Data Analytics and Mathematical Modeling for Simulating the Dynamics of COVID-19 Epidemic-A Case Study of India

The global explosion of the COVID-19 pandemic has created worldwide unprecedented health and economic challenges which stimulated one of the biggest annual migrations globally. In the Indian context, even after proactive decisions taken by the Government, the continual growth of COVID-19 raises questions regarding its extent and severity. The present work utilizes the susceptible-infected-recovered-death (SIRD) compartment model for parameter estimation and fruitful prediction of COVID-19. Further, various optimization techniques such as particle swarm optimization (PSO), gradient (G), pattern search (PS) and their hybrid are employed to solve the considered model. The simulation study endorse the efficiency of PSO (with or without G) and G+PS+G over other techniques for ongoing pandemic assessment. The key parametric values including characteristic time of infection and death and reproduction number have been estimated as 60 days, 67 days and 4.78 respectively by utilizing the optimum results. The model assessed that India has passed its peak duration of COVID-19 with more than 81% recovery and only a 1.59% death rate. The short duration analysis (15 days) of obtained results against reported data validates the effectiveness of the developed models for ongoing pandemic assessment.

Automated summary

The study utilized the SIRD model to predict the development trend of COVID-19 in India, with optimization techniques like PSO, G, and PS for parameter estimation. Results indicated that PSO and G+PS+G methods were more effective, and India has passed the peak period of the pandemic with a high recovery rate of 81%.