Analysis and prediction of <scp>COVID</scp>‐19 trajectory: A machine learning approach

Ritanjali Majhi, Rahul Thangeda, Renu Prasad Sugasi, Niraj Kumar

2020Journal of Public Affairs40 citationsDOI Open Access PDF

Abstract

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.

Topics & Concepts

Random forestCoronavirus disease 2019 (COVID-19)Decision treeComputer science2019-20 coronavirus outbreakSample (material)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Tree (set theory)Key (lock)Machine learningArtificial intelligenceWork (physics)Predictive modellingTrajectoryChinaEconometricsStatisticsOutbreakMathematicsGeographyEngineeringComputer securityMedicineAstronomyMechanical engineeringPathologyMathematical analysisChemistryArchaeologyInfectious disease (medical specialty)DiseasePhysicsChromatographyVirologyCOVID-19 epidemiological studiesCOVID-19 diagnosis using AIAnomaly Detection Techniques and Applications