Application of machine learning for predicting the spread of COVID-19
This work addresses disease transmission prediction for public health, but it appears incremental as it applies existing methods to a new context without novel breakthroughs.
The paper tackled predicting COVID-19 spread and the effects of containment measures like quarantine and social distancing using machine learning, but no concrete results or numbers were provided in the abstract.
The spread of diseases has been studied for many years, but it receives a particular focus recently due to the outbreak and spread of COVID-19. Studies show that the spread of COVID-19 can be characterized by the Susceptible-Infectious-Recovered-Deceased (SIRD) model with containment coefficients (due to quarantine and keeping social distance). This project aims to apply the machine learning technique to predict the severity of COVID-19 and the effect of quarantine, keeping social distance, working from home, and wearing masks on the transmission of the disease. This work deepens our understanding of disease transmission and reveals the importance of following policies.