Integrating Spatial Modelling and Space-Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives:...

Area of Science:

• Public Health
• Spatial Epidemiology
• Environmental Science

Background:

• Spatial-temporal assessment of vector-borne diseases is crucial for effective public health planning and preventive strategies.
• Understanding disease distribution and influencing factors aids in targeted interventions.

Purpose of the Study:

• To propose and demonstrate an integrated spatial disease evaluation (I-SpaDE) framework for analyzing vector-borne diseases.
• To systematically assess disease concentrations, patterns, clustering, prediction dynamics, and relationships with socio-ecological factors.

Main Methods:

• The I-SpaDE framework integrates Kernel Density Estimation, Optimized Hot Spot Analysis, space-time assessment/prediction, and Geographically Weighted Regression (GWR).
• Applied to Dengue Fever (DF) in Lahore, Pakistan (2007-2016).

Main Results:

• Significant DF clustering observed in specific years (2007-2008, 2010-2011, 2013, 2016), primarily in the city's central functional area.
• Prediction analysis indicated DF spread from less to more urbanized areas.
• GWR results identified temperature as the most significant factor associated with DF, followed by vegetation and built-up areas.

Conclusions:

• The I-SpaDE framework provides a robust and flexible approach for spatial-temporal disease assessment.
• Findings offer valuable insights for public health planning and disease control, particularly for Dengue Fever in urban settings.
• The framework is adaptable for use in similar tropical and subtropical regions, especially in developing countries.