Machine Learning Based Prediction and Impact Analysis of Various Lockdown Stages of COVID-19 Outbreak – A Case Study of India
Keywords:COVID-19, machine learning, SVM, SIR model, lockdown predictions.
Various measures have been taken into account for the virus outbreak. But
how much it successes to control outbreak to fights against COVID-19.
Machine learning is used as a tool to study these complex impacts on various
stages of the epidemic. While India is forced to open up the economy after
an extended lockdown, the effect of lockdown, which is critical to decide the
future course of action, is yet to be understood. The study suggests Support
Vector Machine (SVM) and Polynomial Regression (PR) are better suited compared to Long Short-Term Memory (LSTM) in scenarios consisting of
sparse and discrete events. The time-series memory of LSTM is outperformed
by the contextual hyperplanes of SVM which classifies the data even more
precisely. The study suggests while phase 1 of lockdown was effective, the
rest of them were not. Had India continued with lockdown 1, it would have
flattened the COVID-19 infection curve by mid of May 2020. With the current
rate, India will hit the 8 million mark by 23 October 2020. The SVM model is
further integrated with an SIR (Susceptible, Infected and Recovered) model
of epidemiology, which suggests that 70% of India’s population is infected
by this pandemic during this 8 month and the peak reached in October 2020
if vaccine not found. With increasing recovery rate increases the possibility
of decreasing COVID-19 cases. According to the SVM model’s prediction,
90% of cases of COVID-19 will be end in February.
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