Analysing Recovery From Pandemics by Learning Theory: The Case of CoVid-19
Romney B. Duffey, Enrico Zio
Abstract
We present a method for predicting the recovery time from infectious diseases outbreaks such as the recent CoVid-19 virus. The approach is based on the theory of learning from errors, specifically adapted to the control of the virus spread by reducing infection rates using countermeasures such as medical treatment, isolation, social distancing etc. When these are effective, the infection rate, after reaching a peak, declines following what we call the Universal Recovery Curve. We use presently available data from many countries to make actual predictions of the recovery trend and time needed for securing minimum infection rates in the future. We claim that the trend of decline is direct evidence of learning about risk reduction, also in this case of the pandemic.