An improved epidemiological-unscented Kalman filter (hybrid SEIHCRDV-UKF) model for the prediction of COVID-19. Application on real-time data
© 2022 Elsevier Ltd. All rights reserved..
The prevalence of COVID-19 has been the most serious health challenge of the 21th century to date, concerning national health systems on a daily basis, since December 2019 when it appeared in Wuhan City. Nevertheless, most of the proposed mathematical methodologies aiming to describe the dynamics of an epidemic, rely on deterministic models that are not able to reflect the true nature of its spread. In this paper, we propose a SEIHCRDV model - an extension/improvement of the classic SIR compartmental model - which also takes into consideration the populations of exposed, hospitalized, admitted in intensive care units (ICU), deceased and vaccinated cases, in combination with an unscented Kalman filter (UKF), providing a dynamic estimation of the time dependent system's parameters. The stochastic approach is considered necessary, as both observations and system equations are characterized by uncertainties. Apparently, this new consideration is useful for examining various pandemics more effectively. The reliability of the model is examined on the daily recordings of COVID-19 in France, over a long period of 265 days. Two major waves of infection are observed, starting in January 2021, which signified the start of vaccinations in Europe providing quite encouraging predictive performance, based on the produced NRMSE values. Special emphasis is placed on proving the non-negativity of SEIHCRDV model, achieving a representative basic reproductive number R 0 and demonstrating the existence and stability of disease equilibria according to the formula produced to estimate R 0 . The model outperforms in predictive ability not only deterministic approaches but also state-of-the-art stochastic models that employ Kalman filters. Furthermore, the relevant analysis supports the importance of vaccination, as even a small increase in the dialy vaccination rate could lead to a notable reduction in mortality and hospitalizations.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:166 |
|---|---|
| Enthalten in: |
Chaos, solitons, and fractals - 166(2023) vom: 01. Jan., Seite 112914 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Papageorgiou, Vasileios E [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
37A50 |
|---|
| Anmerkungen: |
Date Revised 09.12.2022 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1016/j.chaos.2022.112914 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM349529876 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM349529876 | ||
| 003 | DE-627 | ||
| 005 | 20231226042841.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.chaos.2022.112914 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1165.xml |
| 035 | |a (DE-627)NLM349529876 | ||
| 035 | |a (NLM)36440087 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Papageorgiou, Vasileios E |e verfasserin |4 aut | |
| 245 | 1 | 3 | |a An improved epidemiological-unscented Kalman filter (hybrid SEIHCRDV-UKF) model for the prediction of COVID-19. Application on real-time data |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 09.12.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2022 Elsevier Ltd. All rights reserved. | ||
| 520 | |a The prevalence of COVID-19 has been the most serious health challenge of the 21th century to date, concerning national health systems on a daily basis, since December 2019 when it appeared in Wuhan City. Nevertheless, most of the proposed mathematical methodologies aiming to describe the dynamics of an epidemic, rely on deterministic models that are not able to reflect the true nature of its spread. In this paper, we propose a SEIHCRDV model - an extension/improvement of the classic SIR compartmental model - which also takes into consideration the populations of exposed, hospitalized, admitted in intensive care units (ICU), deceased and vaccinated cases, in combination with an unscented Kalman filter (UKF), providing a dynamic estimation of the time dependent system's parameters. The stochastic approach is considered necessary, as both observations and system equations are characterized by uncertainties. Apparently, this new consideration is useful for examining various pandemics more effectively. The reliability of the model is examined on the daily recordings of COVID-19 in France, over a long period of 265 days. Two major waves of infection are observed, starting in January 2021, which signified the start of vaccinations in Europe providing quite encouraging predictive performance, based on the produced NRMSE values. Special emphasis is placed on proving the non-negativity of SEIHCRDV model, achieving a representative basic reproductive number R 0 and demonstrating the existence and stability of disease equilibria according to the formula produced to estimate R 0 . The model outperforms in predictive ability not only deterministic approaches but also state-of-the-art stochastic models that employ Kalman filters. Furthermore, the relevant analysis supports the importance of vaccination, as even a small increase in the dialy vaccination rate could lead to a notable reduction in mortality and hospitalizations | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a 37A50 | |
| 650 | 4 | |a 60G35 | |
| 650 | 4 | |a 62M20 | |
| 650 | 4 | |a 62P10 | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Dynamic parameter estimation | |
| 650 | 4 | |a Epidemiology | |
| 650 | 4 | |a Infectious diseases | |
| 650 | 4 | |a State-space models | |
| 650 | 4 | |a Unscented Kalman filter | |
| 700 | 1 | |a Tsaklidis, George |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Chaos, solitons, and fractals |d 1995 |g 166(2023) vom: 01. Jan., Seite 112914 |w (DE-627)NLM114377014 |x 0960-0779 |7 nnns |
| 773 | 1 | 8 | |g volume:166 |g year:2023 |g day:01 |g month:01 |g pages:112914 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.chaos.2022.112914 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 166 |j 2023 |b 01 |c 01 |h 112914 | ||