2021
DOI: 10.1016/j.chaos.2021.110922
|View full text |Cite
|
Sign up to set email alerts
|

Maximum likelihood-based extended Kalman filter for COVID-19 prediction

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
43
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
4
2
2

Relationship

1
7

Authors

Journals

citations
Cited by 45 publications
(43 citation statements)
references
References 28 publications
0
43
0
Order By: Relevance
“…For high values of v, the Student's t distribution resembles the Gaussian distribution. For this reason we prefer to use a normal error, which in turn has other advantages [26][27][28], and is given by…”
Section: Model Fitting and Assessmentmentioning
confidence: 99%
“…For high values of v, the Student's t distribution resembles the Gaussian distribution. For this reason we prefer to use a normal error, which in turn has other advantages [26][27][28], and is given by…”
Section: Model Fitting and Assessmentmentioning
confidence: 99%
“…T , z i are the measured variables and Q id,i ∈ R 4×4 is a weight matrix calculated to normalize measurements from early stages of the pandemic to July 1 st 2021 according to Equation (17). All integration therein state estimators are solved with cvodes via CasADI/MATLAB.…”
Section: /16mentioning
confidence: 99%
“…Zhu et al 16 estimated states and parameters into an augmented state with an Extended Kalman Filter (EKF). Song et al 17 estimated states with an EKF and parameters with a proposed strategy based on maximum likelihood. Nonetheless, state and parameter estimations in the literature are based on simple models and the authors did not evaluate virus mutations and vaccination dynamics with the estimator results.…”
Section: Introductionmentioning
confidence: 99%
“…The inadequate contact tracing, lack of population-wide PCR testing and short-term policy changes also cause the uncertainties in reported data on COVID-19 [ 32 ]. However, the existing studies on COVID-19 modelling are dominated by deterministic epidemiological models for describing the epidemiological evolution deterministically via ordinary differential equations, unable to model the stochastic behaviours of the COVID-19 epidemic [ 9 , 10 ]. Therefore, it is also necessary to develop a stochastic epidemiological model to account for random or stochastic events involved in the COVID-19 transmission system.…”
Section: Introductionmentioning
confidence: 99%
“…However, the predator-prey interaction mechanism described by the Lotka-Volterra model has a very limited capacity to model the complex characteristics of the natural transmission process of COVID-19. Song et al studied a novel maximum likelihood based EKF to estimate COVID-19 spread [ 9 ]. However, since this method is based on a deterministic epidemiological model, it is incapable of characterizing the stochastic characteristics of COVID-19 spread.…”
Section: Introductionmentioning
confidence: 99%