Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information

• URL: http://arxiv.org/abs/2111.05199v1
• Date: Tue, 9 Nov 2021 15:18:03 GMT
• Title: Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information
• Authors: Padmaksha Roy, Shailik Sarkar, Subhodip Biswas, Fanglan Chen, Zhiqian Chen, Naren Ramakrishnan, Chang-Tien Lu
• Abstract summary: We develop a deep learning-based timeseries model for probabilistic forecasting called Auto-regressive Mixed Density Diffusion Dynamic Network(ARM3Dnet) We show that our model can outperform both traditional statistical and deep learning models in forecasting the number of Covid-19 deaths and cases at the county level in the United States.
• Score: 22.60685417365995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling the spatiotemporal nature of the spread of infectious diseases can provide useful intuition in understanding the time-varying aspect of the disease spread and the underlying complex spatial dependency observed in people's mobility patterns. Besides, the county level multiple related time series information can be leveraged to make a forecast on an individual time series. Adding to this challenge is the fact that real-time data often deviates from the unimodal Gaussian distribution assumption and may show some complex mixed patterns. Motivated by this, we develop a deep learning-based time-series model for probabilistic forecasting called Auto-regressive Mixed Density Dynamic Diffusion Network(ARM3Dnet), which considers both people's mobility and disease spread as a diffusion process on a dynamic directed graph. The Gaussian Mixture Model layer is implemented to consider the multimodal nature of the real-time data while learning from multiple related time series. We show that our model, when trained with the best combination of dynamic covariate features and mixture components, can outperform both traditional statistical and deep learning models in forecasting the number of Covid-19 deaths and cases at the county level in the United States.

Related papers

• Stochastic Diffusion: A Diffusion Probabilistic Model for Stochastic Time Series Forecasting [8.232475807691255]
  We propose a novel Diffusion (StochDiff) model which learns data-driven prior knowledge at each time step. The learnt prior knowledge helps the model to capture complex temporal dynamics and the inherent uncertainty of the data.
  arXiv  Detail & Related papers  (2024-06-05T00:13:38Z)
• A Survey on Diffusion Models for Time Series and Spatio-Temporal Data [92.1255811066468]
  We review the use of diffusion models in time series and S-temporal data, categorizing them by model, task type, data modality, and practical application domain. We categorize diffusion models into unconditioned and conditioned types discuss time series and S-temporal data separately. Our survey covers their application extensively in various fields including healthcare, recommendation, climate, energy, audio, and transportation.
  arXiv  Detail & Related papers  (2024-04-29T17:19:40Z)
• A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation [35.46631415365955]
  We introduce a conditional diffusion framework called C$2$TSD, which incorporates disentangled temporal (trend and seasonality) representations as conditional information. Our experiments on three real-world datasets demonstrate the superior performance of our approach compared to a number of state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-18T11:59:04Z)
• Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation [58.20016784231991]
  Diffusion models operate over a sequence of timesteps instead of instantaneous input-output relationships in previous contexts. We present Diffusion-TracIn that incorporates this temporal dynamics and observe that samples' loss gradient norms are highly dependent on timestep. We introduce Diffusion-ReTrac as a re-normalized adaptation that enables the retrieval of training samples more targeted to the test sample of interest.
  arXiv  Detail & Related papers  (2024-01-17T07:58:18Z)
• An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks [6.5700527395783315]
  We propose a novel probability-enhanced neural network model, called NEC+, which concurrently learns extreme and normal prediction functions. We evaluate the proposed model on the difficult 3-day ahead hourly water level prediction task applied to 9 reservoirs in California.
  arXiv  Detail & Related papers  (2022-11-29T03:01:59Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• Model-Attentive Ensemble Learning for Sequence Modeling [86.4785354333566]
  We present Model-Attentive Ensemble learning for Sequence modeling (MAES) MAES is a mixture of time-series experts which leverages an attention-based gating mechanism to specialize the experts on different sequence dynamics and adaptively weight their predictions. We demonstrate that MAES significantly out-performs popular sequence models on datasets subject to temporal shift.
  arXiv  Detail & Related papers  (2021-02-23T05:23:35Z)
• STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization [76.57716281104938]
  We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
  arXiv  Detail & Related papers  (2020-12-08T21:21:47Z)
• Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting [10.052302234274256]
  We design and analysis of deep learning-based models for COVID-19 forecasting. We consider multiple sources such as COVID-19 confirmed and death case count data and testing data for better predictions. We propose clustering-based training for high-temporal forecasting.
  arXiv  Detail & Related papers  (2020-10-27T17:52:02Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.