Spatiotemporal patterns of Covid-19 pandemic in India: Inferences of pandemic dynamics from data analysis
Preet Mishra, R. K. Brojen Singh

TL;DR
This paper analyzes Covid-19 data in India using growth functions and harmonic analysis, revealing insights into disease spread, effects of interventions, and regional wave patterns to inform mitigation strategies.
Contribution
It introduces a combined growth and harmonic analysis approach to understand Covid-19 dynamics in India, highlighting regional variations and the impact of control measures.
Findings
Growth function parameters are sensitive to infection growth and lockdown effects.
Harmonic analysis shows synchronous national incident features due to control strategies.
Regional analysis reveals traveling wave patterns in disease propagation.
Abstract
Modeling and analysis of the large scale Covid-19 pandemic data can yield inferences about it's dynamics and characteristics of disease propagation. These inferences can then be correlated with contextual factors like population density, effects of strategic interventions, heterogeneous disease propagation etc, and such set of validated inferences can serve as precedents for designing of subsequent mitigation strategies. In this work, we present the analysis of Covid-19 pandemic data in Indian context using growth functions fitting procedure and harmonic analysis method. Our results of growth function fitting to the data indicate that the growth function parameters are quite sensitive to the growth of the infected population indicating positive impact of lockdown strategy, identification of inflection point and nearly synchronous statistical features of disease spreading. The harmonic…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Mathematical and Theoretical Epidemiology and Ecology Models
