Using the Time Varying Kalman Filter for Prediction of Covid-19 Cases in Latvia and Greece

2020 IEEE 61st International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON 2020): Conference Proceedings 2020
Nikolass Assimakis, Aphrodite Ktena, Christoss Manasis, Dimitris Enea Mele, Nadežda Kuņicina, Anatolijs Zabašta, Tālis Juhna

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.

Atslēgas vārdi
prediction; forecasting; Kalman filters, Covid-19, Internet of Things
DOI
10.1109/RTUCON51174.2020.9316598
Hipersaite
https://ieeexplore.ieee.org/document/9316598

Assimakis, N., Ktena, A., Manasis, C., Enea Mele, D., Kuņicina, N., Zabašta, A., Juhna, T. Using the Time Varying Kalman Filter for Prediction of Covid-19 Cases in Latvia and Greece. No: 2020 IEEE 61st International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON 2020): Conference Proceedings, Latvija, Riga, 5.-7. novembris, 2020. Piscataway: IEEE, 2020, Article number 9316598. ISBN 978-1-7281-9511-7. e-ISBN 978-1-7281-9510-0. Pieejams: doi:10.1109/RTUCON51174.2020.9316598

Publikācijas valoda
English (en)