Predicting the rate of CoVid-19 infected cases by Learning Theory

We predict the evolution of the daily number of infected cases from CoVid-19 virus. We use the theory of learning from errors, adapted to the problem of virus containment by protective measures such as testing, isolation and social distancing. The theory is consistent with the findings of cognitive psychology on how humans address the solution of errors. Application of these measures leads to the infection rate declining, after reaching a peak. We use publicly available data to predict the recovery rate curve and the time still needed to reach minimum infection rates.