COVID-19 forecasting with deep learning: a distressing survey
preprintposted on 01.11.2021, 14:18 by Luis GutierrezLuis Gutierrez, Rodrigo de MedranoRodrigo de Medrano, Jose L. AznarteJose L. Aznarte
This document pretends to provide an overview about the lights and shadows on the latest trends in this specific area.
Unlike previously released literature reviews, that are providing a wide overview about any type of AI techniques applied to overall aspects of the pandemics, this document will focus specifically on the use of DL techniques applied to COVID-19 time series forecasting. The production in this field within the last months has become quite large.
After setting a group of quality criteria, related to problem definition, dataset manipulation, model identification and evaluation, 96 papers has been screened.
Most of the analysed papers did not meet the common quality standards of scientific work: none of them positively scored in all of the criteria, while only about one third scored positively in at least half of the defined criteria. The emergency character of this scientific production led to getting away from some of the basic requirements for quality scientific work.
Email Address of Submitting Authorlgutierre396@alumno.uned.es
Submitting Author's InstitutionUNED
Submitting Author's CountrySpain
artifical neural networksDeep LearningConvolutional neural networksRecurrent Neural Networks (RNNs)Extreme Learning Machine (ELM)LSTM (long short term memory networks)Ensemble methodDeep NeuralNetworks (DNNs)COVID-19 forecastingtime series forecastingAssessment FrameworkChecklistCOVID-19 Machine LearningPrediction Methods