to time series data based on state-space modeling, employing a stochastic Susceptible-Exposed-Infectious-Removed (SEIR) model for transmission dynamics analysis. Our approach uses the particle Markov chain Monte Carlo (PMCMC) method to estimate key epidemiological parameters, including the onset time, the transmission rate, and the recovery rate. The PMCMC algorithm integrates the advantageous aspects of both MCMC and particle filtering methodologies to yield a computationally feasible and effective means of approximating the likelihood function, especially when it is computationally intractable. Results To validate the proposed method, we conduct case studies on COVID-19 outbreaks in Wuhan, Shanghai and Nanjing, China, respectively. Using early-stage case reports, the PMCMC algorithm accurately predicted the onset time, key epidemiological parameters, and the basic reproduction number. These findings are consistent with empirical studies and the literature. Conclusion This study presents a robust Bayesian inference method for the timely investigation of emerging infectious diseases. By accurately estimating the onset time and essential epidemiological parameters, our approach is versatile and efficient, extending its utility beyond COVID-19.
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Affiliation(s)
- Benyun Shi
- College of Computer and Information Engineering, Nanjing Tech University, Nanjing, China
- College of Artificial Intelligence, Nanjing Tech University, Nanjing, China
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Sanguo Yang
- College of Computer and Information Engineering, Nanjing Tech University, Nanjing, China
- College of Artificial Intelligence, Nanjing Tech University, Nanjing, China
| | - Qi Tan
- College of Computer and Information Engineering, Nanjing Tech University, Nanjing, China
- College of Artificial Intelligence, Nanjing Tech University, Nanjing, China
| | - Lian Zhou
- Center for Disease Control and Prevention of Jiangsu Province, Nanjing, China
| | - Yang Liu
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Xiaohong Zhou
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jiming Liu
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
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Tan Q, Liu Y, Liu J, Shi B, Xia S, Zhou XN. Heterogeneous neural metric learning for spatio-temporal modeling of infectious diseases with incomplete data. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2019.12.145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Zheng J, Shi B, Xia S, Yang G, Zhou XN. Spatial patterns of <em>Plasmodium vivax</em> transmission explored by multivariate auto-regressive state-space modelling - A case study in Baoshan Prefecture in southern China. GEOSPATIAL HEALTH 2021; 16. [PMID: 33733649 DOI: 10.4081/gh.2021.879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 08/21/2020] [Indexed: 06/12/2023]
Abstract
The transition from the control phase to elimination of malaria in China through the national malaria elimination programme has focussed attention on the need for improvement of the surveillance- response systems. It is now understood that routine passive surveillance is inadequate in the parasite elimination phase that requires supplementation by active surveillance in foci where cluster cases have occurred. This study aims to explore the spatial clusters and temporal trends of malaria cases by the multivariate auto-regressive state-space model (MARSS) along the border to Myanmar in southern China. Data for indigenous cases spanning the period from 2007 to 2010 were extracted from the China's Infectious Diseases Information Reporting Management System (IDIRMS). The best MARSS model indicated that malaria transmission in the study area during 36 months could be grouped into three clusters. The estimation of malaria transmission patterns showed a downward trend across all clusters. The proposed methodology used in this study offers a simple and rapid, yet effective way to categorize patterns of foci which provide assistance for active monitoring of malaria in the elimination phase.
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Affiliation(s)
- Jinxin Zheng
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China; Key Laboratory of Parasite and Vector Biology, National Health Commission, Shanghai, China; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China; Chinese Center for Tropical Diseases Research, Shanghai.
| | - Benyun Shi
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, Jiangsu.
| | - Shang Xia
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China; Key Laboratory of Parasite and Vector Biology, National Health Commission, Shanghai, China; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China; Chinese Center for Tropical Diseases Research, Shanghai.
| | - Guojing Yang
- Hainan Medical University, Laboratory of Tropical Environment and Health, Haikou, Hainan, China; Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute; University of Basel, Basel.
