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Abdalgader T, Zheng Z, Banerjee M, Zhang L. The timeline of overseas imported cases acts as a strong indicator of dengue outbreak in mainland China. CHAOS (WOODBURY, N.Y.) 2024; 34:083106. [PMID: 39213011 DOI: 10.1063/5.0204336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 07/20/2024] [Indexed: 09/04/2024]
Abstract
The emergence of dengue viruses in new, susceptible human populations worldwide is increasingly influenced by a combination of local and global human movements and favorable environmental conditions. While various mathematical models have explored the impact of environmental factors on dengue outbreaks, the significant role of human mobility both internationally and domestically in transmitting the disease has been less frequently addressed. In this context, we introduce a modeling framework that integrates the effects of international travel-induced imported cases, climatic conditions, and local human movements to assess the spatiotemporal dynamics of dengue transmission. Utilizing the generation matrix method, we calculate the basic reproduction number and its sensitivity to various model parameters. Through numerical simulations using data on climate, human mobility, and reported dengue cases in mainland China, our model demonstrates a good agreement with observed data upon validation. Our findings reveal that while climatic conditions are a key driver for the rapid dengue transmission, human mobility plays a crucial role in its local spread. Importantly, the model highlights the significant impact of imported cases from overseas on the initiation of dengue outbreaks and their contribution to increasing the disease incidence rate by 34.6%. Furthermore, the analysis identifies that dengue cases originating from regions, such as Cambodia and Myanmar internationally, and Guangzhou and Xishuangbanna domestically, have the potential to significantly increase the disease burden in mainland China. These insights emphasize the critical need to include data on imported cases and domestic travel patterns in disease outbreak models to improve the precision of predictions, thereby enhancing dengue prevention, surveillance, and response strategies.
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Affiliation(s)
- Tarteel Abdalgader
- School of Mathematical Science, Yangzhou University, Yangzhou 225002, China
- Department of Mathematics, Faculty of Education, University of Khartoum, P.O. Box 321, Khartoum, Sudan
| | - Zhoumin Zheng
- School of Mathematical Science, Yangzhou University, Yangzhou 225002, China
| | - Malay Banerjee
- Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - Lai Zhang
- School of Mathematical Science, Yangzhou University, Yangzhou 225002, China
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2
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Ospina-Aguirre C, Soriano-Paños D, Olivar-Tost G, Galindo-González CC, Gómez-Gardeñes J, Osorio G. Effects of human mobility on the spread of Dengue in the region of Caldas, Colombia. PLoS Negl Trop Dis 2023; 17:e0011087. [PMID: 38011274 PMCID: PMC10703399 DOI: 10.1371/journal.pntd.0011087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 12/07/2023] [Accepted: 09/21/2023] [Indexed: 11/29/2023] Open
Abstract
According to the World Health Organization (WHO), dengue is the most common acute arthropod-borne viral infection in the world. The spread of dengue and other infectious diseases is closely related to human activity and mobility. In this paper we analyze the effect of introducing mobility restrictions as a public health policy on the total number of dengue cases within a population. To perform the analysis, we use a complex metapopulation in which we implement a compartmental propagation model coupled with the mobility of individuals between the patches. This model is used to investigate the spread of dengue in the municipalities of Caldas (CO). Two scenarios corresponding to different types of mobility restrictions are applied. In the first scenario, the effect of restricting mobility is analyzed in three different ways: a) limiting the access to the endemic node but allowing the movement of its inhabitants, b) restricting the diaspora of the inhabitants of the endemic node but allowing the access of outsiders, and c) a total isolation of the inhabitants of the endemic node. In this scenario, the best simulation results are obtained when specific endemic nodes are isolated during a dengue outbreak, obtaining a reduction of up to 2.5% of dengue cases. Finally, the second scenario simulates a total isolation of the network, i.e., mobility between nodes is completely limited. We have found that this control measure increases the number of total dengue cases in the network by 2.36%.
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Affiliation(s)
- Carolina Ospina-Aguirre
- ABCDynamics, Facultad de ciencias exactas y naturales, Universidad Nacional de Colombia - Sede Manizales, Manizales, Colombia
- Departamento de electrónica y automatización, Universidad Autonoma de Manizales, Manizales, Colombia
| | - David Soriano-Paños
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
- GOTHAM lab, Institute for Biocomputation & Physics of Complex Systems (BIFI), Zaragoza, España
| | - Gerard Olivar-Tost
- Departamento de Ciencias Naturales y Tecnología, Universidad de Aysén, Coyhaique, Chile
| | - Cristian C. Galindo-González
- Percepción y Control Inteligente (PCI), Departamento de Ingeniería Eléctrica, Electrónica y Computación, Universidad Nacional de Colombia - Sede Manizales, Manizales, Colombia
| | - Jesús Gómez-Gardeñes
- Departamento de Física de la Materia Condensada Facultad de Ciencias, Universidad de Zaragoza, Zaragoza, España
- GOTHAM lab, Institute for Biocomputation & Physics of Complex Systems (BIFI), Zaragoza, España
| | - Gustavo Osorio
- Percepción y Control Inteligente (PCI), Departamento de Ingeniería Eléctrica, Electrónica y Computación, Universidad Nacional de Colombia - Sede Manizales, Manizales, Colombia
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3
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Zeng Q, Yu X, Ni H, Xiao L, Xu T, Wu H, Chen Y, Deng H, Zhang Y, Pei S, Xiao J, Guo P. Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm. PLoS Negl Trop Dis 2023; 17:e0011418. [PMID: 37285385 DOI: 10.1371/journal.pntd.0011418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 05/24/2023] [Indexed: 06/09/2023] Open
Abstract
Predicting the specific magnitude and the temporal peak of the epidemic of individual local outbreaks is critical for infectious disease control. Previous studies have indicated that significant differences in spatial transmission and epidemic magnitude of dengue were influenced by multiple factors, such as mosquito population density, climatic conditions, and population movement patterns. However, there is a lack of studies that combine the above factors to explain their complex nonlinear relationships in dengue transmission and generate accurate predictions. Therefore, to study the complex spatial diffusion of dengue, this research combined the above factors and developed a network model for spatiotemporal transmission prediction of dengue fever using metapopulation networks based on human mobility. For improving the prediction accuracy of the epidemic model, the ensemble adjusted Kalman filter (EAKF), a data assimilation algorithm, was used to iteratively assimilate the observed case data and adjust the model and parameters. Our study demonstrated that the metapopulation network-EAKF system provided accurate predictions for city-level dengue transmission trajectories in retrospective forecasts of 12 cities in Guangdong province, China. Specifically, the system accurately predicts local dengue outbreak magnitude and the temporal peak of the epidemic up to 10 wk in advance. In addition, the system predicted the peak time, peak intensity, and total number of dengue cases more accurately than isolated city-specific forecasts. The general metapopulation assimilation framework presented in our study provides a methodological foundation for establishing an accurate system with finer temporal and spatial resolution for retrospectively forecasting the magnitude and temporal peak of dengue fever outbreaks. These forecasts based on the proposed method can be interoperated to better support intervention decisions and inform the public of potential risks of disease transmission.
