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

Syed Ali Asad Naqvi, Muhammad Sajjad, Liaqat Ali Waseem, Shoaib Khalid, Saima Shaikh and Syed Jamil Hasan Kazmi
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Syed Ali Asad Naqvi: Department of Geography, Government College University Faisalabad, Faisalabad 38000, Pakistan
Muhammad Sajjad: Department of Geography, Hong Kong Baptist University, Hong Kong
Liaqat Ali Waseem: Department of Geography, Government College University Faisalabad, Faisalabad 38000, Pakistan
Shoaib Khalid: Department of Geography, Government College University Faisalabad, Faisalabad 38000, Pakistan
Saima Shaikh: Department of Geography, University of Karachi, Karachi 75270, Pakistan
Syed Jamil Hasan Kazmi: Department of Geography, University of Karachi, Karachi 75270, Pakistan

IJERPH, 2021, vol. 18, issue 22, 1-30

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.

Keywords: I-SpaDE; spatial–temporal analysis; disease mapping; Dengue Fever; public health planning; Geographic Information Systems (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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