Integrating Spatial Modelling and Space–Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan

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 <i>integrated spatial disease evalu...

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Autores principales:	Syed Ali Asad Naqvi, Muhammad Sajjad, Liaqat Ali Waseem, Shoaib Khalid, Saima Shaikh, Syed Jamil Hasan Kazmi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	I-SpaDE spatial–temporal analysis disease mapping Dengue Fever public health planning Geographic Information Systems Medicine R
Acceso en línea:	https://doaj.org/article/91af0e6274d342b3b0f52b523231b716
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id	oai:doaj.org-article:91af0e6274d342b3b0f52b523231b716
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spelling	oai:doaj.org-article:91af0e6274d342b3b0f52b523231b7162021-11-25T17:50:28ZIntegrating Spatial Modelling and Space–Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan10.3390/ijerph1822120181660-46011661-7827https://doaj.org/article/91af0e6274d342b3b0f52b523231b7162021-11-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/22/12018https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601The 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 <i>integrated spatial disease evaluation</i> (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the <i>Kernel Density Estimation</i>, the <i>Optimized Hot Spot Analysis</i>, space–time assessment and prediction, and the <i>Geographically Weighted Regression</i> (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 <i>city’s central functional area</i>. 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.Syed Ali Asad NaqviMuhammad SajjadLiaqat Ali WaseemShoaib KhalidSaima ShaikhSyed Jamil Hasan KazmiMDPI AGarticleI-SpaDEspatial–temporal analysisdisease mappingDengue Feverpublic health planningGeographic Information SystemsMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 12018, p 12018 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	I-SpaDE spatial–temporal analysis disease mapping Dengue Fever public health planning Geographic Information Systems Medicine R
spellingShingle	I-SpaDE spatial–temporal analysis disease mapping Dengue Fever public health planning Geographic Information Systems Medicine R Syed Ali Asad Naqvi Muhammad Sajjad Liaqat Ali Waseem Shoaib Khalid Saima Shaikh Syed Jamil Hasan Kazmi Integrating Spatial Modelling and Space–Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan
description	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 <i>integrated spatial disease evaluation</i> (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the <i>Kernel Density Estimation</i>, the <i>Optimized Hot Spot Analysis</i>, space–time assessment and prediction, and the <i>Geographically Weighted Regression</i> (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 <i>city’s central functional area</i>. 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.
format	article
author	Syed Ali Asad Naqvi Muhammad Sajjad Liaqat Ali Waseem Shoaib Khalid Saima Shaikh Syed Jamil Hasan Kazmi
author_facet	Syed Ali Asad Naqvi Muhammad Sajjad Liaqat Ali Waseem Shoaib Khalid Saima Shaikh Syed Jamil Hasan Kazmi
author_sort	Syed Ali Asad Naqvi
title	Integrating Spatial Modelling and Space–Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan
title_short	Integrating Spatial Modelling and Space–Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan
title_full	Integrating Spatial Modelling and Space–Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan
title_fullStr	Integrating Spatial Modelling and Space–Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan
title_full_unstemmed	Integrating Spatial Modelling and Space–Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan
title_sort	integrating spatial modelling and space–time pattern mining analytics for vector disease-related health perspectives: a case of dengue fever in pakistan
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/91af0e6274d342b3b0f52b523231b716
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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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