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

Li, Yingxue; Jia, Wenxiao; Wang, Junmei; Jian-ying, Guo; Qin, Liu; Li, Xiang; Wang, Fei

doi:10.1007/s41666-020-00088-y

Cited by 14 publications

(15 citation statements)

References 27 publications

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“…In the existing scholarly literature, there are few studies specifically utilizing RNNbased models aimed at predicting COVID-19 waves. For instance, Li and colleagues [24] have devised a RNN-based alert system, termed as Attentive Lockdown-awaRe Transfer Learning for Predicting COVID-19 Pandemics in Different Countries (ALeRT-COVID). This system was devised and trained on a pre-defined country ("source country") and, then, adapted ("transferred") to other target countries.…”

Section: Discussionmentioning

confidence: 99%

Development of an Early Alert System for an Additional Wave of COVID-19 Cases using a Recurrent Neural Network with Long Short-Term Memory

et al. 2021

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Section: Discussionmentioning

confidence: 99%

Development of an Early Alert System for an Additional Wave of COVID-19 Cases using a Recurrent Neural Network with Long Short-Term Memory

et al. 2021

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“…In the existing scholarly literature, there are few studies specifically utilizing RNN-based models aimed at predicting COVID-19 waves. For instance, Li and colleagues [ 24 ] have devised a RNN-based alert system, termed as Attentive Lockdown-awaRe Transfer Learning for Predicting COVID-19 Pandemics in Different Countries (ALeRT-COVID). This system was devised and trained on a pre-defined country (“source country”) and, then, adapted (“transferred”) to other target countries.…”

Section: Discussionmentioning

confidence: 99%

Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory

Stevenson

Hayasi

Bragazzi

et al. 2021

IJERPH

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The impact of the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic has been and is still vast, affecting not only global human health and stretching healthcare facilities, but also profoundly disrupting societal and economic systems worldwide. The nature of the way the virus spreads causes cases to come in further recurring waves. This is due a complex array of biological, societal and environmental factors, including the novel nature of the emerging pathogen. Other parameters explaining the epidemic trend consisting of recurring waves are logistic–organizational challenges in the implementation of the vaccine roll-out, scarcity of doses and human resources, seasonality, meteorological drivers, and community heterogeneity, as well as cycles of strengthening and easing/lifting of the mitigation interventions. Therefore, it is crucial to be able to have an early alert system to identify when another wave of cases is about to occur. The availability of a variety of newly developed indicators allows for the exploration of multi-feature prediction models for case data. Ten indicators were selected as features for our prediction model. The model chosen is a Recurrent Neural Network with Long Short-Term Memory. This paper documents the development of an early alert/detection system that functions by predicting future daily confirmed cases based on a series of features that include mobility and stringency indices, and epidemiological parameters. The model is trained on the intermittent period in between the first and the second wave, in all of the South African provinces.

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“…Jumlah kumulatif orang dengan vaksinasi COVID-19 dosis penuh karena vaksin mengurangi penyebaran virus; diperoleh dari repository Github OWID [35] 3. Periode Pembatasan Sosial Berskala Besar (PSBB) sebagai local lockdown karena efektif mengurangi penyebaran COVID-19 [18], [25], [36], [37]; direkap secara manual dari situs berita 4. Tanggal penting lain yang mempengaruhi mobilitas, misalnya libur panjang atau hari raya idul fitri, karena mobilitas mempengaruhi tingkat penyebaran COVID-19 [38], [39], direkap secara manual dari kalender akademik [40] dan situs berita.…”

Section: A Pengumpulan Dataunclassified

“…Evaluasi dilakukan untuk mengukur kinerja model untuk data baru [21], mengetahui kebergunaan tiap bagian model [25], dan membandingkan model dengan model lain. Untuk mengukur kinerja model untuk data baru, model dilatih menggunakan training set hingga nilai validasi mulai naik untuk menghindari overfitting atau setidaknya 100 epoch, lalu dievaluasi menggunakan test set yang belum pernah dilihat oleh model [52].…”

Section: F Evaluasiunclassified

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Peramalan Jumlah Kasus COVID-19 Menggunakan Joint Learning

Nur

Amin

Yusuf

2022

JuTISI

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COVID-19 is a dangerous illness because it spreads quickly and easily. Vaccines are already available but the pandemic isn’t likely to end soon. Forecasting is hoped to help handle the pandemic. Deep learning, specially LSTM, has been used to forecast COVID-19 case count in some regions. However, deep learning models generally need a lot of training data while COVID-19 daily data are scarce. However, COVID-19 pandemic happens in many regions. This research aims to use joint learning with data from other regions to improve model performance with fewer data and to use the model to forecast until 9 months since the date of last data taken. Joint learning was done by making models share some parts and training the models together. To overcome the different data scale and pandemic age in the regions, the data was first transformed into discrete SIRD variables and was evaluated using RMSSE. Joint learning failed to improve the model performance in this research. The proposed model performance was signficantly better than ARIMA-SIRD and SIRD model but wasn’t better than normal encoder-decoder LSTM. The models only reached RMSSE below one occasionally. Additionally, it was found that doing joint learning with all regions without selecting them by clustering can make the model performance worse instead. It was also found that RMSSE is too sensitive to a mostly stagnant time-series due to its division by the error of one-step naïve forecast.

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

Cited by 14 publications

References 27 publications

Development of an Early Alert System for an Additional Wave of COVID-19 Cases using a Recurrent Neural Network with Long Short-Term Memory

Development of an Early Alert System for an Additional Wave of COVID-19 Cases using a Recurrent Neural Network with Long Short-Term Memory

Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory

Peramalan Jumlah Kasus COVID-19 Menggunakan Joint Learning

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