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Flores Lima M, Cotton J, Marais M, Faggian R. Modelling the risk of Japanese encephalitis virus in Victoria, Australia, using an expert-systems approach. BMC Infect Dis 2024; 24:60. [PMID: 38191322 PMCID: PMC10775567 DOI: 10.1186/s12879-023-08741-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 10/23/2023] [Indexed: 01/10/2024] Open
Abstract
Predictive models for vector-borne diseases (VBDs) are instrumental to understanding the potential geographic spread of VBDs and therefore serve as useful tools for public health decision-making. However, predicting the emergence of VBDs at the micro-, local, and regional levels presents challenges, as the importance of risk factors can vary spatially and temporally depending on climatic factors and vector and host abundance and preferences. We propose an expert-systems-based approach that uses an analytical hierarchy process (AHP) deployed within a geographic information system (GIS), to predict areas susceptible to the risk of Japanese encephalitis virus (JEV) emergence. This modelling approach produces risk maps, identifying micro-level risk areas with the potential for disease emergence. The results revealed that climatic conditions, especially the minimum temperature and precipitation required for JEV transmission, contributed to high-risk conditions developed during January and March of 2022 in Victora. Compared to historical climate records, the risk of JEV emergence was increased in most parts of the state due to climate. Importantly, the model accurately predicted 7 out of the 8 local government areas that reported JEV-positive cases during the outbreak of 2022 in Victorian piggeries. This underscores the model's potential as a reliable tool for supporting local risk assessments in the face of evolving climate change.
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Affiliation(s)
- Mariel Flores Lima
- Centre for Regional and Rural Futures, Faculty of Science, Engineering and Built Environment, Deakin University, Melbourne, VIC, Australia.
| | - Jacqueline Cotton
- National Centre for Farmer Health, School of Medicine, Deakin University, Hamilton, VIC, Australia
| | - Monique Marais
- Centre for Regional and Rural Futures, Faculty of Science, Engineering and Built Environment, Deakin University, Melbourne, VIC, Australia
| | - Robert Faggian
- Centre for Regional and Rural Futures, Faculty of Science, Engineering and Built Environment, Deakin University, Melbourne, VIC, Australia
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Pakaya R, Daniel D, Widayani P, Utarini A. Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review. BMC Public Health 2023; 23:2448. [PMID: 38062404 PMCID: PMC10701958 DOI: 10.1186/s12889-023-17185-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 11/08/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Creating a spatial model of dengue fever risk is challenging duet to many interrelated factors that could affect dengue. Therefore, it is crucial to understand how these critical factors interact and to create reliable predictive models that can be used to mitigate and control the spread of dengue. METHODS This scoping review aims to provide a comprehensive overview of the important predictors, and spatial modelling tools capable of producing Dengue Haemorrhagic Fever (DHF) risk maps. We conducted a methodical exploration utilizing diverse sources, i.e., PubMed, Scopus, Science Direct, and Google Scholar. The following data were extracted from articles published between January 2011 to August 2022: country, region, administrative level, type of scale, spatial model, dengue data use, and categories of predictors. Applying the eligibility criteria, 45 out of 1,349 articles were selected. RESULTS A variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and machine learning technique. We found that there was no pattern of predictor use associated with particular approaches. Instead, a wide range of predictors was used to create the DHF risk maps. These predictors may include climatology factors (e.g., temperature, rainfall, humidity), epidemiological factors (population, demographics, socio-economic, previous DHF cases), environmental factors (land-use, elevation), and relevant factors. CONCLUSIONS DHF risk spatial models are useful tools for detecting high-risk locations and driving proactive public health initiatives. Relying on geographical and environmental elements, these models ignored the impact of human behaviour and social dynamics. To improve the prediction accuracy, there is a need for a more comprehensive approach to understand DHF transmission dynamics.
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Affiliation(s)
- Ririn Pakaya
- Doctoral Program in Public Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.
- Department of Public Health, Public Health Faculty, Universitas Gorontalo, Gorontalo, Indonesia.
| | - D Daniel
- Department of Health Behaviour, Environment and Social Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Prima Widayani
- Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Adi Utarini
- Doctoral Program in Public Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
- Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
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Lim AY, Jafari Y, Caldwell JM, Clapham HE, Gaythorpe KAM, Hussain-Alkhateeb L, Johansson MA, Kraemer MUG, Maude RJ, McCormack CP, Messina JP, Mordecai EA, Rabe IB, Reiner RC, Ryan SJ, Salje H, Semenza JC, Rojas DP, Brady OJ. A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk. BMC Infect Dis 2023; 23:708. [PMID: 37864153 PMCID: PMC10588093 DOI: 10.1186/s12879-023-08717-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 10/16/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. METHODS We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). RESULTS We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. CONCLUSIONS Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.
