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Introduction: Mapping of infectious diseases like COVID-19 is the foremost importance for diseases control and prevention. This study attempts to identify the spatio-temporal pattern and evolution trend of COVID-19 at the district level in India using spatial statistical models., Materials and Methods: Active cases of eleven time-stamps (30 March-2 December, 2020) with an approximately 20-day interval are considered. The study reveals applications of spatial statistical tools, i.e. optimised hotspot and outlier analysis (which follow Gi* and Moran I statistics) and emerging hotspot with the base of space time cube, are effective for the spatio-temporal evolution of disease clusters., Results: The result shows the overall increasing trend of COVID-19 infection with a Mann-Kendall trend score of 2.95 ( p   =   0.0031). The spatial clusters of high infection (hotspots) and low infection (coldspots) change their location over time but are limited to the districts of the south-western states (Kerala, Karnataka, Andhra Pradesh, Maharashtra, Gujarat) and the north-eastern states (West Bengal, Jharkhand, Assam, Tripura, Manipur, etc.) respectively., Conclusions: A total of eight types of patterns are identified, but the most concerning types are consecutive (7.24% of districts), intensifying (15.13% districts) and persistent (24.34% of districts) which will help health policy makers and the government to prioritize-based resource allocation and control measures.

Byline: Nirmalya. Das, Abhishek. De, Biswanath. Naskar, Amrita. Sil, Sudip. Das, Aarti. Sarda, Gobinda. Chatterjee Background: Leprosy is a chronic infectious disease caused by Mycobacterium leprae affecting the skin, peripheral [...]