A novel deep interval type-2 fuzzy LSTM (DIT2FLSTM) model applied to COVID-19 pandemic time-series prediction

Abstract

Currently, the novel COVID-19 coronavirus has been widely spread as a global pandemic. The COVID-19 pandemic has a major influence on human life, healthcare systems, and the economy. There are a large number of methods available for predicting the incidence of the virus. A complex and non-stationary problem such as the COVID-19 pandemic is characterized by high levels of uncertainty in its behavior during the pandemic time. The fuzzy logic, especially Type-2 Fuzzy Logic, is a robust and capable model to cope with high-order uncertainties associated with non-stationary time-dependent features. The main objective of the current study is to present a novel Deep Interval Type-2 Fuzzy LSTM (DIT2FLSTM) model for prediction of the COVID-19 incidence, including new cases, recovery cases, and mortality rate in both short and long time series. The proposed model was evaluated on real datasets produced by the world health organization (WHO) on top highly risked countries, including the USA, Brazil, Russia, India, Peru, Spain, Italy, Iran, Germany, and the U.K. The results confirm the superiority of the DIT2FLSTM model with an average area under the ROC curve (AUC) of 96% and a 95% confidence interval of [92-97] % in the short-term and long-term. The DIT2FLSTM was applied to a well-known standard benchmark, the Mackey-Glass time-series, to show the robustness and proficiency of the proposed model in uncertain and chaotic time series problems. The results were evaluated using a 10-fold cross-validation technique and statistically validated through the t-test method. The proposed DIT2FLSTM model is promising for the prediction of complex problems such as the COVID-19 pandemic and making strategic prevention decisions to save more lives.

A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning

“…Long short-term memory (LSTM), the most representative one of deep-learning, is a special kind of RNN, which is mainly designed to solve the gradient disappearance and gradient explosion problems during the training of long sequences. LSTM has been proved to be a powerful deep-learning network for the forecast of COVID-19 infections [ [40] , [41] , [42] , [43] ].…”

“… USA, Brazil, etc. [ 41 ] generalized linear and tree-based machine learning models 0.21 N.P. 0.99 N.P.…”

Novel informatics approaches to COVID-19 Research: From methods to applications

“…Methods for short-term forecasting are usually data-driven, but they can also incorporate domain knowledge in the form of compartmental modelling (i.e., SIR-type models – see [48] ), as a means of integrating and simultaneously forecasting in a coherent manner multiple epidemiological indicators, such as cases, hospitalizations, and deaths. The study by Swaraj et al [21] uses a statistical data-driven approach, Aljaaf et al [22] , Dairi et al [23] , and Safari et al [24] use a machine learning data-driven approach, while Jing et al [25] uses compartmental modelling. Most of the forecasting studies were conducted as stand-alone evaluations or comparisons of methods by researchers who were not directly affiliated with public health authorities mandated to perform surveillance.…”

“…Most of the forecasting studies were conducted as stand-alone evaluations or comparisons of methods by researchers who were not directly affiliated with public health authorities mandated to perform surveillance. In terms of epidemiological indicators being forecast, the studies focused on confirmed cases, hospitalizations and deaths, although the contribution of smartphone mapping data, was explored by Jing et al [25] Methodologically, most studies used statistical and ML methods, either in comparison (Dairi et al [23] and Safari et al [24] ), within an ensemble (Aljaaf et al [22] and Swaraj et al [21] ), or as a part of a pipeline. Although results varied, findings were consistent with a recent review of methods for aberration detection in public health, with ML methods tending to outperform statistical methods for short-term forecasting.…”

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