Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BioMed Central Ltd
2023
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| Online Access: | https://repository.ugm.ac.id/286334/1/scopus%20%281%29.bib |
| _version_ | 1826050863428272128 |
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| author | Pakaya, Ririn Daniel, D. Widayani, Prima Utarini, Adi |
| author_facet | Pakaya, Ririn Daniel, D. Widayani, Prima Utarini, Adi |
| author_sort | Pakaya, Ririn |
| collection | UGM |
| description | 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. © 2023, The Author(s). |
| first_indexed | 2024-09-25T03:59:10Z |
| format | Article |
| id | oai:generic.eprints.org:286334 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-09-25T03:59:10Z |
| publishDate | 2023 |
| publisher | BioMed Central Ltd |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2863342024-08-27T07:48:10Z https://repository.ugm.ac.id/286334/ Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review Pakaya, Ririn Daniel, D. Widayani, Prima Utarini, Adi Health and Community Services Public Health and Health Services 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. © 2023, The Author(s). BioMed Central Ltd 2023 Article PeerReviewed text/html en https://repository.ugm.ac.id/286334/1/scopus%20%281%29.bib Pakaya, Ririn and Daniel, D. and Widayani, Prima and Utarini, Adi (2023) Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review. BMC Public Health, 23 (1). 10.1186/s12889-023-17185-3 |
| spellingShingle | Health and Community Services Public Health and Health Services Pakaya, Ririn Daniel, D. Widayani, Prima Utarini, Adi Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review |
| title | Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review |
| title_full | Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review |
| title_fullStr | Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review |
| title_full_unstemmed | Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review |
| title_short | Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review |
| title_sort | spatial model of dengue hemorrhagic fever dhf risk scoping review |
| topic | Health and Community Services Public Health and Health Services |
| url | https://repository.ugm.ac.id/286334/1/scopus%20%281%29.bib |
| work_keys_str_mv | AT pakayaririn spatialmodelofdenguehemorrhagicfeverdhfriskscopingreview AT danield spatialmodelofdenguehemorrhagicfeverdhfriskscopingreview AT widayaniprima spatialmodelofdenguehemorrhagicfeverdhfriskscopingreview AT utariniadi spatialmodelofdenguehemorrhagicfeverdhfriskscopingreview |