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China; Key Laboratory of Parasite and Vector Biology, National Health Commission, Shanghai, China; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China; Chinese Center for Tropical Diseases Research, Shanghai.
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Shi B, Zheng J, Xia S, Lin S, Wang X, Liu Y, Zhou XN, Liu J. Accessing the syndemic of COVID-19 and malaria intervention in Africa. Infect Dis Poverty 2021; 10:5. [PMID: 33413680 PMCID: PMC7788178 DOI: 10.1186/s40249-020-00788-y] [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: 08/07/2020] [Accepted: 12/16/2020] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND The pandemic of the coronavirus disease 2019 (COVID-19) has caused substantial disruptions to health services in the low and middle-income countries with a high burden of other diseases, such as malaria in sub-Saharan Africa. The aim of this study is to assess the impact of COVID-19 pandemic on malaria transmission potential in malaria-endemic countries in Africa. METHODS We present a data-driven method to quantify the extent to which the COVID-19 pandemic, as well as various non-pharmaceutical interventions (NPIs), could lead to the change of malaria transmission potential in 2020. First, we adopt a particle Markov Chain Monte Carlo method to estimate epidemiological parameters in each country by fitting the time series of the cumulative number of reported COVID-19 cases. Then, we simulate the epidemic dynamics of COVID-19 under two groups of NPIs: (1) contact restriction and social distancing, and (2) early identification and isolation of cases. Based on the simulated epidemic curves, we quantify the impact of COVID-19 epidemic and NPIs on the distribution of insecticide-treated nets (ITNs). Finally, by treating the total number of ITNs available in each country in 2020, we evaluate the negative effects of COVID-19 pandemic on malaria transmission potential based on the notion of vectorial capacity. RESULTS We conduct case studies in four malaria-endemic countries, Ethiopia, Nigeria, Tanzania, and Zambia, in Africa. The epidemiological parameters (i.e., the basic reproduction number [Formula: see text] and the duration of infection [Formula: see text]) of COVID-19 in each country are estimated as follows: Ethiopia ([Formula: see text], [Formula: see text]), Nigeria ([Formula: see text], [Formula: see text]), Tanzania ([Formula: see text], [Formula: see text]), and Zambia ([Formula: see text], [Formula: see text]). Based on the estimated epidemiological parameters, the epidemic curves simulated under various NPIs indicated that the earlier the interventions are implemented, the better the epidemic is controlled. Moreover, the effect of combined NPIs is better than contact restriction and social distancing only. By treating the total number of ITNs available in each country in 2020 as a baseline, our results show that even with stringent NPIs, malaria transmission potential will remain higher than expected in the second half of 2020. CONCLUSIONS By quantifying the impact of various NPI response to the COVID-19 pandemic on malaria transmission potential, this study provides a way to jointly address the syndemic between COVID-19 and malaria in malaria-endemic countries in Africa. The results suggest that the early intervention of COVID-19 can effectively reduce the scale of the epidemic and mitigate its impact on malaria transmission potential.