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Affiliation(s)
- Ah-Young Lim
- Department of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
- Centre for Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
| | - Yalda Jafari
- 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, UK
| | - Jamie M Caldwell
- High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA
| | - Hannah E Clapham
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Laith Hussain-Alkhateeb
- School of Public Health and Community Medicine, Sahlgrenska Academy, Institute of Medicine, Global Health, University of Gothenburg, Gothenburg, Sweden
- Population Health Research Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
| | - Michael A Johansson
- Dengue Branch, Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico, USA
| | | | - Richard J 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, UK
| | - Clare P McCormack
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Jane P Messina
- School of Geography and the Environment, University of Oxford, Oxford, UK
- Oxford School of Global and Area Studies, University of Oxford, Oxford, UK
| | - Erin A Mordecai
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Ingrid B Rabe
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
- Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Sadie J Ryan
- Department of Geography and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Jan C Semenza
- Department of Public Health and Clinical Medicine, Section of Sustainable Health, Umeå University, Umeå, Sweden
| | - Diana P Rojas
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | - Oliver J Brady
- Department of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
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Rubio C, Alfaro M, Mejia-Giraldo A, Fuertes G, Mosquera R, Vargas M. Multivariate analysis in data science for the geospatial distribution of the breast cancer mortality rate in Colombia. Front Oncol 2023; 12:1055655. [PMID: 36686819 PMCID: PMC9853892 DOI: 10.3389/fonc.2022.1055655] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 12/13/2022] [Indexed: 01/07/2023] Open
Abstract
This research is framed in the area of biomathematics and contributes to the epidemiological surveillance entities in Colombia to clarify how breast cancer mortality rate (BCM) is spatially distributed in relation to the forest area index (FA) and circulating vehicle index (CV). In this regard, the World Health Organization has highlighted the scarce generation of knowledge that relates mortality from tumor diseases to environmental factors. Quantitative methods based on geospatial data science are used with cross-sectional information from the 2018 census; it's found that the BCM in Colombia is not spatially randomly distributed, but follows cluster aggregation patterns. Under multivariate modeling methods, the research provides sufficient statistical evidence in terms of not rejecting the hypothesis that if a spatial unit has high FA and low CV, then it has significant advantages in terms of lower BCM.
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Affiliation(s)
- Carlos Rubio
- Facultad de Ingeniería, Universidad de San Buenaventura, Cali, Colombia
| | - Miguel Alfaro
- Industrial Engineering Department, University of Santiago de Chile, Santiago, Chile
| | | | - Guillermo Fuertes
- Industrial Engineering Department, University of Santiago de Chile, Santiago, Chile,Faculty of Engineering, Science and Technology, Universidad Bernardo O’Higgins, Santiago, Chile,*Correspondence: Guillermo Fuertes,
| | - Rodolfo Mosquera
- Escuela de Estudios Industriales y Empresariales, Universidad Industrial de Santander, Bucaramanga, Colombia
| | - Manuel Vargas
- Industrial Engineering Department, University of Santiago de Chile, Santiago, Chile
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Harsha G, Anish TS, Rajaneesh A, Prasad MK, Mathew R, Mammen PC, Ajin RS, Kuriakose SL. Dengue risk zone mapping of Thiruvananthapuram district, India: a comparison of the AHP and F-AHP methods. GEOJOURNAL 2022; 88:2449-2470. [PMID: 36157197 PMCID: PMC9483355 DOI: 10.1007/s10708-022-10757-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/06/2022] [Indexed: 05/12/2023]
Abstract
Dengue fever, which is spread by Aedes mosquitoes, has claimed many lives in Kerala, with the Thiruvananthapuram district bearing the brunt of the toll. This study aims to demarcate the dengue risk zones in Thiruvananthapuram district using the analytical hierarchy process (AHP) and the fuzzy-AHP (F-AHP) methods. For the risk modelling, geo-environmental factors (normalized difference vegetation index, land surface temperature, topographic wetness index, land use/land cover types, elevation, normalized difference built-up index) and demographic factors (household density, population density) have been utilized. The ArcGIS 10.8 and ERDAS Imagine 8.4 software tools have been used to derive the risk zone maps. The area of the risk maps is classified into five zones. The dengue risk zone maps were validated using dengue case data collected from the Integrated Disease Surveillance Programme portal. From the receiver operating characteristic (ROC) curve analysis and the area under the ROC curve (AUC) values, it is proved that the F-AHP method (AUC value of 0.971) has comparatively more prediction capability than the AHP method (AUC value of 0.954) in demarcating the dengue risk zones. Also, based on the comparison of the risk zone map with actual case data, it was confirmed that around 82.87% of the dengue cases occurred in the very high and high-risk zones, thus proving the efficacy of the model. According to the dengue risk map prepared using the F-AHP model, 9.09% of the area of Thiruvananthapuram district is categorized as very high risk. The prepared dengue risk maps will be helpful for decision-makers, staff with the health, and disaster management departments in adopting effective measures to prevent the risks of dengue spread and thereby minimize loss of life.
