Modelling dynamics of coronavirus disease 2019 spread for pandemic forecasting based on Simulink.
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.
Keywords
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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