Modeling and Sensitivity Analysis of Coronavirus Disease (COVID-19) Outbreak Prediction

Sedaghat, Ahmad; Oloomi, Seyed Amir Abbas; Malayer, Mahdi Ashtian; Band, Shahab S.; Mosavi, Amir; Nádai, László

doi:10.1109/cando-epe51100.2020.9337772

Cited by 2 publications

(2 citation statements)

References 8 publications

(4 reference statements)

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“…which clearly does not affect the dynamics of other states. The detailed worldwide [54] and local serological [55] and dynamical [7,20,56,57] analysis results provide estimates for the average lengths of the phases of the illness and the probabilities of transitions between the compartments. These parameters are aligned with the Hungary-specific data and were presented in detail in [21].…”

Section: Transitions Between the Phases Of The Diseasementioning

confidence: 99%

Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control

2022

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In this paper, we propose a model-based method for the reconstruction of not directly measured epidemiological data. To solve this task, we developed a generic optimization-based approach to compute unknown time-dependent quantities (such as states, inputs, and parameters) of discrete-time stochastic nonlinear models using a sequence of output measurements. The problem was reformulated as a stochastic nonlinear model predictive control computation, where the unknown inputs and parameters were searched as functions of the uncertain states, such that the model output followed the observations. The unknown data were approximated by Gaussian distributions. The predictive control problem was solved over a relatively long time window in three steps. First, we approximated the expected trajectories of the unknown quantities through a nonlinear deterministic problem. In the next step, we fixed the expected trajectories and computed the corresponding variances using closed-form expressions. Finally, the obtained mean and variance values were used as an initial guess to solve the stochastic problem. To reduce the estimated uncertainty of the computed states, a closed-loop input policy was considered during the optimization, where the state-dependent gain values were determined heuristically. The applicability of the approach is illustrated through the estimation of the epidemiological data of the COVID-19 pandemic in Hungary. To describe the epidemic spread, we used a slightly modified version of a previously published and validated compartmental model, in which the vaccination process was taken into account. The mean and the variance of the unknown data (e.g., the number of susceptible, infected, or recovered people) were estimated using only the daily number of hospitalized patients. The problem was reformulated as a finite-horizon predictive control problem, where the unknown time-dependent parameter, the daily transmission rate of the disease, was computed such that the expected value of the computed number of hospitalized patients fit the truly observed data as much as possible.

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Section: Transitions Between the Phases Of The Diseasementioning

confidence: 99%

Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control

2022

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“…Table 1 includes the fundamental models of the pandemic [1,2,12]. Further advanced SIRDbased models and data-driven had been investigated in [13][14][15][16][17][18][19][20][21]. The above results show that social distancing, quarantine, and isolation of infected populations support in controlling of an epidemic.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear control of Covid-19 pandemic based on the SIRD model

Ghadami

Seyyedi

Rituraj

2022

2022 IEEE 20th Jubilee International Symposium on Intelligent Systems and Informatics (SISY)

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In the last month of 2019, a new version of Corona disease was observed in Wuhan (China) which is known as Covid-19. Several models have been proposed to predict disease treatment. The SIR model is considered one of the simplest models for the prediction of pandemic disease. This means susceptible (S), infected (I), and recovered (R) populations. The SIRD model is yet another method that includes one more equation, i.e., the number of deaths (D). This paper proposed a control law for the first time to prevent the progression of the disease. The proposed control law is based on the SIRD model that is determined using two methods, i.e., the input-state feedback linearization method and the input-output feedback linearization method for the nonlinear modeling of Covid-19. The goal of control in this model is to reduce the percentage or number of infected people and the number of deaths due to Covid-19 disease. Simulation results show that the feedback linearization methods can have positive results in a significant reduction in unfurl of Covid-19. Delay in quarantine of infected people and constant percentage of people who should be quarantined are investigated as two important parameters. Results show that the percentage of infected people decreases 96.3 % and the percentage of deaths decreases 93.6 % when delay in quarantine equals 7 weeks.

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Modeling and Sensitivity Analysis of Coronavirus Disease (COVID-19) Outbreak Prediction

Cited by 2 publications

References 8 publications

Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control

Reconstruction of Epidemiological Data in Hungary Using Stochastic Model Predictive Control

Nonlinear control of Covid-19 pandemic based on the SIRD model

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