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Affiliation(s)
- Qinghui Zeng
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Xiaolin Yu
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Haobo Ni
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Lina Xiao
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Ting Xu
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Haisheng Wu
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Yuliang Chen
- Department of Medical Quality Management, Nanfang Hospital, Guangzhou, China
| | - Hui Deng
- Institute of Vector Control, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Yingtao Zhang
- Institute of Infectious Disease Control and Prevention, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Pi Guo
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Shantou, China
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4
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Osorio Arjona J, de Las Obras-Loscertales Sampériz J. Estimation of mobility and population in Spain during different phases of the COVID-19 pandemic from mobile phone data. Sci Rep 2023; 13:8962. [PMID: 37268712 DOI: 10.1038/s41598-023-36108-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 05/30/2023] [Indexed: 06/04/2023] Open
Abstract
This work aims to find out the effectiveness of sources based on Big Data like mobile phone records to analyze mobility flows and changes in the population of Spain in different scenarios during the period of the pandemic caused by the COVID-19 virus. To this end, we have used mobile phone data provided by the National Institute of Statistics from four days corresponding to different phases of the pandemic. Origin-Destination matrices and population estimation calculations at the spatial level of population cells have been elaborated. The results show different patterns that correspond to the phenomena that have occurred, as the decrease of the population during the periods associated with the confinement measures. The consistency of findings with the reality and the generally good correlation with the population census data indicate that mobile phone records are a useful source of data for the elaboration of demographic and mobility studies during pandemics.
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Zaw W, Lin Z, Ko Ko J, Rotejanaprasert C, Pantanilla N, Ebener S, Maude RJ. Dengue in Myanmar: Spatiotemporal epidemiology, association with climate and short-term prediction. PLoS Negl Trop Dis 2023; 17:e0011331. [PMID: 37276226 PMCID: PMC10270578 DOI: 10.1371/journal.pntd.0011331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 06/15/2023] [Accepted: 04/24/2023] [Indexed: 06/07/2023] Open
Abstract
Dengue is a major public health problem in Myanmar. The country aims to reduce morbidity by 50% and mortality by 90% by 2025 based on 2015 data. To support efforts to reach these goals it is important to have a detailed picture of the epidemiology of dengue, its relationship to meteorological factors and ideally to predict ahead of time numbers of cases to plan resource allocations and control efforts. Health facility-level data on numbers of dengue cases from 2012 to 2017 were obtained from the Vector Borne Disease Control Unit, Department of Public Health, Myanmar. A detailed analysis of routine dengue and dengue hemorrhagic fever (DHF) incidence was conducted to examine the spatial and temporal epidemiology. Incidence was compared to climate data over the same period. Dengue was found to be widespread across the country with an increase in spatial extent over time. The temporal pattern of dengue cases and fatalities was episodic with annual outbreaks and no clear longitudinal trend. There were 127,912 reported cases and 632 deaths from 2012 and 2017 with peaks in 2013, 2015 and 2017. The case fatality rate was around 0.5% throughout. The peak season of dengue cases was from May to August in the wet season but in 2014 peak dengue season continued until November. The strength of correlation of dengue incidence with different climate factors (total rainfall, maximum, mean and minimum temperature and absolute humidity) varied between different States and Regions. Monthly incidence was forecasted 1 month ahead using the Auto Regressive Integrated Moving Average (ARIMA) method at country and subnational levels. With further development and validation, this may be a simple way to quickly generate short-term predictions at subnational scales with sufficient certainty to use for intervention planning.
