EKF-SIRD model algorithm for predicting the coronavirus (COVID-19) spreading dynamics

Sebbagh, Abdennour; Kechida, Sihem

doi:10.1038/s41598-022-16496-6

Cited by 15 publications

(11 citation statements)

References 39 publications

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“…In the present study, a novel EI-PF is introduced for the modeling of epidemic outbreaks. The presented approach incorporates time-varying transmissibility and mortality rates, which frequently characterize the prevalence of epidemics [19,20,25,50]. Furthermore, two extra parameters were integrated that improve the particle weight distribution and consequently the resampling procedure.…”

Section: Discussionmentioning

confidence: 99%

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A novel epidemiologically informed particle filter for assessing epidemic phenomena. Application to the monkeypox outbreak of 2022

Papageorgiou,

Kolias

2024

Inverse Problems

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Contagious diseases are constantly affecting more and more people every day, resulting in widespread health crises especially in developing nations. Previous studies have developed deterministic and stochastic mathematical models to investigate the spread of epidemics. In the present study, a hybrid particle filtering epidemiological model is proposed, which combines the elements of a deterministic susceptible-exposed-infectious-recovered-deceased (SEIRD) model with the inclusion of stochastic and penalty factors, in order to efficiently evaluate the dynamics of the disease. The inclusion of penalty factors stands out as the main novelty of the proposed methodology, guaranteeing estimations that align with the unique aspects of the examined natural phenomenon. The model is applied to the monkeypox (mpox) data of the United States from June 25th to November 21th, 2022. Our approach is compared to four alternatives, corresponding to deterministic and stochastic approaches that are associated with either fixed or time-varying parameters. In all cases, the particle filtering models displayed better characteristics in terms of infectious cases and deaths compared to their deterministic counterpart. The final version of the proposed epidemiologically informed particle filtering (EI-PF) model exhibited significant potential and provided the best fitting/predictive performance compared to other examined methodologies. The predictive effectiveness of the proposed methodology has been thoroughly evaluated across various time intervals. Moreover, the inclusion of additional penalty factors in the weight computation procedure, assists in reducing fitting and prediction errors while simultaneously providing increased likelihood estimates. This modelling approach can be readily applied to other epidemics, both existing and emerging, where uncertainties in system dynamics and real-time observations hinder the accurate capture of the epidemic's progression. 

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Section: Discussionmentioning

confidence: 99%

“…Sebbagh and Kechida [19], employed a SEIRD-extended Kalman filter (EKF), including the parameters of the model in the updating process of the EKF. Ndanguza et al [20] included in the EKF-SEIR model the estimation of the epidemic parameters, while Costa et al [21] combined also an EKF with a SEIR scheme to simulate an epidemic outbreak.…”

Section: Introductionmentioning

confidence: 99%

A novel epidemiologically informed particle filter for assessing epidemic phenomena. Application to the monkeypox outbreak of 2022

Papageorgiou,

Kolias

2024

Inverse Problems

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“…Our model provides the best fitting results, displaying the least NRMSE values compared to the four remaining models, while the deterministic approach shows by far the worst estimations. We also compare our model with other models that employ the usage of the EKF, as this is the most popular stochastic approach in the literature for modeling epidemic outbreaks [25][26][27]. The SEIRD model is one of the most frequently used models to describe an epidemic, containing the most common observable states.…”

Section: Examination Of the Model's Efficiencymentioning

confidence: 99%

A stochastic particle extended SEIRS model with repeated vaccination: Application to real data of COVID‐19 in Italy

Papageorgiou,

Tsaklidis

2024

Math Methods in App Sciences

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The prediction of the evolution of epidemics plays an important role in limiting the transmissibility and the burdensome consequences of infectious diseases, which leads to the employment of mathematical modeling. In this paper, we propose a stochastic particle filtering extended SEIRS model with repeated vaccination and time‐dependent parameters, aiming to efficiently describe the demanding dynamics of time‐varying epidemics. The validity of our model is examined using daily records of COVID‐19 in Italy for a period of 525 days, revealing a notable capacity to uncover the hidden dynamics of the pandemic. The main findings include the estimation of asymptomatic cases, which is a well‐known feature of the current pandemic. Unlike other proposed models that employ extra compartments for asymptomatic cases, which force the estimation of this proportion and significantly increase the model's complexity, our approach leads to the evaluation of the hidden dynamics of COVID‐19 without additional computational burden. Other findings that confirm the model's appropriateness and robustness are its parameter evolution and the estimation of more ICU‐admitted cases compared to the official records during the most prevalent infection wave of January 2022, attributed to the intensified increase in admissions that may have led to full occupancy in ICUs. As the vast majority of datasets contain time series of total recovered and vaccinated cases, we propose a statistical algorithm to estimate the currently recovered and protected through vaccination cases. This necessity arises from the attenuation of antibodies after vaccination/infection and is necessary for long‐time interval predictions. Finally, we not only present a novel stochastic epidemiological model and test its efficiency but also investigate its mathematical properties, such as the existence and stability of epidemic equilibria, giving new insights to the literature. The latter provides additional details concerning the system's long‐term behavior, while the conclusions drawn from the index provide perspectives on the severity and future of the COVID‐19 pandemic.

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“…Several mathematicians have investigated further infectious epidemic models based on the SIR model, which is the simplest epidemic model and consists of three compartments 18 , 19 . The SIRD model 20 , 21 , SEIR model 22 – 24 , SEIS 25 , and MSEIR 26 models are modifications of SIR that represent diverse epidemiological situations for diseases 27 – 29 . This paper proposes a new stochastic SIRD epidemic model that incorporates a mean-reverting Ornstein Uhlenbech process (MROU process).…”

Section: Introductionmentioning

confidence: 99%

Introducing a novel mean-reverting Ornstein–Uhlenbeck process based stochastic epidemic model

Nabati

2024

Sci Rep

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The major objective of this paper is to examine a novel mean-reverting Ornstein–Uhlenbeck process-based stochastic SIRD model for transmission the epidemic disease that is a great crisis in numerous societies. For this purpose, the deterministic model is further converted into the stochastic form by allowing the infection rate satisfies the mean-reverting Ornstein–Uhlenbeck process to account the uncertainties involved in epidemic spread. At first using Lyapunov functions, the solution’s uniqueness and positivity will be demonstrated. Subsequently, the stochastic epidemic threshold $$\Re_{0}^{S}$$ ℜ 0 S that controls the disease’s extinction and persistence in the mean is identified analytically. It has been established that when $$\Re_{0}^{S} < 1$$ ℜ 0 S < 1 the disease will extinguish, whereas if $$\Re_{0}^{S} > 1$$ ℜ 0 S > 1 the disease is persistent. At last, several numerical simulations are presented to demonstrate the findings of the hypothetical investigation results. These simulations served to vividly illustrate and validate the implications derived from the hypothetical analysis.

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EKF-SIRD model algorithm for predicting the coronavirus (COVID-19) spreading dynamics

Cited by 15 publications

References 39 publications

A novel epidemiologically informed particle filter for assessing epidemic phenomena. Application to the monkeypox outbreak of 2022

A novel epidemiologically informed particle filter for assessing epidemic phenomena. Application to the monkeypox outbreak of 2022

A stochastic particle extended SEIRS model with repeated vaccination: Application to real data of COVID‐19 in Italy

Introducing a novel mean-reverting Ornstein–Uhlenbeck process based stochastic epidemic model

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