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Affiliation(s)
- Benyun Shi
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, 211800 Jiangsu China
| | - Jinxin Zheng
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, 200025 China
- Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, 200025 China
- Chinese Center for Tropical Diseases Research, Shanghai, 200025 China
- WHO Collaborating Centre for Tropical Diseases, Shanghai, 200025 China
- National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025 China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
| | - Shang Xia
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, 200025 China
- Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, 200025 China
- Chinese Center for Tropical Diseases Research, Shanghai, 200025 China
- WHO Collaborating Centre for Tropical Diseases, Shanghai, 200025 China
- National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025 China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
| | - Shan Lin
- College of Information Engineering, Nanjing University of Finance & Economics, Nanjing, 210003 Jiangsu China
| | - Xinyi Wang
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, 200025 China
- Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, 200025 China
- Chinese Center for Tropical Diseases Research, Shanghai, 200025 China
- WHO Collaborating Centre for Tropical Diseases, Shanghai, 200025 China
- National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025 China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
| | - Yang Liu
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, 200025 China
- Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, 200025 China
- Chinese Center for Tropical Diseases Research, Shanghai, 200025 China
- WHO Collaborating Centre for Tropical Diseases, Shanghai, 200025 China
- National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, 200025 China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
| | - Jiming Liu
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
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Shi B, Lin S, Tan Q, Cao J, Zhou X, Xia S, Zhou XN, Liu J. Inference and prediction of malaria transmission dynamics using time series data. Infect Dis Poverty 2020; 9:95. [PMID: 32678025 PMCID: PMC7367373 DOI: 10.1186/s40249-020-00696-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Accepted: 06/11/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Disease surveillance systems are essential for effective disease intervention and control by monitoring disease prevalence as time series. To evaluate the severity of an epidemic, statistical methods are widely used to forecast the trend, seasonality, and the possible number of infections of a disease. However, most statistical methods are limited in revealing the underlying dynamics of disease transmission, which may be affected by various impact factors, such as environmental, meteorological, and physiological factors. In this study, we focus on investigating malaria transmission dynamics based on time series data. METHODS A data-driven nonlinear stochastic model is proposed to infer and predict the dynamics of malaria transmission based on the time series of prevalence data. Specifically, the dynamics of malaria transmission is modeled based on the notion of vectorial capacity (VCAP) and entomological inoculation rate (EIR). A particle Markov chain Monte Carlo (PMCMC) method is employed to estimate the model parameters. Accordingly, a one-step-ahead prediction method is proposed to project the number of future malaria infections. Finally, two case studies are carried out on the inference and prediction of Plasmodium vivax transmission in Tengchong and Longling, Yunnan province, China. RESULTS The results show that the trained data-driven stochastic model can well fit the historical time series of P. vivax prevalence data in both counties from 2007 to 2010. Moreover, with well-trained model parameters, the proposed one-step-ahead prediction method can achieve better performances than that of the seasonal autoregressive integrated moving average model with respect to predicting the number of future malaria infections. CONCLUSIONS By involving dynamically changing impact factors, the proposed data-driven model together with the PMCMC method can successfully (i) depict the dynamics of malaria transmission, and (ii) achieve accurate one-step-ahead prediction about malaria infections. Such a data-driven method has the potential to investigate malaria transmission dynamics in other malaria-endemic countries/regions.
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Affiliation(s)
- Benyun Shi
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, 211800 Jiangsu China
| | - Shan Lin
- College of Information Engineering, Nanjing University of Finance & Economics, NanjingJiangsu, 210003 China
| | - Qi Tan
- Department of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong
| | - Jie Cao
- College of Information Engineering, Nanjing University of Finance & Economics, NanjingJiangsu, 210003 China
| | - Xiaohong Zhou
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, 510515 Guangdong China
| | - Shang Xia
- National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention, Shanghai, 200025 China
- Key Laboratory of Parasite and Vector Biology, National Health Commission of the People Republic of China, Shanghai, 200025 China
- Chinese Center for Tropical Disease Research, Shanghai, 200025 China
- Shanghai, 200025 China
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention, Shanghai, 200025 China
- Key Laboratory of Parasite and Vector Biology, National Health Commission of the People Republic of China, Shanghai, 200025 China
- Chinese Center for Tropical Disease Research, Shanghai, 200025 China
- Shanghai, 200025 China
| | - Jiming Liu
- Department of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong
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Xia S, Zheng JX, Wang XY, Xue JB, Hu JH, Zhang XQ, Zhou XN, Li SZ. Epidemiological big data and analytical tools applied in the control programmes on parasitic diseases in China: NIPD's sustained contributions in 70 years. ADVANCES IN PARASITOLOGY 2020; 110:319-347. [PMID: 32563330 DOI: 10.1016/bs.apar.2020.04.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The analysis of epidemiological data has played an important role for the academic research carried out by the National Institute of Parasitic Diseases, China CDC, since its foundation in 1950s. Those researches, e.g., the temporal-spatial patterns of disease transmission and the identification of risk factors, have contributed significantly to the national parasitic disease control and elimination programmes in China. With the development and application of epidemiological data analysis in the last decade, all research results improve our understanding of parasitic diseases epidemiology and related health issues through the application platform of epidemiological big data and analytical tools. In particular, implementation research on analytical predictions on disease outbreak or epidemic risks have provided references to the scientific guidance on effective preventions and interventions in the parasitic disease elimination in China, such as fliariasis, malaria and schistosomiasis. This review has reflected the function of data accumulation and application of temporospatial tools in parasitic diseases control, and the ways of the NIPD's sustained contributions to the disease control programmes in China.
