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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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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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Yi C, Vajdi A, Ferdousi T, Cohnstaedt LW, Scoglio C. PICTUREE-Aedes: A Web Application for Dengue Data Visualization and Case Prediction. Pathogens 2023; 12:771. [PMID: 37375461 DOI: 10.3390/pathogens12060771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/18/2023] [Accepted: 05/19/2023] [Indexed: 06/29/2023] Open
Abstract
Dengue fever remains a significant public health concern in many tropical and subtropical countries, and there is still a need for a system that can effectively combine global risk assessment with timely incidence forecasting. This research describes an integrated application called PICTUREE-Aedes, which can collect and analyze dengue-related data, display simulation results, and forecast outbreak incidence. PICTUREE-Aedes automatically updates global temperature and precipitation data and contains historical records of dengue incidence (1960-2012) and Aedes mosquito occurrences (1960-2014) in its database. The application utilizes a mosquito population model to estimate mosquito abundance, dengue reproduction number, and dengue risk. To predict future dengue outbreak incidence, PICTUREE-Aedes applies various forecasting techniques, including the ensemble Kalman filter, recurrent neural network, particle filter, and super ensemble forecast, which are all based on user-entered case data. The PICTUREE-Aedes' risk estimation identifies favorable conditions for potential dengue outbreaks, and its forecasting accuracy is validated by available outbreak data from Cambodia.
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Affiliation(s)
- Chunlin Yi
- Department of Electrical and Computer Engineering, College of Engineering, Kansas State University, Manhattan, KS 66506, USA
| | - Aram Vajdi
- Department of Electrical and Computer Engineering, College of Engineering, Kansas State University, Manhattan, KS 66506, USA
| | - Tanvir Ferdousi
- Department of Electrical and Computer Engineering, College of Engineering, Kansas State University, Manhattan, KS 66506, USA
| | - Lee W Cohnstaedt
- National Bio- and Agro-Defense Facility, Agricultural Research Service, United States Department of Agriculture, Manhattan, KS 66502, USA
| | - Caterina Scoglio
- Department of Electrical and Computer Engineering, College of Engineering, Kansas State University, Manhattan, KS 66506, USA
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Leung XY, Islam RM, Adhami M, Ilic D, McDonald L, Palawaththa S, Diug B, Munshi SU, Karim MN. A systematic review of dengue outbreak prediction models: Current scenario and future directions. PLoS Negl Trop Dis 2023; 17:e0010631. [PMID: 36780568 PMCID: PMC9956653 DOI: 10.1371/journal.pntd.0010631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 02/24/2023] [Accepted: 01/29/2023] [Indexed: 02/15/2023] Open
Abstract
Dengue is among the fastest-spreading vector-borne infectious disease, with outbreaks often overwhelm the health system and result in huge morbidity and mortality in its endemic populations in the absence of an efficient warning system. A large number of prediction models are currently in use globally. As such, this study aimed to systematically review the published literature that used quantitative models to predict dengue outbreaks and provide insights about the current practices. A systematic search was undertaken, using the Ovid MEDLINE, EMBASE, Scopus and Web of Science databases for published citations, without time or geographical restrictions. Study selection, data extraction and management process were devised in accordance with the 'Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies' ('CHARMS') framework. A total of 99 models were included in the review from 64 studies. Most models sourced climate (94.7%) and climate change (77.8%) data from agency reports and only 59.6% of the models adjusted for reporting time lag. All included models used climate predictors; 70.7% of them were built with only climate factors. Climate factors were used in combination with climate change factors (13.4%), both climate change and demographic factors (3.1%), vector factors (6.3%), and demographic factors (5.2%). Machine learning techniques were used for 39.4% of the models. Of these, random forest (15.4%), neural networks (23.1%) and ensemble models (10.3%) were notable. Among the statistical (60.6%) models, linear regression (18.3%), Poisson regression (18.3%), generalized additive models (16.7%) and time series/autoregressive models (26.7%) were notable. Around 20.2% of the models reported no validation at all and only 5.2% reported external validation. The reporting of methodology and model performance measures were inadequate in many of the existing prediction models. This review collates plausible predictors and methodological approaches, which will contribute to robust modelling in diverse settings and populations.
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Affiliation(s)
- Xing Yu Leung
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Rakibul M. Islam
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Mohammadmehdi Adhami
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Dragan Ilic
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Lara McDonald
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Shanika Palawaththa
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Basia Diug
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Saif U. Munshi
- Department of Virology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh
| | - Md Nazmul Karim
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- * E-mail:
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