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Affiliation(s)
- G. Harsha
- School of Fishery Environment, Kerala University of Fisheries and Ocean Studies, Kochi, Kerala India
| | - T. S. Anish
- Department of Community Medicine, Government Medical College, Thiruvananthapuram, Kerala India
| | - A. Rajaneesh
- Department of Geology, University of Kerala, Thiruvananthapuram, India
| | - Megha K. Prasad
- Department of Remote Sensing, Bharathidasan University, Tiruchirappalli, Tamil Nadu India
| | - Ronu Mathew
- Department of Remote Sensing, Bharathidasan University, Tiruchirappalli, Tamil Nadu India
- Kerala State Emergency Operations Centre, Kerala State Disaster Management Authority, Thiruvananthapuram, India
| | - Pratheesh C. Mammen
- Kerala State Emergency Operations Centre, Kerala State Disaster Management Authority, Thiruvananthapuram, India
| | - R. S. Ajin
- Kerala State Emergency Operations Centre, Kerala State Disaster Management Authority, Thiruvananthapuram, India
- Resilience Development Initiative (RDI), Bandung, Indonesia
| | - Sekhar L. Kuriakose
- Kerala State Emergency Operations Centre, Kerala State Disaster Management Authority, Thiruvananthapuram, India
- Faculty for Geo-Information Science and Earth Observation (ITC), Centre for Disaster Resilience (CDR), University of Twente, Enschede, Netherlands
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Yu D, Li X, Yu J, Shi X, Liu P, Tian P. Whether Urbanization Has Intensified the Spread of Infectious Diseases-Renewed Question by the COVID-19 Pandemic. Front Public Health 2021; 9:699710. [PMID: 34900884 PMCID: PMC8652246 DOI: 10.3389/fpubh.2021.699710] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 10/05/2021] [Indexed: 12/30/2022] Open
Abstract
The outbreak of the COVID-19 epidemic has triggered adiscussion of the relationship between urbanization and the spread of infectious diseases. Namely, whether urbanization will exacerbate the spread of infectious diseases. Based on 31 provincial data from 2002 to 2018 in China, the impact of urbanization on the spread of infectious diseases from the dimensions of "population" and "land" is analyzed in this paper by using the GMM (generalized method of moments) model. The empirical study shows that the population increase brought by urbanization does not aggravate the spread of infectious diseases. On the contrary, urban education, employment and entrepreneurship, housing, medical and health care, and other basic public services brought by population urbanization can help reduce the risk of the spread of infectious diseases. The increasing density of buildings caused by land urbanization increases the risk of the spread of infectious diseases. Moreover, the impact of urbanization on the spread of infectious diseases has regional heterogeneity. Therefore, the prevention and control of disease play a crucial role.
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Affiliation(s)
- Dongsheng Yu
- School of Economics, Zhongnan University of Economics and Law, Wuhan, China
| | - Xiaoping Li
- School of Economics, Zhongnan University of Economics and Law, Wuhan, China
| | - Juanjuan Yu
- School of Economics, Zhongnan University of Economics and Law, Wuhan, China
| | - Xunpeng Shi
- Australia-China Relations Institute, University of Technology Sydney, Sydney, NSW, Australia
| | - Pei Liu
- School of Economics, Zhengzhou University of Aeronautics, Zhengzhou, China
| | - Pu Tian
- School of Economics, Zhongnan University of Economics and Law, Wuhan, China
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The Development of Decarbonisation Strategies: A Three-Step Methodology for the Suitable Analysis of Current EVCS Locations Applied to Istanbul, Turkey. ENERGIES 2021. [DOI: 10.3390/en14102756] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
One of the solutions to reduce environmental emissions is related to the deployment of electric vehicles (EVs) with sustainable energy. In order to be able to increase the number of electric vehicles in circulation, it is important to implement optimal planning and design of the infrastructure, with particular reference to areas equipped with charging stations. The suitable analysis of the location of current electric vehicle charging stations (EVCSs) is the central theme of this document. The research focused on the actual location of the charging stations of five major EVCS companies in the province by selecting Istanbul as the study area. The study was conducted through a three-step approach and specifically (i) the application of the analytical hierarchy process (AHP) method for creating the weights of the 6 main and 18 secondary criteria that influence the location of EVCSs; (ii) a geospatial analysis using GIS considering each criterion and developing the suitability map for the locations of EVCSs, and (iii) application of the technique for order preference by similarity to ideal solution (TOPSIS) to evaluate the location performance of current EVCSs. The results show that the ratio between the most suitable and unsuitable areas for the location of EVCSs in Istanbul and the study area is about 5% and 4%, respectively. The results achieved means of improving sustainable urban planning and laying the basis for an assessment of other areas where EVCSs could be placed.
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