Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

• Published in: Healthcare (Basel) Vol. 8; no. 2; p. 181
• Main Authors: Melin, Patricia, Monica, Julio Cesar, Sanchez, Daniela, Castillo, Oscar
• Format: Journal Article
• Language: English
• Published: MDPI AG 19.06.2020; MDPI
• Subjects: COVID-19; COVID-19 time series; Disease transmission; Distribution; ensembles; Epidemics; Epidemiology; Forecasts and trends; Fuzzy logic; Machine learning; Medical research; Mexico; neural networks; Mexico
• ISSN: 2227-9032; 2227-9032
• DOI: 10.3390/healthcare8020181

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.