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Affiliation(s)
- Shang Xia
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China; Chinese Center for Tropical Diseases Research, Shanghai, People's Republic of China; Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China; National Health Commission of China, Shanghai, People's Republic of China; WHO Collaborating Centre for Tropical Diseases, Shanghai, People's Republic of China
| | - Jin-Xin Zheng
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China; Chinese Center for Tropical Diseases Research, Shanghai, People's Republic of China; Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China; National Health Commission of China, Shanghai, People's Republic of China; WHO Collaborating Centre for Tropical Diseases, Shanghai, People's Republic of China
| | - Xin-Yi Wang
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China; Chinese Center for Tropical Diseases Research, Shanghai, People's Republic of China; Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China; National Health Commission of China, Shanghai, People's Republic of China; WHO Collaborating Centre for Tropical Diseases, Shanghai, People's Republic of China
| | - Jing-Bo Xue
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China; Chinese Center for Tropical Diseases Research, Shanghai, People's Republic of China; Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China; National Health Commission of China, Shanghai, People's Republic of China; WHO Collaborating Centre for Tropical Diseases, Shanghai, People's Republic of China
| | - Jian-Hong Hu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China; Chinese Center for Tropical Diseases Research, Shanghai, People's Republic of China; Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China; National Health Commission of China, Shanghai, People's Republic of China; WHO Collaborating Centre for Tropical Diseases, Shanghai, People's Republic of China
| | - Xue-Qiang Zhang
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China; Chinese Center for Tropical Diseases Research, Shanghai, People's Republic of China; Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China; National Health Commission of China, Shanghai, People's Republic of China; WHO Collaborating Centre for Tropical Diseases, Shanghai, People's Republic of China
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China; Chinese Center for Tropical Diseases Research, Shanghai, People's Republic of China; Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China; National Health Commission of China, Shanghai, People's Republic of China; WHO Collaborating Centre for Tropical Diseases, Shanghai, People's Republic of China
| | - Shi-Zhu Li
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China.
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Zhu G, Liu T, Xiao J, Zhang B, Song T, Zhang Y, Lin L, Peng Z, Deng A, Ma W, Hao Y. Effects of human mobility, temperature and mosquito control on the spatiotemporal transmission of dengue. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 651:969-978. [PMID: 30360290 DOI: 10.1016/j.scitotenv.2018.09.182] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 09/14/2018] [Accepted: 09/14/2018] [Indexed: 05/06/2023]
Abstract
Dengue transmission exhibits evident geographic variations and seasonal differences. Such heterogeneity is caused by various impact factors, in which temperature and host/vector behaviors could drive its spatiotemporal transmission, but mosquito control could stop its progression. These factors together contribute to the observed distributions of dengue incidence from surveillance systems. To effectively and efficiently monitor and response to dengue outbreak, it would be necessary to systematically model these factors and their impacts on dengue transmission. This paper introduces a new modeling framework with consideration of multi-scale factors and surveillance data to clarify the hidden dynamics accounting for dengue spatiotemporal transmission. The model is based on compartmental system which takes into account the biting-based interactions among humans, viruses and mosquitoes, as well as the essential impacts of human mobility, temperature and mosquito control. This framework was validated with real epidemic data by applying retrospectively to the 2014 dengue epidemic in the Pearl River Delta (PRD) in southern China. The results indicated that suitable condition of temperature could be responsible for the explosive dengue outbreak in the PRD, and human mobility could be the causal factor leading to its spatial transmission across different cities. It was further found that mosquito intervention has significantly reduced dengue incidence, where a total of 52,770 (95% confidence interval [CI]: 29,231-76,308) dengue cases were prevented in the PRD in 2014. The findings can offer new insights for improving the predictability and risk assessment of dengue epidemics. The model also can be readily extended to investigate the transmission dynamics of other mosquito-borne diseases.
