Steady state Kalman filter design for cases and deaths prediction of Covid-19 in Greece

In this work we study the applicability of the steady state Kalman filter in order to predict new cases and deaths of Covid-19. We use the actual observations of new cases and deaths. First, we deal with short term prediction, namely daily prediction. We propose the use of the golden steady state Kalman Filter, which is designed to have parameters related to the golden section. It was found that the proposed golden steady state Kalman Filter has a satisfactory behavior compared with the classical mean or average filter. Secondly, we deal with long term prediction, for example average prediction per quarantine period (14 days). We propose to process blocks of measurements of time window corresponding for example to the quarantine period in order to predict the average of cases and deaths using steady state Kalman Filter. It was found that the proposed golden steady state Kalman Filter produces more reliable predictions than the classical mean or average filter does. The use of steady state Kalman Filter for cases and deaths prediction of Covid-19 can be effective for resources and prevention measures planning.