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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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Lu G, Zhao L, Chai L, Cao Y, Chong Z, Liu K, Lu Y, Zhu G, Xia P, Müller O, Zhu G, Cao J. Assessing the risk of malaria local transmission and re-introduction in China from pre-elimination to elimination: A systematic review. Acta Trop 2024; 249:107082. [PMID: 38008371 DOI: 10.1016/j.actatropica.2023.107082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/22/2023] [Accepted: 11/23/2023] [Indexed: 11/28/2023]
Abstract
Assessing the risk of malaria local transmission and re-introduction is crucial for the preparation and implementation of an effective elimination campaign and the prevention of malaria re-introduction in China. Therefore, this review aims to evaluate the risk factors for malaria local transmission and re-introduction in China over the period of pre-elimination to elimination. Data were obtained from six databases searched for studies that assessed malaria local transmission risk before malaria elimination and re-introduction risk after the achievement of malaria elimination in China since the launch of the NMEP in 2010, employing the keywords "malaria" AND ("transmission" OR "re-introduction") and their synonyms. A total of 8,124 articles were screened and 53 articles describing 55 malaria risk assessment models in China from 2010 to 2023, including 40 models assessing malaria local transmission risk (72.7%) and 15 models assessing malaria re-introduction risk (27.3%). Factors incorporated in the 55 models were extracted and classified into six categories, including environmental and meteorological factors (39/55, 70.9%), historical epidemiology (35/55, 63.6%), vectorial factors (32/55, 58.2%), socio-demographic information (15/26, 53.8%), factors related to surveillance and response capacity (18/55, 32.7%), and population migration aspects (13/55, 23.6%). Environmental and meteorological factors as well as vectorial factors were most commonly incorporated in models assessing malaria local transmission risk (29/40, 72.5% and 21/40, 52.5%) and re-introduction risk (10/15, 66.7% and 11/15, 73.3%). Factors related to surveillance and response capacity and population migration were also important in malaria re-introduction risk models (9/15, 60%, and 6/15, 40.0%). A total of 18 models (18/55, 32.7%) reported the modeling performance. Only six models were validated internally and five models were validated externally. Of 53 incorporated studies, 45 studies had a quality assessment score of seven and above. Environmental and meteorological factors as well as vectorial factors play a significant role in malaria local transmission and re-introduction risk assessment. The factors related to surveillance and response capacity and population migration are more important in assessing malaria re-introduction risk. The internal and external validation of the existing models needs to be strengthened in future studies.
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Affiliation(s)
- Guangyu Lu
- School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, China; Jiangsu Key Laboratory of Zoonosis, Yangzhou, China.
| | - Li Zhao
- School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Liying Chai
- School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Yuanyuan Cao
- National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, China
| | - Zeyin Chong
- School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Kaixuan Liu
- School of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Yan Lu
- Nanjing Health and Customs Quarantine Office, Nanjing, China
| | - Guoqiang Zhu
- Jiangsu Key Laboratory of Zoonosis, Yangzhou, China
| | - Pengpeng Xia
- Jiangsu Key Laboratory of Zoonosis, Yangzhou, China
| | - Olaf Müller
- Institute of Global Health, Medical School, Ruprecht-Karls-University Heidelberg, Germany
| | - Guoding Zhu
- National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
| | - Jun Cao
- National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, China; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
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Mfisimana LD, Nibayisabe E, Badu K, Niyukuri D. Exploring predictive frameworks for malaria in Burundi. Infect Dis Model 2022; 7:33-44. [PMID: 35388371 PMCID: PMC8941165 DOI: 10.1016/j.idm.2022.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 02/22/2022] [Accepted: 03/05/2022] [Indexed: 11/10/2022] Open
Abstract
In Burundi, malaria infection has been increasing in the last decade despite efforts to increase access to health services, and several intervention programs. The use of heterogeneous data can help to build predictive models of malaria cases. We built predictive frameworks: the generalized linear model (GLM), and artificial neural network (ANN), to predict malaria cases in four sub-groups and the overall general population. Descriptive results showed that more than half of malaria infections are observed in pregnant women and children under 5 years, with high burden to children between 12 and 59 months. Modelling results showed that, ANN model performed better in predicting total cases compared to GLM. Both model frameworks showed that education rates and Insecticide Treated Bed Nets (ITNs) had decreasing effects on malaria cases, some other variables had an increasing effect. Thus, malaria control and prevention interventions program are encouraged to understand those variables, and take appropriate measures such as providing ITNs, sensitization in schools and the communities, starting within high dense communities, among others. Early prediction of cases can provide timely information needed to be proactive for intervention strategies, and it can help to mitigate the epidemics and reduce its impact on populations and the economy.
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Affiliation(s)
| | - Emile Nibayisabe
- Faculté des Sciences Fondamentales, Institut Supérieur des Cadres Militaires, Burundi
| | - Kingsley Badu
- Department of Theoretical and Applied Biology, Kwame Nkrumah University of Science and Technology, Ghana
| | - David Niyukuri
- Faculté des Sciences Fondamentales, Institut Supérieur des Cadres Militaires, Burundi
- Division of Epidemiology & Biostatistics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- The South African Department of Science and Technology–National Research Foundation (DST-NRF) Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Cape Town, South Africa
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