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N‎onlinear stochastic model for epidemic disease prediction ‎by‎ optimal ‎filtering ‎perspective
  • Parisa Nabati,
  • Arezo Hajrajabi
Parisa Nabati
Urmia University of Technology

Corresponding Author:[email protected]

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Arezo Hajrajabi
Imam Khomeini International University
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Understanding and predicting novel diseases has become very important owing to the huge global health burden. ‎Organiz‎ing and studying mathematical models ‎performs‎ an essential role in predicting the behavior of the ‎disease. ‎In this paper, a new stochastic Susceptible-Infected-Recovered-Death (SIRD) model for spreading epidemic disease is investigated. First, the deterministic SIRD model is considered, and then, by allowing randomness in the recovery and death rates that are not deterministic, the system of nonlinear stochastic differential equations is derived. For the suggested model, the existence and uniqueness of a positive global solution are demonstrated. The parameter estimation is done with the conditional least square estimator for deterministic models and the maximum likelihood estimator for stochastic ones. After that, we investigate a nonadditive state-space model for spreading epidemic disease by considering infected as the hidden process variable. The problem of the hidden process variable from noisy observations is filtered, predicted, and smoothed using a recursive Bayesian technique. For estimating the hidden number of infected variables, closed-form solutions are obtained. Finally, numerical simulations with both simulated and real data are performed to demonstrate the efficiency and accuracy of the current work.
21 Jun 2022Submitted to Mathematical Methods in the Applied Sciences
25 Jun 2022Submission Checks Completed
25 Jun 2022Assigned to Editor
10 Jul 2022Reviewer(s) Assigned
12 Oct 2022Review(s) Completed, Editorial Evaluation Pending
26 Oct 2022Editorial Decision: Revise Minor
26 Oct 20221st Revision Received
27 Oct 2022Submission Checks Completed
27 Oct 2022Assigned to Editor
27 Oct 2022Review(s) Completed, Editorial Evaluation Pending
30 Oct 2022Reviewer(s) Assigned