About: ALeRT-COVID: Attentive Lockdown-awaRe Transfer Learning for Predicting COVID-19 Pandemics in Different Countries

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About: ALeRT-COVID: Attentive Lockdown-awaRe Transfer Learning for Predicting COVID-19 Pandemics in Different Countries Goto Sponge NotDistinct Permalink

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	ALeRT-COVID: Attentive Lockdown-awaRe Transfer Learning for Predicting COVID-19 Pandemics in Different Countries
Creator	Li, Xiang Wang, Fei Liu, Qin Guo, Jianying Co-Corresponding Author, † Jia, Wenxiao Li, Yingxue Wang, Junmei Xie, Guotong
source	MedRxiv
abstract	Countries across the world are in different stages of COVID-19 trajectory, among which many have implemented the lockdown measures to prevent its spread. Although the lockdown is effective in such prevention, it may put the economy into a depression. Predicting the epidemic progression with government switching the lockdown on or off is critical. We propose a transfer learning approach called ALeRT-COVID using attention-based recurrent neural network (RNN) architecture to predict the epidemic trends for different countries. A source model was trained on the pre-defined source countries and then transferred to each target country. The lockdown measure was introduced to our model as a predictor and the attention mechanism was utilized to learn the different contributions of the confirmed cases in the past days to the future trend. Results demonstrated that the transfer learning strategy is helpful especially for early-stage countries. By introducing the lockdown predictor and the attention mechanism, ALeRT-COVID showed a significant improvement on the prediction performance. We predicted the confirmed cases in one week when extending and easing lockdown separately. Results showed the lockdown measures is still necessary for a number of countries. We expect our research can help different countries to make better decisions on the lockdown measures.
has issue date	2020-07-10(xsd:dateTime)
bibo:doi	10.1101/2020.07.09.20149831
has license	medrxiv
sha1sum (hex)	736458adc980c7dcfc359eb6ca95d638446eb3a9
schema:url	https://doi.org/10.1101/2020.07.09.20149831
resource representing a document's title	ALeRT-COVID: Attentive Lockdown-awaRe Transfer Learning for Predicting COVID-19 Pandemics in Different Countries
resource representing a document's body	covid:736458adc980c7dcfc359eb6ca95d638446eb3a9#body_text
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