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Affiliation(s)
- Win Zaw
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Zaw Lin
- Vector Borne Disease Control, Department of Public Health, Ministry of Health, Nay Pyi Taw, Myanmar
| | - July Ko Ko
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Chawarat Rotejanaprasert
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Neriza Pantanilla
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Steeve Ebener
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Richard James Maude
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Harvard TH Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
- The Open University, Milton Keynes, United Kingdom
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6
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Nakase T, Giovanetti M, Obolski U, Lourenço J. Global transmission suitability maps for dengue virus transmitted by Aedes aegypti from 1981 to 2019. Sci Data 2023; 10:275. [PMID: 37173303 PMCID: PMC10182074 DOI: 10.1038/s41597-023-02170-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
Mosquito-borne viruses increasingly threaten human populations due to accelerating changes in climate, human and mosquito migration, and land use practices. Over the last three decades, the global distribution of dengue has rapidly expanded, causing detrimental health and economic problems in many areas of the world. To develop effective disease control measures and plan for future epidemics, there is an urgent need to map the current and future transmission potential of dengue across both endemic and emerging areas. Expanding and applying Index P, a previously developed mosquito-borne viral suitability measure, we map the global climate-driven transmission potential of dengue virus transmitted by Aedes aegypti mosquitoes from 1981 to 2019. This database of dengue transmission suitability maps and an R package for Index P estimations are offered to the public health community as resources towards the identification of past, current and future transmission hotspots. These resources and the studies they facilitate can contribute to the planning of disease control and prevention strategies, especially in areas where surveillance is unreliable or non-existent.
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Affiliation(s)
- Taishi Nakase
- Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK.
| | - Marta Giovanetti
- Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, 21040-360, Brazil
- Department of Science and Technology for Humans and the Environment, University of Campus Bio-Medico di Roma, Rome, 00128, Italy
| | - Uri Obolski
- School of Public Health, Faculty of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel
- Porter School of the Environment and Earth Sciences, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, 69978, Israel
| | - José Lourenço
- Biosystems and Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Lisbon, 1749-016, Portugal.
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7
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Yang J, Shi Y, Zheng Y, Zhang Z. The spatiotemporal prediction method of urban population density distribution through behaviour environment interaction agent model. Sci Rep 2023; 13:5821. [PMID: 37037827 PMCID: PMC10086058 DOI: 10.1038/s41598-023-32529-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 03/29/2023] [Indexed: 04/12/2023] Open
Abstract
Based on the interrelationship between the built environment and spatial-temporal distribution of population density, this paper proposes a method to predict the spatial-temporal distribution of urban population density using the depth residual network model (ResNet) of neural network. This study used the time-sharing data of mobile phone users provided by the China Mobile Communications Corporation to predict the time-space sequence of the steady-state distribution of population density. Firstly, 40 prediction databases were constructed according to the characteristics of built environment and the spatial-temporal distribution of population density. Thereafter, the depth residual model ResNet was used as the basic framework to construct the behaviour-environment agent model (BEM) for model training and prediction. Finally, the average percentage error index was used to evaluate the prediction results. The results revealed that the accuracy rate of prediction results reached 76.92% in the central urban area of the verification case. The proposed method can be applied to prevent urban public safety incidents and alleviate pandemics. Moreover, this method can be practically applied to enable the construction of a "smart city" for improving the efficient allocation of urban resources and traffic mobility.
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Affiliation(s)
- Junyan Yang
- School of Architecture, Southeast University, 2nd Sipailou Street, Xuanwu District, Nanjing, 210096, China.
- Southeast University Smart City Institute, Nanjing, China.
| | - Yi Shi
- School of Architecture, Southeast University, 2nd Sipailou Street, Xuanwu District, Nanjing, 210096, China
| | - Yi Zheng
- School of Architecture, Southeast University, 2nd Sipailou Street, Xuanwu District, Nanjing, 210096, China
- Research Centre for Chinese Nation Visual Image, Southeast University, Nanjing, China
| | - Zhonghu Zhang
- School of Architecture, Southeast University, 2nd Sipailou Street, Xuanwu District, Nanjing, 210096, China
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8
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Roster KO, Martinelli T, Connaughton C, Santillana M, Rodrigues FA. Estimating the impact of the COVID-19 pandemic on dengue in Brazil. RESEARCH SQUARE 2023:rs.3.rs-2548491. [PMID: 36798282 PMCID: PMC9934738 DOI: 10.21203/rs.3.rs-2548491/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Atypical dengue prevalence was observed in 2020 in many dengue-endemic countries, including Brazil. Evidence suggests that the pandemic disrupted not only dengue dynamics due to changes in mobility patterns, but also several aspects of dengue surveillance, such as care seeking behavior, care availability, and monitoring systems. However, we lack a clear understanding of the overall impact on dengue in different parts of the country as well as the role of individual causal drivers. In this study, we estimated the gap between expected and observed dengue cases in 2020 using an interrupted time series design with forecasts from a neural network and a structural Bayesian time series model. We also decomposed the gap into the impacts of climate conditions, pandemic-induced changes in reporting, human susceptibility, and human mobility. We find that there is considerable variation across the country in both overall pandemic impact on dengue and the relative importance of individual drivers. Increased understanding of the causal mechanisms driving the 2020 dengue season helps mitigate some of the data gaps caused by the COVID-19 pandemic and is critical to developing effective public health interventions to control dengue in the future.
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Affiliation(s)
- K. O. Roster
- Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, Brazil
| | - T. Martinelli
- Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, Brazil
| | - C. Connaughton
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- London Mathematical Laboratory, London, United Kingdom
| | - M. Santillana
- Machine Intelligence Group for the Betterment of Health and the Environment, Network Science Institute, Northeastern University, Boston, MA, USA
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - F. A. Rodrigues
- Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, Brazil
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9
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Assad DBN, Cara J, Ortega-Mier M. Comparing Short-Term Univariate and Multivariate Time-Series Forecasting Models in Infectious Disease Outbreak. Bull Math Biol 2023; 85:9. [PMID: 36565344 PMCID: PMC9789525 DOI: 10.1007/s11538-022-01112-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 11/29/2022] [Indexed: 12/25/2022]
Abstract
Predicting infectious disease outbreak impacts on population, healthcare resources and economics and has received a special academic focus during coronavirus (COVID-19) pandemic. Focus on human disease outbreak prediction techniques in current literature, Marques et al. (Predictive models for decision support in the COVID-19 crisis. Springer, Switzerland, 2021) state that there are four main methods to address forecasting problem: compartmental models, classic statistical models, space-state models and machine learning models. We adopt their framework to compare our research with previous works. Besides being divided by methods, forecasting problems can also be divided by the number of variables that are considered to make predictions. Considering this number of variables, forecasting problems can be classified as univariate, causal and multivariate models. Multivariate approaches have been applied in less than 10% of research found. This research is the first attempt to evaluate, over real time-series data of 3 different countries with univariate and multivariate methods to provide a short-term prediction. In literature we found no research with that scope and aim. A comparison of univariate and multivariate methods has been conducted and we concluded that besides the strong potential of multivariate methods, in our research univariate models presented best results in almost all regions' predictions.
