MIT machine learning model predicts COVID-19 spike with eased quarantines

MIT has developed a new machine learning model mean to predict the rate and pattern of spread of the COVID-19 novel coronavirus. The machine learning model was based on established epidemiological equations about outbreaks combined with publicly available data as well as neural network-based inference. When it was applied to the coronavirus spread data from late January to early March, its predictions proved accurate based on what actually happened leading up to April 1 in various regions worldwide. It also suggested that any relaxation or elimination of quarantine regulations in the immediate or near-term would result in an "exponential explosion" of infection rates. MIT researchers developed this calculation based only on the data collected about COVID-19's spread. Other prior calculations have incorporated data from other outbreaks such as SARS and MERS.