Abstract:
In late 2019, Wuhan reported the initial cases of COVID-19, and within a short period, it
was declared a global pandemic. The pandemic had a profound impact globally, particularly
affecting developing countries. This research employs spatio-temporal techniques to analyze
the impact of land use factors on COVID-19 spread in Islamabad and to predict hotspots
based on these factors. The land use factors considered include hospitals, pharmacies, local
bus stops, metro stations, and supermarkets. Spatial autocorrelation and Hotspot Analysis
were used to identify disease clusters, followed by Pearson’s correlation analysis to determine
the influence of selected factors. Subsequently, machine learning techniques were applied to
predict hotspots. Local bus stops emerged as the most significant factor contributing to the
virus’s spread. Among the five classification models, K-Nearest Neighbour (KNN) demonstrated the best performance for hotspot prediction, with an accuracy of 92.9%, precision
of 86.5%, recall of 98.3%, F1-score of 92.0%, and an AUC value of 0.989. This research
provides valuable insights for policymakers, aiding in the identification of problem areas and
optimizing resource allocation to address similar viral outbreaks in the future.