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Affiliation(s)
- Daniel Bouzon Nagem Assad
- Universidad Politécnica de Madrid, Department of Organization Engineering, Business Administration and Statistics, Escuela Técnica Superior de Ingenieros Industriales, José Gutiérrez Abascal, 2, 28006 Madrid, Spain ,Universidade do Estado do Rio de Janeiro, Rua São Francisco Xavier, 524, Maracanã, 20550-900 Rio de Janeiro, Brazil
| | - Javier Cara
- Universidad Politécnica de Madrid, Department of Organization Engineering, Business Administration and Statistics, Escuela Técnica Superior de Ingenieros Industriales, José Gutiérrez Abascal, 2, 28006 Madrid, Spain
| | - Miguel Ortega-Mier
- Universidad Politécnica de Madrid, Department of Organization Engineering, Business Administration and Statistics, Escuela Técnica Superior de Ingenieros Industriales, José Gutiérrez Abascal, 2, 28006 Madrid, Spain
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10
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Brown TS, Robinson DA, Buckee CO, Mathema B. Connecting the dots: understanding how human mobility shapes TB epidemics. Trends Microbiol 2022; 30:1036-1044. [PMID: 35597716 PMCID: PMC10068677 DOI: 10.1016/j.tim.2022.04.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/18/2022] [Accepted: 04/19/2022] [Indexed: 01/13/2023]
Abstract
Tuberculosis (TB) remains a leading infectious cause of death worldwide. Reducing TB infections and TB-related deaths rests ultimately on stopping forward transmission from infectious to susceptible individuals. Critical to this effort is understanding how human host mobility shapes the transmission and dispersal of new or existing strains of Mycobacterium tuberculosis (Mtb). Important questions remain unanswered. What kinds of mobility, over what temporal and spatial scales, facilitate TB transmission? How do human mobility patterns influence the dispersal of novel Mtb strains, including emergent drug-resistant strains? This review summarizes the current state of knowledge on mobility and TB epidemic dynamics, using examples from three topic areas, including inference of genetic and spatial clustering of infections, delineating source-sink dynamics, and mapping the dispersal of novel TB strains, to examine scientific questions and methodological issues within this topic. We also review new data sources for measuring human mobility, including mobile phone-associated movement data, and discuss important limitations on their use in TB epidemiology.
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Affiliation(s)
- Tyler S Brown
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Infectious Diseases Division, Massachusetts General Hospital, Boston, MA, USA
| | - D Ashley Robinson
- Department of Microbiology and Immunology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Caroline O Buckee
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Barun Mathema
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.
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11
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Spatially weak syncronization of spreading pattern between Aedes Albopictus and dengue fever. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2022.110123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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12
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Li Z. Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13555. [PMID: 36294134 PMCID: PMC9603269 DOI: 10.3390/ijerph192013555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 10/15/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and processing multi-source data to generate risk predictors and consuming significant time and computational resources to train and validate models locally. In this context, this study proposed a framework for dengue risk prediction by integrating big geospatial data cloud computing based on Google Earth Engine (GEE) platform and artificial intelligence modeling on the Google Colab platform. It enables defining the epidemiological calendar, delineating the predominant area of dengue transmission in cities, generating the data of risk predictors, and defining multi-date ahead prediction scenarios. We implemented the experiments based on weekly dengue cases during 2013-2020 in the Federal District and Fortaleza, Brazil to evaluate the performance of the proposed framework. Four predictors were considered, including total rainfall (Rsum), mean temperature (Tmean), mean relative humidity (RHmean), and mean normalized difference vegetation index (NDVImean). Three models (i.e., random forest (RF), long-short term memory (LSTM), and LSTM with attention mechanism (LSTM-ATT)), and two modeling scenarios (i.e., modeling with or without dengue cases) were set to implement 1- to 4-week ahead predictions. A total of 24 models were built, and the results showed in general that LSTM and LSTM-ATT models outperformed RF models; modeling could benefit from using historical dengue cases as one of the predictors, and it makes the predicted curve fluctuation more stable compared with that only using climate and environmental factors; attention mechanism could further improve the performance of LSTM models. This study provides implications for future dengue risk prediction in terms of the effectiveness of GEE-based big geospatial data processing for risk predictor generation and Google Colab-based risk modeling and presents the benefits of using historical dengue data as one of the input features and the attention mechanism for LSTM modeling.
