Selected article for: "lockdown effect and machine learning"
Author: Majhi, Ritanjali; Thangeda, Rahul; Sugasi, Renu Prasad; Kumar, Niraj
Title: Analysis and prediction of COVID‐19 trajectory: A machine learning approach
  • Cord-id: j6nxlqtx
  • Document date: 2020_11_18
    • ID: j6nxlqtx
    Snippet: The outbreak of Coronavirus 2019 (COVID‐19) has impacted everyday lives globally. The number of positive cases is growing and India is now one of the most affected countries. This paper builds predictive models that can predict the number of positive cases with higher accuracy. Regression‐based, Decision tree‐based, and Random forest‐based models have been built on the data from China and are validated on India's sample. The model is found to be effective and will be able to predict the
    Document: The outbreak of Coronavirus 2019 (COVID‐19) has impacted everyday lives globally. The number of positive cases is growing and India is now one of the most affected countries. This paper builds predictive models that can predict the number of positive cases with higher accuracy. Regression‐based, Decision tree‐based, and Random forest‐based models have been built on the data from China and are validated on India's sample. The model is found to be effective and will be able to predict the positive number of cases in the future with minimal error. The developed machine learning model can work in real‐time and can effectively predict the number of positive cases. Key measures and suggestions have been put forward considering the effect of lockdown.

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