Tracing the COVID‑19 spread pattern in India through aGIS‑based spatio‑temporal analysis of interconnected clusters
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Date
2024
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Abstract
Spatiotemporal analysis is a critical tool for understanding COVID-19 spread. This study examines
the pattern of spatial distribution of COVID-19 cases across India, based on data provided by the
Indian Council of Medical Research (ICMR). The research investigates temporal patterns during
the frst, second, and third waves in India for an informed policy response in case of any present
or future pandemics. Given the colossal size of the dataset encompassing the entire nation’s data
during the pandemic, a time-bound convenience sampling approach was employed. This approach
was carefully designed to ensure a representative sample from advancing timeframes to observe
time-based patterns in data. Data were captured from March 2020 to December 2022, with a
5-day interval considered for downloading the data. We employ robust spatial analysis techniques,
including the Moran’s I index for spatial correlation assessment and the Getis Ord Gi* statistic for
cluster identifcation. It was observed that positive COVID-19 cases in India showed a positive auto correlation from May 2020 till December 2022. Moran’s I index values ranged from 0.11 to 0.39. It
signifes a strong trend over the last 3 years with r2
of 0.74 on order 3 polynomial regression. It is
expected that high-risk zones can have a higher number of cases in future COVID-19 waves. Monthly
clusters of positive cases were mapped through ArcGIS software. Through cluster maps, high risk zones were identifed namely Kerala, Maharashtra, New Delhi, Tamil Nadu, and Gujarat. The
observation is: high-risk zones mostly fall near coastal areas and hotter climatic zones, contrary to
the cold Himalayan region with Montanne climate zone. Our aggregate analysis of 3 years of COVID 19 cases suggests signifcant patterns of interconnectedness between the Indian Railway network,
climatic zones, and geographical location with COVID-19 spread. This study thereby underscores the
vital role of spatiotemporal analysis in predicting and managing future COVID-19 waves as well as
future pandemics for an informed policy response.