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Affiliation(s)
- Zhichao Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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13
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Roster K, Connaughton C, Rodrigues FA. Machine-Learning-Based Forecasting of Dengue Fever in Brazilian Cities Using Epidemiologic and Meteorological Variables. Am J Epidemiol 2022; 191:1803-1812. [PMID: 35584963 DOI: 10.1093/aje/kwac090] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 03/15/2022] [Accepted: 05/10/2022] [Indexed: 01/29/2023] Open
Abstract
Dengue is a serious public health concern in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease. In this study, we developed a model for predicting monthly dengue cases in Brazilian cities 1 month ahead, using data from 2007-2019. We compared different machine learning algorithms and feature selection methods using epidemiologic and meteorological variables. We found that different models worked best in different cities, and a random forests model trained on monthly dengue cases performed best overall. It produced lower errors than a seasonal naive baseline model, gradient boosting regression, a feed-forward neural network, or support vector regression. For each city, we computed the mean absolute error between predictions and true monthly numbers of dengue cases on the test data set. The median error across all cities was 12.2 cases. This error was reduced to 11.9 when selecting the optimal combination of algorithm and input features for each city individually. Machine learning and especially decision tree ensemble models may contribute to dengue surveillance in Brazil, as they produce low out-of-sample prediction errors for a geographically diverse set of cities.
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14
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Choisy M, McBride A, Chambers M, Ho Quang C, Nguyen Quang H, Xuan Chau NT, Thi GN, Bonell A, Evans M, Ming D, Ngo-Duc T, Quang Thai P, Dang Giang DH, Dan Thanh HN, Ngoc Nhung H, Lowe R, Maude R, Elyazar I, Surendra H, Ashley EA, Thwaites L, van Doorn HR, Kestelyn E, Dondorp AM, Thwaites G, Vinh Chau NV, Yacoub S. Climate change and health in Southeast Asia - defining research priorities and the role of the Wellcome Trust Africa Asia Programmes. Wellcome Open Res 2022; 6:278. [PMID: 36176331 PMCID: PMC9493397 DOI: 10.12688/wellcomeopenres.17263.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2022] [Indexed: 11/20/2022] Open
Abstract
This article summarises a recent virtual meeting organised by the Oxford University Clinical Research Unit in Vietnam on the topic of climate change and health, bringing local partners, faculty and external collaborators together from across the Wellcome and Oxford networks. Attendees included invited local and global climate scientists, clinicians, modelers, epidemiologists and community engagement practitioners, with a view to setting priorities, identifying synergies and fostering collaborations to help define the regional climate and health research agenda. In this summary paper, we outline the major themes and topics that were identified and what will be needed to take forward this research for the next decade. We aim to take a broad, collaborative approach to including climate science in our current portfolio where it touches on infectious diseases now, and more broadly in our future research directions. We will focus on strengthening our research portfolio on climate-sensitive diseases, and supplement this with high quality data obtained from internal studies and external collaborations, obtained by multiple methods, ranging from traditional epidemiology to innovative technology and artificial intelligence and community-led research. Through timely agenda setting and involvement of local stakeholders, we aim to help support and shape research into global heating and health in the region.
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Affiliation(s)
- Marc Choisy
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Angela McBride
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Global Health and Infection, Brighton and Sussex Medical School, Brighton, UK
| | - Mary Chambers
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Chanh Ho Quang
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | - Huy Nguyen Quang
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | | | - Giang Nguyen Thi
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | - Ana Bonell
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Megan Evans
- Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK
| | - Damien Ming
- Department of Infectious Disease, Imperial College London, London, UK
| | - Thanh Ngo-Duc
- University of Science and Technology of Hanoi, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Pham Quang Thai
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
- School of Preventative Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam
| | | | - Ho Ngoc Dan Thanh
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | - Hoang Ngoc Nhung
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | - Rachel Lowe
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Richard Maude
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
- Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
| | - Iqbal Elyazar
- Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia
| | - Henry Surendra
- Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia
| | - Elizabeth A. Ashley
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Vientiane, Lao People's Democratic Republic
| | - Louise Thwaites
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - H. Rogier van Doorn
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Evelyne Kestelyn
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Arjen M. Dondorp
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
- Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
| | - Guy Thwaites
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | | | - Sophie Yacoub
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
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15
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Choisy M, McBride A, Chambers M, Ho Quang C, Nguyen Quang H, Xuan Chau NT, Thi GN, Bonell A, Evans M, Ming D, Ngo-Duc T, Quang Thai P, Dang Giang DH, Dan Thanh HN, Ngoc Nhung H, Lowe R, Maude R, Elyazar I, Surendra H, Ashley EA, Thwaites L, van Doorn HR, Kestelyn E, Dondorp AM, Thwaites G, Vinh Chau NV, Yacoub S. Climate change and health in Southeast Asia - defining research priorities and the role of the Wellcome Trust Africa Asia Programmes. Wellcome Open Res 2022; 6:278. [PMID: 36176331 PMCID: PMC9493397 DOI: 10.12688/wellcomeopenres.17263.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/22/2022] [Indexed: 05/18/2024] Open
Abstract
This article summarises a recent virtual meeting organised by the Oxford University Clinical Research Unit in Vietnam on the topic of climate change and health, bringing local partners, faculty and external collaborators together from across the Wellcome and Oxford networks. Attendees included invited local and global climate scientists, clinicians, modelers, epidemiologists and community engagement practitioners, with a view to setting priorities, identifying synergies and fostering collaborations to help define the regional climate and health research agenda. In this summary paper, we outline the major themes and topics that were identified and what will be needed to take forward this research for the next decade. We aim to take a broad, collaborative approach to including climate science in our current portfolio where it touches on infectious diseases now, and more broadly in our future research directions. We will focus on strengthening our research portfolio on climate-sensitive diseases, and supplement this with high quality data obtained from internal studies and external collaborations, obtained by multiple methods, ranging from traditional epidemiology to innovative technology and artificial intelligence and community-led research. Through timely agenda setting and involvement of local stakeholders, we aim to help support and shape research into global heating and health in the region.
