Deep Learning has been successfully applied to many application domains, yet
its advantages have been slow to emerge for time series forecasting. For
example, in the well-known Makridakis (M) Competitions, hybrids of traditional
statistical or machine learning techniques have only recently become the top
performers. With the recent architectural advances in deep learning being
applied to time series forecasting (e.g., encoder-decoders with attention,
transformers, and graph neural networks), deep learning has begun to show
significant advantages. Still, in the area of pandemic prediction, there remain
challenges for deep learning models: the time series is not long enough for
effective training, unawareness of accumulated scientific knowledge, and
interpretability of the model. To this end, the development of foundation
models (large deep learning models with extensive pre-training) allows models
to understand patterns and acquire knowledge that can be applied to new related
problems before extensive training data becomes available. Furthermore, there
is a vast amount of knowledge available that deep learning models can tap into,
including Knowledge Graphs and Large Language Models fine-tuned with scientific
domain knowledge. There is ongoing research examining how to utilize or inject
such knowledge into deep learning models. In this survey, several
state-of-the-art modeling techniques are reviewed, and suggestions for further
work are provided.

深度学习在时间序列预测方面取得了显著优势，然而在大流行预测领域仍存在挑战，如训练数据不足和模型的可解释性。此文献综述了几种先进的建模技术，并提供了进一步研究的建议。

深度学习和基础模型在时间序列预测中的调查

A Survey of Deep Learning and Foundation Models for Time Series  Forecasting

Recurring outbreaks of COVID-19 have posed enduring effects on global
society, which calls for a predictor of pandemic waves using various data with
early availability. Existing prediction models that forecast the first outbreak
wave using mobility data may not be applicable to the multiwave prediction,
because the evidence in the USA and Japan has shown that mobility patterns
across different waves exhibit varying relationships with fluctuations in
infection cases. Therefore, to predict the multiwave pandemic, we propose a
Social Awareness-Based Graph Neural Network (SAB-GNN) that considers the decay
of symptom-related web search frequency to capture the changes in public
awareness across multiple waves. Our model combines GNN and LSTM to model the
complex relationships among urban districts, inter-district mobility patterns,
web search history, and future COVID-19 infections. We train our model to
predict future pandemic outbreaks in the Tokyo area using its mobility and web
search data from April 2020 to May 2021 across four pandemic waves collected by
Yahoo Japan Corporation under strict privacy protection rules. Results
demonstrate our model outperforms state-of-the-art baselines such as ST-GNN,
MPNN, and GraphLSTM. Though our model is not computationally expensive (only 3
layers and 10 hidden neurons), the proposed model enables public agencies to
anticipate and prepare for future pandemic outbreaks.

本研究提出了一个基于社交意识图神经网络（SAB-GNN）的预测模型，通过考虑病征相关搜索频率的下降来捕捉公众意识在疫情多波次间的变化，以实现对疫情多次爆发进行预测，并利用日本东京地区的移动和网络搜索数据进行模型训练，结果表明该模型在预测未来疫情爆发方面优于 ST-GNN、MPNN 和 GraphLSTM 等现有模型。

利用网络搜索和移动数据的社会意识多波 COVID-19 预测

Multiwave COVID-19 Prediction from Social Awareness using Web Search and  Mobility Data

Objective: The COVID-19 pandemic has created many challenges that need
immediate attention. Various epidemiological and deep learning models have been
developed to predict the COVID-19 outbreak, but all have limitations that
affect the accuracy and robustness of the predictions. Our method aims at
addressing these limitations and making earlier and more accurate pandemic
outbreak predictions by (1) using patients' EHR data from different counties
and states that encode local disease status and medical resource utilization
condition; (2) considering demographic similarity and geographical proximity
between locations; and (3) integrating pandemic transmission dynamics into deep
learning models. Materials and Methods: We proposed a spatio-temporal attention
network (STAN) for pandemic prediction. It uses an attention-based graph
convolutional network to capture geographical and temporal trends and predict
the number of cases for a fixed number of days into the future. We also
designed a physical law-based loss term for enhancing long-term prediction.
STAN was tested using both massive real-world patient data and open source
COVID-19 statistics provided by Johns Hopkins university across all U.S.
counties. Results: STAN outperforms epidemiological modeling methods such as
SIR and SEIR and deep learning models on both long-term and short-term
predictions, achieving up to 87% lower mean squared error compared to the best
baseline prediction model. Conclusions: By using information from real-world
patient data and geographical data, STAN can better capture the disease status
and medical resource utilization information and thus provides more accurate
pandemic modeling. With pandemic transmission law based regularization, STAN
also achieves good long-term prediction performance.

本文通过使用真实世界患者数据和地理数据以及传染病传播定律设计的损失项来提高预测准确性，提出了一种基于定位注意力和空间 - 时间图卷积网络的流行病预测方法，并在 COVID-19 数据集上进行了测试。结果表明，这种方法在短期和长期预测中均优于现有方法，提高了预测的准确性。