Using the time varying Kalman filter for prediction of Covid-19 cases in Latvia and Greece

N. Assimakis; A. Ktena; C. Manasis; E. Mele; N. Kunicina; A. Zabasta; T. Juhna

doi:10.1109/RTUCON51174.2020.9316598

Using the time varying Kalman filter for prediction of Covid-19 cases in Latvia and Greece

N. Assimakis
, A. Ktena
, C. Manasis
, E. Mele
, N. Kunicina
, A. Zabasta
, T. Juhna

National and Kapodistrian University of Athens
Riga Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Research › peer-review

4 Citations (Scopus)

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 Co Vid-19cases. 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.

Original language	English
Title of host publication	2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728195100
DOIs	https://doi.org/10.1109/RTUCON51174.2020.9316598
Publication status	Published - 5 Nov 2020
Externally published	Yes
Event	61st Annual IEEE International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Riga, Latvia Duration: 5 Nov 2020 → 7 Nov 2020

Publication series

Name	2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Proceedings

Conference

Conference	61st Annual IEEE International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020
Country/Territory	Latvia
City	Riga
Period	5/11/20 → 7/11/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Covid-19
Internet of Things
Kalman filters
forecasting
prediction

Access to Document

10.1109/RTUCON51174.2020.9316598

Cite this

Assimakis, N., Ktena, A., Manasis, C., Mele, E., Kunicina, N., Zabasta, A., & Juhna, T. (2020). Using the time varying Kalman filter for prediction of Covid-19 cases in Latvia and Greece. In 2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Proceedings Article 9316598 (2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RTUCON51174.2020.9316598

Assimakis, N. ; Ktena, A. ; Manasis, C. et al. / Using the time varying Kalman filter for prediction of Covid-19 cases in Latvia and Greece. 2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Proceedings).

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title = "Using the time varying Kalman filter for prediction of Covid-19 cases in Latvia and Greece",

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 Co Vid-19cases. 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.",

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author = "N. Assimakis and A. Ktena and C. Manasis and E. Mele and N. Kunicina and A. Zabasta and T. Juhna",

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year = "2020",

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doi = "10.1109/RTUCON51174.2020.9316598",

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Assimakis, N, Ktena, A, Manasis, C, Mele, E, Kunicina, N, Zabasta, A & Juhna, T 2020, Using the time varying Kalman filter for prediction of Covid-19 cases in Latvia and Greece. in 2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Proceedings., 9316598, 2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 61st Annual IEEE International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020, Riga, Latvia, 5/11/20. https://doi.org/10.1109/RTUCON51174.2020.9316598

Using the time varying Kalman filter for prediction of Covid-19 cases in Latvia and Greece. / Assimakis, N.; Ktena, A.; Manasis, C. et al.
2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9316598 (2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Research › peer-review

TY - GEN

T1 - Using the time varying Kalman filter for prediction of Covid-19 cases in Latvia and Greece

AU - Assimakis, N.

AU - Ktena, A.

AU - Manasis, C.

AU - Mele, E.

AU - Kunicina, N.

AU - Zabasta, A.

AU - Juhna, T.

PY - 2020/11/5

Y1 - 2020/11/5

N2 - 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 Co Vid-19cases. 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.

AB - 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 Co Vid-19cases. 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.

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KW - Internet of Things

KW - Kalman filters

KW - forecasting

KW - prediction

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M3 - Conference paper

AN - SCOPUS:85100053413

T3 - 2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Proceedings

BT - 2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 61st Annual IEEE International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020

Y2 - 5 November 2020 through 7 November 2020

ER -

Assimakis N, Ktena A, Manasis C, Mele E, Kunicina N, Zabasta A et al. Using the time varying Kalman filter for prediction of Covid-19 cases in Latvia and Greece. In 2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9316598. (2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2020 - Proceedings). doi: 10.1109/RTUCON51174.2020.9316598

Using the time varying Kalman filter for prediction of Covid-19 cases in Latvia and Greece

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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