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Affiliation(s)
- Marc Choisy
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Angela McBride
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Global Health and Infection, Brighton and Sussex Medical School, Brighton, UK
| | - Mary Chambers
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Chanh Ho Quang
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | - Huy Nguyen Quang
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | | | - Giang Nguyen Thi
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | - Ana Bonell
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Megan Evans
- Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK
| | - Damien Ming
- Department of Infectious Disease, Imperial College London, London, UK
| | - Thanh Ngo-Duc
- University of Science and Technology of Hanoi, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Pham Quang Thai
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
- School of Preventative Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam
| | | | - Ho Ngoc Dan Thanh
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | - Hoang Ngoc Nhung
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | - Rachel Lowe
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Richard Maude
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
- Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
| | - Iqbal Elyazar
- Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia
| | - Henry Surendra
- Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia
| | - Elizabeth A. Ashley
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Vientiane, Lao People's Democratic Republic
| | - Louise Thwaites
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - H. Rogier van Doorn
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Evelyne Kestelyn
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Arjen M. Dondorp
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
- Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
| | - Guy Thwaites
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | | | - Sophie Yacoub
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
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Calvez E, Bounmany P, Somlor S, Xaybounsou T, Viengphouthong S, Keosenhom S, Brey PT, Lacoste V, Grandadam M. Multiple chikungunya virus introductions in Lao PDR from 2014 to 2020. PLoS One 2022; 17:e0271439. [PMID: 35839218 PMCID: PMC9286254 DOI: 10.1371/journal.pone.0271439] [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: 10/21/2021] [Accepted: 06/30/2022] [Indexed: 12/04/2022] Open
Abstract
The first documented chikungunya virus (CHIKV) outbreak in Lao People’s Democratic Republic (Lao PDR) occurred in 2012–2013. Since then, several imported and a few autochthonous cases were identified by the national arbovirus surveillance network. The present study aimed to summarize the main genetic features of the CHIKV strains detected in Lao PDR between 2014 and 2020. Samples from Lao patients presenting symptoms compatible with a CHIKV infection were centralized in Vientiane Capital city for real-time RT-PCR screening. Molecular epidemiology was performed by sequencing the E2-6K-E1 region. From 2014 to 2020, two Asian lineage isolates (e.g. French Polynesia; Indonesia), one ECSA-IOL lineage isolate (e.g. Thailand) and one unclassified (e.g. Myanmar) were imported in Vientiane Capital city. Sequences from the autochthonous cases recorded in the Central and Southern parts of the country between July and September 2020 belonged to the ECSA-IOL lineage and clustered with CHIKV strains recently detected in neighboring countries. These results demonstrate the multiple CHIKV introductions in Lao PDR since 2014 and provide evidence for sporadic and time-limited circulation of CHIKV in the country. Even if the circulation of CHIKV seems to be geographically and temporally limited in Lao PDR, the development of international tourism and trade may cause future outbreaks of CHIKV in the country and at the regional level.
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Affiliation(s)
- Elodie Calvez
- Arbovirus and Emerging Viral Diseases Laboratory, Institut Pasteur du Laos, Vientiane, Lao People’s Democratic Republic
- * E-mail:
| | - Phaithong Bounmany
- Arbovirus and Emerging Viral Diseases Laboratory, Institut Pasteur du Laos, Vientiane, Lao People’s Democratic Republic
| | - Somphavanh Somlor
- Arbovirus and Emerging Viral Diseases Laboratory, Institut Pasteur du Laos, Vientiane, Lao People’s Democratic Republic
| | - Thonglakhone Xaybounsou
- Arbovirus and Emerging Viral Diseases Laboratory, Institut Pasteur du Laos, Vientiane, Lao People’s Democratic Republic
| | - Souksakhone Viengphouthong
- Arbovirus and Emerging Viral Diseases Laboratory, Institut Pasteur du Laos, Vientiane, Lao People’s Democratic Republic
| | - Sitsana Keosenhom
- Arbovirus and Emerging Viral Diseases Laboratory, Institut Pasteur du Laos, Vientiane, Lao People’s Democratic Republic
| | - Paul T. Brey
- Medical Entomology and Vector-Borne Disease Unit, Institut Pasteur du Laos, Vientiane, Lao People’s Democratic Republic
| | - Vincent Lacoste
- Arbovirus and Emerging Viral Diseases Laboratory, Institut Pasteur du Laos, Vientiane, Lao People’s Democratic Republic
| | - Marc Grandadam
- Arbovirus and Emerging Viral Diseases Laboratory, Institut Pasteur du Laos, Vientiane, Lao People’s Democratic Republic
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17
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Li Z, Gurgel H, Xu L, Yang L, Dong J. Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling. BIOLOGY 2022; 11:biology11020169. [PMID: 35205036 PMCID: PMC8869738 DOI: 10.3390/biology11020169] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 01/04/2022] [Accepted: 01/17/2022] [Indexed: 11/26/2022]
Abstract
Simple Summary Forecasting dengue cases often face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without historical dengue cases. With the advance of the geospatial big data cloud computing in Google Earth Engine and deep learning, this study proposed an efficient framework of dengue prediction at an epidemiological week basis using geospatial big data analysis in Google Earth Engine and Long Short Term Memory modeling. We focused on the dengue epidemics in the Federal District of Brazil during 2007–2019. Based on Google Earth Engine and epidemiological calendar, we computed the weekly composite for each dengue driving factor, and spatially aggregated the pixel values into dengue transmission areas to generate the time series of driving factors. A multi-step-ahead Long Short Term Memory modeling was used, and the time-differenced natural log-transformed dengue cases and the time series of driving factors were considered as outcomes and explantary factors, respectively, with two modeling scenarios (with and without historical cases). The performance is better when historical cases were used, and the 5-weeks-ahead forecast has the best performance. Abstract Timely and accurate forecasts of dengue cases are of great importance for guiding