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Modelling dynamics of coronavirus disease 2019 spread for pandemic forecasting based on Simulink.
Liu, Xian-Xian; Hu, Shimin; Fong, Simon James; Crespo, Rubén González; Herrera-Viedma, Enrique.
  • Liu XX; Department of Computer and Information Science, University of Macau, Macau SAR, China.
  • Hu S; Department of Computer and Information Science, University of Macau, Macau SAR, China.
  • Fong SJ; Department of Computer and Information Science, University of Macau, Macau SAR, China.
  • Crespo RG; Computer Science and Technology Department, Universidad Internacional de La Rioja, La Rioja, Logroño, Spain.
  • Herrera-Viedma E; University of Granada, Spain.
Phys Biol ; 18(4)2021 05 28.
Article in English | MEDLINE | ID: covidwho-1192595
ABSTRACT
In this paper, we demonstrate the application of MATLAB to develop a pandemic prediction system based on Simulink. The susceptible-exposed-asymptomatic but infectious-symptomatic and infectious (severe infected population + mild infected population)-recovered-deceased (SEAI(I1+I2)RD) physical model for unsupervised learning and two types of supervised learning, namely, fuzzy proportional-integral-derivative (PID) and wavelet neural-network PID learning, are used to build a predictive-control system model that enables self-learning artificial intelligence (AI)-based control. After parameter setting, the data entering the model are predicted, and the value of the data set at a future moment is calculated. PID controllers are added to ensure that the system does not diverge at the beginning of iterative learning. To adapt to complex system conditions and afford excellent control, a wavelet neural-network PID control strategy is developed that can be adjusted and corrected in real time, according to the output error.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computer Simulation / COVID-19 / Models, Biological Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: North America / Asia Language: English Journal subject: Biology Year: 2021 Document Type: Article Affiliation country: 1478-3975

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computer Simulation / COVID-19 / Models, Biological Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: North America / Asia Language: English Journal subject: Biology Year: 2021 Document Type: Article Affiliation country: 1478-3975