MIT has developed a new model of the spread of COVID-19 infection, based on publicly available data, combined with established epidemiological equations about outbreaks, and neural network-based inference. The model, described in a new report, proves accurate when trained on data spanning late January to early March in terms of anticipating the actual spread leading up to April 1 in different regions around the world, and it indicates that any immediate or near-term relaxation or reversal of quarantine measures currently in place would lead to an “exponential explosion” in the number of infections.

Researchers at MIT sought to develop a model based just on COVID-19 data, whereas others have used SARS or MERS information to inform their charting of the outbreak’s progress. Combining available COVID-19

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