disease prevention strategies, but still face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation capability due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without the application of historical case information. Geospatial big data, cloud computing platforms (e.g., Google Earth Engine, GEE), and emerging deep learning algorithms (e.g., long short term memory, LSTM) provide new opportunities for advancing these efforts. Here, we focused on the dengue epidemics in the urban agglomeration of the Federal District of Brazil (FDB) during 2007–2019. A new framework was proposed using geospatial big data analysis in the Google Earth Engine (GEE) platform and long short term memory (LSTM) modeling for dengue case forecasts over an epidemiological week basis. We first defined a buffer zone around an impervious area as the main area of dengue transmission by considering the impervious area as a human-dominated area and used the maximum distance of the flight range of Aedes aegypti and Aedes albopictus as a buffer distance. Those zones were used as units for further attribution analyses of dengue epidemics by aggregating the pixel values into the zones. The near weekly composite of potential driving factors was generated in GEE using the epidemiological weeks during 2007–2019, from the relevant geospatial data with daily or sub-daily temporal resolution. A multi-step-ahead LSTM model was used, and the time-differenced natural log-transformed dengue cases were used as outcomes. Two modeling scenarios (with and without historical dengue cases) were set to examine the potential of historical information on dengue forecasts. The results indicate that the performance was better when historical dengue cases were used and the 5-weeks-ahead forecast had the best performance, and the peak of a large outbreak in 2019 was accurately forecasted. The proposed framework in this study suggests the potential of the GEE platform, the LSTM algorithm, as well as historical information for dengue risk forecasting, which can easily be extensively applied to other regions or globally for timely and practical dengue forecasts.
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Affiliation(s)
- Zhichao Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; (Z.L.); (L.Y.)
| | - Helen Gurgel
- Department of Geography, University of Brasilia (UnB), Brasilia 70910-900, Brazil;
| | - Lei Xu
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China;
| | - Linsheng Yang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; (Z.L.); (L.Y.)
| | - Jinwei Dong
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; (Z.L.); (L.Y.)
- Correspondence:
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18
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Brown TS, Arogbokun O, Buckee CO, Chang HH. Distinguishing gene flow between malaria parasite populations. PLoS Genet 2021; 17:e1009335. [PMID: 34928954 PMCID: PMC8726502 DOI: 10.1371/journal.pgen.1009335] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/04/2022] [Accepted: 10/12/2021] [Indexed: 11/19/2022] Open
Abstract
Measuring gene flow between malaria parasite populations in different geographic locations can provide strategic information for malaria control interventions. Multiple important questions pertaining to the design of such studies remain unanswered, limiting efforts to operationalize genomic surveillance tools for routine public health use. This report examines the use of population-level summaries of genetic divergence (FST) and relatedness (identity-by-descent) to distinguish levels of gene flow between malaria populations, focused on field-relevant questions about data size, sampling, and interpretability of observations from genomic surveillance studies. To do this, we use P. falciparum whole genome sequence data and simulated sequence data approximating malaria populations evolving under different current and historical epidemiological conditions. We employ mobile-phone associated mobility data to estimate parasite migration rates over different spatial scales and use this to inform our analysis. This analysis underscores the complementary nature of divergence- and relatedness-based metrics for distinguishing gene flow over different temporal and spatial scales and characterizes the data requirements for using these metrics in different contexts. Our results have implications for the design and implementation of malaria genomic surveillance studies.
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Affiliation(s)
- Tyler S. Brown
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Infectious Diseases Division, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Olufunmilayo Arogbokun
- Infectious Disease Epidemiology and Ecology Lab, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America
| | - Caroline O. Buckee
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Hsiao-Han Chang
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu City, Taiwan
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19
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Choisy M, McBride A, Chambers M, Ho Quang C, Nguyen Quang H, Xuan Chau NT, Thi GN, Bonell A, Evans M, Ming D, Ngo-Duc T, Quang Thai P, Dang Giang DH, Dan Thanh HN, Ngoc Nhung H, Lowe R, Maude R, Elyazar I, Surendra H, Ashley EA, Thwaites L, van Doorn HR, Kestelyn E, Dondorp AM, Thwaites G, Vinh Chau NV, Yacoub S. Climate change and health in Southeast Asia - defining research priorities and the role of the Wellcome Trust Africa Asia Programmes. Wellcome Open Res 2021; 6:278. [PMID: 36176331 PMCID: PMC9493397 DOI: 10.12688/wellcomeopenres.17263.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/01/2021] [Indexed: 02/26/2024] Open
Abstract
This article summarises a recent virtual meeting organised by the Oxford University Clinical Research Unit in Vietnam on the topic of climate change and health, bringing local partners, faculty and external collaborators together from across the Wellcome and Oxford networks. Attendees included invited local and global climate scientists, clinicians, modelers, epidemiologists and community engagement practitioners, with a view to setting priorities, identifying synergies and fostering collaborations to help define the regional climate and health research agenda. In this summary paper, we outline the major themes and topics that were identified and what will be needed to take forward this research for the next decade. We aim to take a broad, collaborative approach to including climate science in our current portfolio where it touches on infectious diseases now, and more broadly in our future research directions. We will focus on strengthening our research portfolio on climate-sensitive diseases, and supplement this with high quality data obtained from internal studies and external collaborations, obtained by multiple methods, ranging from traditional epidemiology to innovative technology and artificial intelligence and community-led research. Through timely agenda setting and involvement of local stakeholders, we aim to help support and shape research into global heating and health in the region.