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Affiliation(s)
- Guanghu Zhu
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Department of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China; Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Tao Liu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Bing Zhang
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Tie Song
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Yonghui Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Lifeng Lin
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Zhiqiang Peng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Aiping Deng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Wenjun Ma
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China.
| | - Yuantao Hao
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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Wang L, Zhao H, Oliva SM, Zhu H. Modeling the transmission and control of Zika in Brazil. Sci Rep 2017; 7:7721. [PMID: 28798323 PMCID: PMC5552773 DOI: 10.1038/s41598-017-07264-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Accepted: 06/28/2017] [Indexed: 01/18/2023] Open
Abstract
Zika virus, a reemerging mosquito-borne flavivirus, started spread across Central and Southern America and more recently to North America. The most serious impacted country is Brazil. Based on the transmission mechanism of the virus and assessment of the limited data on the reported suspected cases, we establish a dynamical model which allows us to estimate the basic reproduction number R 0 = 2.5020. The wild spreading of the virus make it a great challenge to public health to control and prevention of the virus. We formulate two control models to study the impact of releasing transgenosis mosquitoes (introducing bacterium Wolbachia into Aedes aegypti) on the transmission of Zika virus in Brazil. Our models and analysis suggest that simultaneously releasing Wolbachia-harboring female and male mosquitoes will achieve the target of population replacement, while releasing only Wolbachia-harboring male mosquitoes will suppress or even eradicate wild mosquitoes eventually. We conclude that only releasing male Wolbachia mosquitoes is a better strategy for control the spreading of Zika virus in Brazil.
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Affiliation(s)
- Liping Wang
- Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, P.R. China
| | - Hongyong Zhao
- Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, P.R. China.
| | - Sergio Muniz Oliva
- Departamento de Matemática Aplicada, Instituto de Matemática e Estatística, Universidade de São Paulo, Rua do Matão, 1010, Cidade Universitária, CEP 05508-090, São Paulo, SP, Brazil
| | - Huaiping Zhu
- Lamps and Department of Mathematics and Statistics, York University, Toronto, ON, M3J 1P3, Canada
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Liu T, Zhu G, He J, Song T, Zhang M, Lin H, Xiao J, Zeng W, Li X, Li Z, Xie R, Zhong H, Wu X, Hu W, Zhang Y, Ma W. Early rigorous control interventions can largely reduce dengue outbreak magnitude: experience from Chaozhou, China. BMC Public Health 2017; 18:90. [PMID: 28768542 PMCID: PMC5541667 DOI: 10.1186/s12889-017-4616-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Accepted: 07/20/2017] [Indexed: 01/03/2023] Open
Abstract
Background Dengue fever is a severe public heath challenge in south China. A dengue outbreak was reported in Chaozhou city, China in 2015. Intensified interventions were implemented by the government to control the epidemic. However, it is still unknown the degree to which intensified control measures reduced the size of the epidemics, and when should such measures be initiated to reduce the risk of large dengue outbreaks developing? Methods We selected Xiangqiao district as study setting because the majority of the indigenous cases (90.6%) in Chaozhou city were from this district. The numbers of daily indigenous dengue cases in 2015 were collected through the national infectious diseases and vectors surveillance system, and daily Breteau Index (BI) data were reported by local public health department. We used a compartmental dynamic SEIR (Susceptible, Exposed, Infected and Removed) model to assess the effectiveness of control interventions, and evaluate the control effect of intervention timing on dengue epidemic. Results A total of 1250 indigenous dengue cases was reported from Xiangqiao district. The results of SEIR modeling using BI as an indicator of actual control interventions showed a total of 1255 dengue cases, which is close to the reported number (n = 1250). The size and duration of the outbreak were highly sensitive to the intensity and timing of interventions. The more rigorous and earlier the control interventions implemented, the more effective it yielded. Even if the interventions were initiated several weeks after the onset of the dengue outbreak, the interventions were shown to greatly impact the prevalence and duration of dengue outbreak. Conclusions This study suggests that early implementation of rigorous dengue interventions can effectively reduce the epidemic size and shorten the epidemic duration.
