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Modeling and analysis of the large scale Covid-19 pandemic data can yieldinferences about it's dynamics and characteristics of disease propagation.These inferences can then be correlated with contextual factors like populationdensity, effects of strategic interventions, heterogeneous disease propagationetc, and such set of validated inferences can serve as precedents for designingof subsequent mitigation strategies. In this work, we present the analysis ofCovid-19 pandemic data in Indian context using growth functions fittingprocedure and harmonic analysis method. Our results of growth function fittingto the data indicate that the growth function parameters are quite sensitive tothe growth of the infected population indicating positive impact of lockdownstrategy, identification of inflection point and nearly synchronous statisticalfeatures of disease spreading. The harmonic analysis of the data shows thecountrywide synchronous incident features due to simultaneous implementation ofcontrol strategies. However, if one analyzes the data from each state of theIndia, one can see various forms of travelling waves in the countrywide wavepattern. Hence, one needs to do these analysis from time to time to understandthe effectiveness of any control strategy and to closely look at the diseasepropagation to devise the required type of mitigation strategies.

Spatiotemporal patterns of Covid-19 pandemic in India: Inferences of pandemic dynamics from data analysis