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Affiliation(s)
- Marc Choisy
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Angela McBride
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Global Health and Infection, Brighton and Sussex Medical School, Brighton, UK
| | - Mary Chambers
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Chanh Ho Quang
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | - Huy Nguyen Quang
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | | | - Giang Nguyen Thi
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | - Ana Bonell
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Megan Evans
- Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK
| | - Damien Ming
- Department of Infectious Disease, Imperial College London, London, UK
| | - Thanh Ngo-Duc
- University of Science and Technology of Hanoi, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Pham Quang Thai
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
- School of Preventative Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam
| | | | - Ho Ngoc Dan Thanh
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | - Hoang Ngoc Nhung
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
| | - Rachel Lowe
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Richard Maude
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
- Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
| | - Iqbal Elyazar
- Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia
| | - Henry Surendra
- Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia
| | - Elizabeth A. Ashley
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Vientiane, Lao People's Democratic Republic
| | - Louise Thwaites
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - H. Rogier van Doorn
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Evelyne Kestelyn
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Arjen M. Dondorp
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
- Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand
| | - Guy Thwaites
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | | | - Sophie Yacoub
- Oxford University Clinical Research Unit, Ho Chi Minh City and Hanoi, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
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20
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Kiang MV, Chen JT, Krieger N, Buckee CO, Alexander MJ, Baker JT, Buckner RL, Coombs G, Rich-Edwards JW, Carlson KW, Onnela JP. Sociodemographic characteristics of missing data in digital phenotyping. Sci Rep 2021; 11:15408. [PMID: 34326370 PMCID: PMC8322366 DOI: 10.1038/s41598-021-94516-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 07/12/2021] [Indexed: 11/09/2022] Open
Abstract
The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect longitudinal, diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphones for "digital phenotyping," the collection and analysis of raw phone sensor and log data to study the lived experiences of subjects in their natural environments using their own devices. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. In this meta-study using individual-level data from six different studies, we examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression with study- and user-level random intercepts. Sensitivity analyses including alternative model specification and stratified models were conducted. We found that iOS users had lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection, but rates did not differ by sex, education, or age. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies.
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Affiliation(s)
- Mathew V Kiang
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Jarvis T Chen
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Nancy Krieger
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Caroline O Buckee
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Monica J Alexander
- Department of Sociology, University of Toronto, Toronto, ON, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Justin T Baker
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
| | - Randy L Buckner
- Department of Psychology, Harvard University, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Garth Coombs
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - Janet W Rich-Edwards
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Women's Health, Department of Medicine, Brigham and Women's Hospital and Harvard Medical, Boston, MA, USA
| | - Kenzie W Carlson
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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21
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Castro LA, Generous N, Luo W, Pastore y Piontti A, Martinez K, Gomes MFC, Osthus D, Fairchild G, Ziemann A, Vespignani A, Santillana M, Manore CA, Del Valle SY. Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil. PLoS Negl Trop Dis 2021; 15:e0009392. [PMID: 34019536 PMCID: PMC8174735 DOI: 10.1371/journal.pntd.0009392] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 06/03/2021] [Accepted: 04/16/2021] [Indexed: 12/18/2022] Open
Abstract
Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010-2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the "normalized burn ratio," experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of "adaptive models" rather than "one-size-fits-all" models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.
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Affiliation(s)
- Lauren A. Castro
- Information Systems and Modeling Group, Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Nicholas Generous
- National Security and Defense Program Office, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Wei Luo
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
- Geography Department, National University of Singapore, Singapore, Singapore
| | - Ana Pastore y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Kaitlyn Martinez
- Information Systems and Modeling Group, Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Department of Mathematics & Statistics, Colorado School of Mines, Golden, Colorado, United States of America
| | - Marcelo F. C. Gomes
- Núcleo de Métodos Analíticos em Vigilância Epidemiológica Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brazil
| | - Dave Osthus
- Statistical Sciences Group, Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Geoffrey Fairchild
- Information Systems and Modeling Group, Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Amanda Ziemann
- Space Data Science and Systems Group, Intelligence and Space Research Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts, United States of America
| | - Mauricio Santillana
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America
| | - Carrie A. Manore
- Information Systems and Modeling Group, Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Sara Y. Del Valle
- Information Systems and Modeling Group, Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
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22
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Reconstructing unseen transmission events to infer dengue dynamics from viral sequences. Nat Commun 2021; 12:1810. [PMID: 33753725 PMCID: PMC7985522 DOI: 10.1038/s41467-021-21888-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 02/18/2021] [Indexed: 11/18/2022] Open
Abstract
For most pathogens, transmission is driven by interactions between the behaviours of infectious individuals, the behaviours of the wider population, the local environment, and immunity. Phylogeographic approaches are currently unable to disentangle the relative effects of these competing factors. We develop a spatiotemporally structured phylogenetic framework that addresses these limitations by considering individual transmission events, reconstructed across spatial scales. We apply it to geocoded dengue virus sequences from Thailand (N = 726 over 18 years). We find infected individuals spend 96% of their time in their home community compared to 76% for the susceptible population (mainly children) and 42% for adults. Dynamic pockets of local immunity make transmission more likely in places with high heterotypic immunity and less likely where high homotypic immunity exists. Age-dependent mixing of individuals and vector distributions are not important in determining spread. This approach provides previously unknown insights into one of the most complex disease systems known and will be applicable to other pathogens. Phylogeographic analyses can provide broad descriptions of the spread of pathogens between populations, but are limited by incomplete sampling. Here, the authors develop an inference framework that reconstructs sequential transmission events and use it to characterise dynamics of dengue in Thailand.
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