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- Publisher Website: 10.3390/ijerph182212018
- Scopus: eid_2-s2.0-85118981347
- PMID: 34831785
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Article: Integrating spatial modelling and space–time pattern mining analytics for vector disease-related health perspectives: A case of dengue fever in Pakistan
Title | Integrating spatial modelling and space–time pattern mining analytics for vector disease-related health perspectives: A case of dengue fever in Pakistan |
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Authors | |
Keywords | Dengue fever Disease mapping Geographic information systems I-SpaDE Public health planning Spatial–temporal analysis |
Issue Date | 2021 |
Citation | International Journal of Environmental Research and Public Health, 2021, v. 18, n. 22, article no. 12018 How to Cite? |
Abstract | The spatial–temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space–time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007–2016 as an example vector disease. The most significant clustering is evident during the years 2007–2008, 2010–2011, 2013, and 2016. Mostly, the clusters are found within the city’s central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics. |
Persistent Identifier | http://hdl.handle.net/10722/349630 |
ISSN | 2019 Impact Factor: 2.849 2023 SCImago Journal Rankings: 0.808 |
DC Field | Value | Language |
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dc.contributor.author | Naqvi, Syed Ali Asad | - |
dc.contributor.author | Sajjad, Muhammad | - |
dc.contributor.author | Waseem, Liaqat Ali | - |
dc.contributor.author | Khalid, Shoaib | - |
dc.contributor.author | Shaikh, Saima | - |
dc.contributor.author | Kazmi, Syed Jamil Hasan | - |
dc.date.accessioned | 2024-10-17T06:59:49Z | - |
dc.date.available | 2024-10-17T06:59:49Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | International Journal of Environmental Research and Public Health, 2021, v. 18, n. 22, article no. 12018 | - |
dc.identifier.issn | 1661-7827 | - |
dc.identifier.uri | http://hdl.handle.net/10722/349630 | - |
dc.description.abstract | The spatial–temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space–time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007–2016 as an example vector disease. The most significant clustering is evident during the years 2007–2008, 2010–2011, 2013, and 2016. Mostly, the clusters are found within the city’s central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics. | - |
dc.language | eng | - |
dc.relation.ispartof | International Journal of Environmental Research and Public Health | - |
dc.subject | Dengue fever | - |
dc.subject | Disease mapping | - |
dc.subject | Geographic information systems | - |
dc.subject | I-SpaDE | - |
dc.subject | Public health planning | - |
dc.subject | Spatial–temporal analysis | - |
dc.title | Integrating spatial modelling and space–time pattern mining analytics for vector disease-related health perspectives: A case of dengue fever in Pakistan | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.3390/ijerph182212018 | - |
dc.identifier.pmid | 34831785 | - |
dc.identifier.scopus | eid_2-s2.0-85118981347 | - |
dc.identifier.volume | 18 | - |
dc.identifier.issue | 22 | - |
dc.identifier.spage | article no. 12018 | - |
dc.identifier.epage | article no. 12018 | - |
dc.identifier.eissn | 1660-4601 | - |