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Affiliation(s)
- Tao Liu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China
| | - Guanghu Zhu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China
| | - Jianfeng He
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China
| | - Tie Song
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China
| | - Meng Zhang
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China
| | - Hualiang Lin
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China
| | - Weilin Zeng
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China
| | - Xing Li
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China
| | - Zhihao Li
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China
| | - Runsheng Xie
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China
| | - Haojie Zhong
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China
| | - Xiaocheng Wu
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China.,Faculty of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Wenbiao Hu
- School of Public Health and Social Work & Institute of Health and Biomedical Innovation, Queensland University of Technology, QLD, Brisbane, Australia
| | - Yonghui Zhang
- Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China.
| | - Wenjun Ma
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China.
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Shi B, Zheng J, Qiu H, Yang GJ, Xia S, Zhou XN. Risk assessment of malaria transmission at the border area of China and Myanmar. Infect Dis Poverty 2017; 6:108. [PMID: 28679420 PMCID: PMC5499046 DOI: 10.1186/s40249-017-0322-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 06/01/2017] [Indexed: 01/03/2023] Open
Abstract
Background In order to achieve the goal of malaria elimination, the Chinese government launched the National Malaria Elimination Programme in 2010. However, as a result of increasing cross-border population movements, the risk of imported malaria cases still exists at the border areas of China, resulting in a potential threat of local transmission. The focus of this paper is to assess the Plasmodium vivax incidences in Tengchong, Yunnan Province, at the border areas of China and Myanmar. Methods Time series of P. vivax incidences in Tengchong from 2006 to 2010 are collected from the web-based China Information System for Disease Control and Prevention, which are further separated into time series of imported and local cases. First, the seasonal and trend decomposition are performed on time series of imported cases using Loess method. Then, the impact of climatic factors on the local transmission of P. vivax is assessed using both linear regression models (LRM) and generalized additive models (GAM). Specifically, the notion of vectorial capacity (VCAP) is used to estimate the transmission potential of P. vivax at different locations, which is calculated based on temperature and rainfall collected from China Meteorological Administration. Results Comparing with Ruili County, the seasonal pattern of imported cases in Tengchong is different: Tengchong has only one peak, while Ruili has two peaks during each year. This may be due to the different cross-border behaviors of peoples in two locations. The vectorial capacity together with the imported cases and the average humidity, can well explain the local incidences of P. vivax through both LRM and GAM methods. Moreover, the maximum daily temperature is verified to be more suitable to calculate VCAP than the minimal and average temperature in Tengchong County. Conclusion To achieve malaria elimination in China, the assessment results in this paper will provide further guidance in active surveillance and control of malaria at the border areas of China and Myanmar. Electronic supplementary material The online version of this article (doi:10.1186/s40249-017-0322-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Benyun Shi
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Jinxin Zheng
- Jiangsu Institute of Parasitic Diseases, Wuxi, 214064, China
| | - Hongjun Qiu
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Guo-Jing Yang
- Jiangsu Institute of Parasitic Diseases, Wuxi, 214064, China.
