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Forecasting The Spread of COVID-19 with Enhanced Linear Autoregression

Ethan Mann; Quentin Chevalier; Geoffrey Magda; Matthew Gonzalgo

The COVID-19 pandemic is the focal point of global attention in 2020, as the virus has affected most of the world. Effectively responding to the pandemic continues to be a major and urgent issue; as of September 2020, there is still no widely accepted cure for the disease. To complement medical studies regarding the nature of the virus and provide decision-makers with tools to handle the crisis, Caltech offered the Spring 2020 CS156b course to forecast the probability distribution of the number of COVID-19 related deaths. The challenge was held during April and May 2020, and the predictive goal was to model fatalities as reported by the New York Times (NYT) two weeks into the future on the county level, with predictions evaluated via pinball loss. This paper details our team's model submission, a linear autoregressive model with features for cases and fatalities.

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