ArticleSocio-economic planning sciences2022
Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019.
Article in Socio-economic planning sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.Epidemiologic reviews · 2025Pooled it
- AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.Osong public health and research perspectives · 2024Article
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
- Article
- Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting.PloS one · 2023Article
- Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model.Chaos, solitons, and fractals · 2022Article
- COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts.Socio-economic planning sciences · 2022Article
- How heterogeneous is the dengue transmission profile in Brazil? A study in six Brazilian states.PLoS neglected tropical diseases · 2022Article
- Deep Spatiotemporal Model for COVID-19 Forecasting.Sensors (Basel, Switzerland) · 2022Article
- Editorial for the special issue on "Transforming public health through artificial intelligence, machine learning and internet of things".Socio-economic planning sciences · 2022Article
- Space-Distributed Traffic-Enhanced LSTM-Based Machine Learning Model for COVID-19 Incidence Forecasting.Computational intelligence and neuroscience · 2022Article
- Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic.IEEE internet of things journal · 2021Article
- Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China.Environmental science and pollution research international · 2021Article
- EVDHM-ARIMA-Based Time Series Forecasting Model and Its Application for COVID-19 Cases.IEEE transactions on instrumentation and measurement · 2021Article
- DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions.IEEE access : practical innovations, open solutions · 2020Article
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4 authors.
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Abstract
The coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths in Europe. To refer government and enterprise to arrange countermeasures. The paper proposes a novel deep neural network framework to forecast the COVID-19 outbreak. The COVID-19Net framework combined 1D convolutional neural network, 2D convolutional neural network, and bidirectional gated recurrent units. COVID-19Net can well integrate the characteristics of time, space, and influencing factors of the COVID-19 accumulative cases. Three European countries with severe outbreaks were studied-Germany, Italy, and Spain-to extract spatiotemporal features and predict the number of confirmed cases. The prediction results acquired from COVID-19Net are compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute error, mean absolute percentage error, and root mean square error, which is commonly used model assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which was considerably better than those of the other models. This indicated that the proposed framework could accurately predict the accumulated number of confirmed cases in the three countries and serve as an essential reference for devising public health strategies. And also indicated that COVID-19 has high spatiotemporal relations, it suggests us to keep a social distance and avoid unnecessary trips.
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