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

¹School of Computer Science, University of Nottingham, Nottingham, United Kingdom.

Finance Research Letters

|June 16, 2021

Summary

This study uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict COVID-19 cases in the UK. Incorporating data from Spain and Italy enhances prediction accuracy for policymakers.

Keywords:

ANFIS Contagion effect Coronavirus Forecasting system Time series

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Area of Science:

Computational intelligence
Epidemiology
Artificial intelligence in healthcare

Background:

Accurate forecasting of COVID-19 cases is crucial for public health policy.
Traditional time series models may struggle with the complex dynamics of pandemics.
Hybrid intelligent systems offer potential for improved prediction accuracy.

Purpose of the Study:

To propose and evaluate an Adaptive Neuro-Fuzzy Inference System (ANFIS) for forecasting COVID-19 cases in the United Kingdom.
To investigate the impact of incorporating international data on prediction performance.
To provide a tool for policymakers to predict contagion effects.

Main Methods:

Development of an ANFIS model combining artificial neural networks and fuzzy logic.
Training the ANFIS model using historical COVID-19 case data.
Evaluating the model's predictive power, including the effect of external data sources.

Main Results:

The ANFIS model demonstrated effectiveness in time series prediction of COVID-19 cases.
Inclusion of COVID-19 data from Spain and Italy significantly strengthened the predictive accuracy for the UK.
The model provides a robust method for forecasting pandemic trajectories.

Conclusions:

The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a viable and effective tool for COVID-19 case forecasting.
International data integration enhances the predictive capabilities of epidemiological models.
Policymakers are advised to utilize ANFIS for predicting and managing pandemic contagion effects.