| | - Shang Xia
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.,Key Laboratory of Parasite and Vector Biology, MOH; WHO Collaborating Center for Tropical Diseases, Shanghai, 200025, China
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IASM: A System for the Intelligent Active Surveillance of Malaria. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:2080937. [PMID: 27563343 PMCID: PMC4983402 DOI: 10.1155/2016/2080937] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Revised: 05/10/2016] [Accepted: 06/09/2016] [Indexed: 11/28/2022]
Abstract
Malaria, a life-threatening infectious disease, spreads rapidly via parasites. Malaria prevention is more effective and efficient than treatment. However, the existing surveillance systems used to prevent malaria are inadequate, especially in areas with limited or no access to medical resources. In this paper, in order to monitor the spreading of malaria, we develop an intelligent surveillance system based on our existing algorithms. First, a visualization function and active surveillance were implemented in order to predict and categorize areas at high risk of infection. Next, socioeconomic and climatological characteristics were applied to the proposed prediction model. Then, the redundancy of the socioeconomic attribute values was reduced using the stepwise regression method to improve the accuracy of the proposed prediction model. The experimental results indicated that the proposed IASM predicted malaria outbreaks more close to the real data and with fewer variables than other models. Furthermore, the proposed model effectively identified areas at high risk of infection.
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Zhu G, Liu J, Tan Q, Shi B. Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China. PLoS Negl Trop Dis 2016; 10:e0004633. [PMID: 27105350 PMCID: PMC4841561 DOI: 10.1371/journal.pntd.0004633] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Accepted: 03/27/2016] [Indexed: 11/29/2022] Open
Abstract
Background Dengue is a serious vector-borne disease, and incidence rates have significantly increased during the past few years, particularly in 2014 in Guangzhou. The current situation is more complicated, due to various factors such as climate warming, urbanization, population increase, and human mobility. The purpose of this study is to detect dengue transmission patterns and identify the disease dispersion dynamics in Guangzhou, China. Methodology We conducted surveys in 12 districts of Guangzhou, and collected daily data of Breteau index (BI) and reported cases between September and November 2014 from the public health authority reports. Based on the available data and the Ross-Macdonald theory, we propose a dengue transmission model that systematically integrates entomologic, demographic, and environmental information. In this model, we use (1) BI data and geographic variables to evaluate the spatial heterogeneities of Aedes mosquitoes, (2) a radiation model to simulate the daily mobility of humans, and (3) a Markov chain Monte Carlo (MCMC) method to estimate the model parameters. Results/Conclusions By implementing our proposed model, we can (1) estimate the incidence rates of dengue, and trace the infection time and locations, (2) assess risk factors and evaluate the infection threat in a city, and (3) evaluate the primary diffusion process in different districts. From the results, we can see that dengue infections exhibited a spatial and temporal variation during 2014 in Guangzhou. We find that urbanization, vector activities, and human behavior play significant roles in shaping the dengue outbreak and the patterns of its spread. This study offers useful information on dengue dynamics, which can help policy makers improve control and prevention measures. Dengue transmission is a spatio-temporal process with interactions between hosts, vectors, and viruses. Its transmission also involves multiple complex or even hidden factors, such as climate, social environment, vector ecology, and host mobility. These complexities make the underlying process of dengue transmission difficult to clarify. We address how the patterns of dengue transmission can be inferred by investigating the 2014 dengue outbreak in the city of Guangzhou, China, taking the available surveillance data and applying mathematical models and computational methods. We can then estimate the distribution of dengue infections and identify the transmission mechanisms. In our study, we systematically investigate the critical factors, enabling us to estimate the real patterns and dynamics of dengue transmission beyond the surveillance data.
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Affiliation(s)
- Guanghu Zhu
- Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, China
| | - Jiming Liu
- Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- * E-mail:
| | - Qi Tan
- Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Benyun Shi
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
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