COVID-19 Lifecycle: Predictive Modelling of States in India
Ramesh Behl and
Global Business Review, 2020, vol. 21, issue 4, 883-891
The study captures the COVID-19 lifecycle in different states of India using predictive analytics. Drawing upon the seminal susceptibleâ€“infectedâ€“removed (SIR) model of capturing the spread of viral diseases, this study models the spread of COVID-19 in the ten most infected states of India (as on 30 April 2020). Using publicly available state-wise time series data of COVID-19 patients during the period 1â€“30 April 2020, the study uses the forecasting technique of auto-regressive integrated moving averages (ARIMA) to predict the likely population susceptible to COVID-19 in each state. Thereafter, based on the SIR model, predictive modelling of state-wise COVID-19 data is carried out to determine: (a) the predictive accuracy; (b) the likely number of days it would take for the disease to reach the peak number of infections in a state; (c) the likely number of infections at the peak; and (d) the state-wise end date. The SIR model is implemented by running Python 3.7.4 on Jupyter Notebook and using the package Matplotlib 3.2.1 for visualization. The study offers rich insights for policymakers as well as common citizens.
Keywords: COVID-19; India; SIR model; predictive analytics; ARIMA (search for similar items in EconPapers)
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