Forecasting COVID-19 Infection Rates with Artificial Intelligence Model

Jesse Yang Jingye ()
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Jesse Yang Jingye: National University of Singapore

International Real Estate Review, 2022, vol. 25, issue 4, 525-542

Abstract: This study applies an artificial intelligence (AI) based model to predict the infection rate of coronavirus disease 2019 (COVID-19). The results provide information for managing public and global health risks regarding pandemic controls, disease diagnosis, vaccine development, and socio-economic responses. The machine learning algorithm is developed with the Python program to analyze pathways and evolutions of infection. The finding is robust in predicting the virus spread situation. The machine learning algorithms predict the rate of spread of COVID -19 with an accuracy of nearly 90%. The algorithms simulate the virus spread distance and coverage. We find that self-isolation for suspected cases plays an important role in containing the pandemic. The COVID-19 virus could spread asymptotically (silent spreader); therefore, earlier doctor consultation and testing of the virus could reduce its spread in local communities.

Keywords: COVID-19; Artificial Intelligence; Forecasting (search for similar items in EconPapers)
Date: 2022
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