Document Type : Original Articles


Research Scholar, Department of Geography, Cotton University, India



Background: The COVID-19 pandemic has shattered the global health system and caused havoc worldwide. Since there is no proper medication, non-pharmaceutical intervention methods are followed to mitigate viral transmission. For its proper implementation, it is crucial to track the spatial pattern of transmission and target those areas which require immediate action to control the spread of the pandemic. The geospatial technologies have established themselves as powerful tools that have substantial ability to track outbreak patterns in real-time, identify at-risk populations, and plan targeted intervention.
Methods: The study provides a robust methodological framework with three geospatial tools: Spatial Autocorrelation (Global Moran's I), Hot Spot Analysis (Getis-Ord Gi*), and Space-time scan statistic. The quantitative study was carried out exclusively for North-East India to track the COVID-19 outbreaks from April 2020 to December 2020.
Results: The results obtained indicate a gradual change of spatial distribution of the disease from cluster to random distribution at the global scale. But, eventually, the COVID-19 cases tend to form clusters again. Kamrup Metro, the district with the highest urban population, was reported constantly as a hotspot. Moreover, space-time clusters tend to expand in size over time.
Conclusion:  The research study's findings provide an overview of the spatio-temporal pattern of COVID-19 in the study area and help the health officials and policy-makers in formulating effective management strategies and non-pharmaceutical intervention measures by targeting the high-risk areas. The study is a valuable guide towards implementing Geographic Information Science technologies in monitoring and tracking the pandemic.


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