@inproceedings{3188408,
    title = "Using the time varying Kalman filter for prediction of Covid-19 cases in
Latvia and Greece",
    author = "Assimakis, N. and Ktena, A. and Manasis, C. and Mele, E. and Kunicina, and N. and Zabasta, A. and Juhna, T.",
    year = "2020",
    publisher = "IEEE Comput. Soc",
    booktitle = "2020 IEEE 61ST ANNUAL INTERNATIONAL SCIENTIFIC CONFERENCE ON POWER AND
ELECTRICAL ENGINEERING OF RIGA TECHNICAL UNIVERSITY (RTUCON)",
    doi = "10.1109/RTUCON51174.2020.9316598",
    keywords = "prediction; forecasting; Kalman filters; Covid-19; Internet of Things",
    abstract = "In this work we study applicability of Kalman filters as decision
support for early warning and emergency response system for infectious
diseases as CoVID-19. Here we use only the actual observations of new
cases/deaths from epidemiological survey. We investigated the behavior
of various time varying measurement driven models. We implement time
varying Kalman filters. Preliminary results from Greece and Latvia
showed that Kalman Filters can be used for short term forecasting of
CoVID-19 cases. The mean percent absolute error may vary by model; some
models give satisfactory results where the mean percent absolute error
in new cases is of the order of 2%-5%. The mean absolute error in new
deaths is of the order of 1-2 deaths. We propose the use of Kalman
Filters for short term forecasting, i.e. next day, which can be a useful
tool for improved crisis management at the points of entry to a country
or hospitals."
}