EKF-SIRD model algorithm for predicting the coronavirus (COVID-19) spreading dynamics.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
04 08 2022
04 08 2022
Historique:
received:
26
07
2021
accepted:
11
07
2022
entrez:
4
8
2022
pubmed:
5
8
2022
medline:
9
8
2022
Statut:
epublish
Résumé
In this paper, we study the Covid 19 disease profile in the Algerian territory since February 25, 2020 to February 13, 2021. The idea is to develop a decision support system allowing public health decision and policy-makers to have future statistics (the daily prediction of parameters) of the pandemic; and also encourage citizens for conducting health protocols. Many studies applied traditional epidemic models or machine learning models to forecast the evolution of coronavirus epidemic, but the use of such models alone to make the prediction will be less precise. For this purpose, we assume that the spread of the coronavirus is a moving target described by an epidemic model. On the basis of a SIRD model (Susceptible-Infection-Recovery- Death), we applied the EKF algorithm to predict daily all parameters. These predicted parameters will be much beneficial to hospital managers for updating the available means of hospitalization (beds, oxygen concentrator, etc.) in order to reduce the mortality rate and the infected. Simulations carried out reveal that the EKF seems to be more efficient according to the obtained results.
Identifiants
pubmed: 35927443
doi: 10.1038/s41598-022-16496-6
pii: 10.1038/s41598-022-16496-6
pmc: PMC9352705
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
13415Informations de copyright
© 2022. The Author(s).
Références
Infect Dis Model. 2020 Jul 09;5:495-501
pubmed: 32766461
Sci Rep. 2020 Jul 1;10(1):10711
pubmed: 32612204
AIMS Public Health. 2020 Nov 2;7(4):828-843
pubmed: 33294485
AIMS Public Health. 2020 May 22;7(2):306-318
pubmed: 32617358
Biology (Basel). 2020 May 08;9(5):
pubmed: 32397286
Nonlinear Dyn. 2020;102(4):2951-2957
pubmed: 33162673
Clin Epidemiol Glob Health. 2021 Jan-Mar;9:26-33
pubmed: 32838058
Chaos Solitons Fractals. 2020 Oct;139:110049
pubmed: 32834603
Bull Natl Res Cent. 2020;44(1):180
pubmed: 33100825
Infect Dis Model. 2020;5:748-754
pubmed: 32984666
Sci Rep. 2020 Dec 9;10(1):21522
pubmed: 33298986
Chaos Solitons Fractals. 2020 Sep;138:109946
pubmed: 32836915
Sci Rep. 2020 Oct 14;10(1):17306
pubmed: 33057119
Nat Commun. 2021 Jan 18;12(1):418
pubmed: 33462211
Chaos. 2020 Jul;30(7):071101
pubmed: 32752627
Nat Methods. 2020 Jun;17(6):557-558
pubmed: 32499633
Sci Total Environ. 2020 Aug 10;729:138959
pubmed: 32375067
PLoS One. 2020 Mar 31;15(3):e0230405
pubmed: 32231374
Nat Methods. 2020 May;17(5):455-456
pubmed: 32313223
Chaos Solitons Fractals. 2021 Mar;144:110652
pubmed: 33519122
Sci Rep. 2020 Nov 10;10(1):19457
pubmed: 33173127
Indian J Phys Proc Indian Assoc Cultiv Sci (2004). 2021;95(12):2575-2587
pubmed: 33250600
Chaos Solitons Fractals. 2020 Jul;136:109889
pubmed: 32406395
Adv Differ Equ. 2020;2020(1):489
pubmed: 32952537
Indian J Phys Proc Indian Assoc Cultiv Sci (2004). 2021;95(9):1941-1957
pubmed: 32837088