A COVID-19 forecasting system using adaptive neuro-fuzzy inference.

Kim Tien Ly

Author Information

Kim Tien Ly: School of Computer Science, University of Nottingham, Nottingham, United Kingdom.

PMID: 34131413 DOI: 10.1016/j.frl.2020.101844

This article proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) to forecast the number of COVID-19 cases in the United Kingdom. With the combination of artificial neural network and fuzzy logic structure, the model is trained based on collected data. The study examines various factors of ANFIS to come up with an effective time series prediction model. The result indicates that Spain and Italy data can strengthen the predictive power of COVID-19 cases in the UK. It is suggested that the policymakers should adopt Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict contagion effect during the COVID-19 pandemic.

ANFIS Contagion effect Coronavirus Forecasting system Time series

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Journal Article

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An innovative ensemble model based on multiple neural networks and a novel heuristic optimization algorithm for COVID-19 forecasting.Status evaluation of provinces affected by COVID-19: A qualitative assessment using fuzzy system.Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview.A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning.

OpenLB
Open Library of Bioscience