MPSTAN: Metapopulation-based Spatio-Temporal Attention Network for Epidemic Forecasting

• URL: http://arxiv.org/abs/2306.12436v1
• Date: Thu, 15 Jun 2023 18:12:55 GMT
• Title: MPSTAN: Metapopulation-based Spatio-Temporal Attention Network for Epidemic Forecasting
• Authors: Junkai Mao, Yuexing Han and Bing Wang
• Abstract summary: We propose a hybrid model called Metapopulation-based Spatio-Temporal Attention Network (MPSTAN) This model aims to improve the accuracy of epidemic forecasting by incorporating multi-patch epidemiological knowledge into a-temporal model and adaptively defining inter-patch interactions.
• Score: 2.0297284948237366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate epidemic forecasting plays a vital role for governments in developing effective prevention measures for suppressing epidemics. Most of the present spatio-temporal models cannot provide a general framework for stable, and accurate forecasting of epidemics with diverse evolution trends. Incorporating epidemiological domain knowledge ranging from single-patch to multi-patch into neural networks is expected to improve forecasting accuracy. However, relying solely on single-patch knowledge neglects inter-patch interactions, while constructing multi-patch knowledge is challenging without population mobility data. To address the aforementioned problems, we propose a novel hybrid model called Metapopulation-based Spatio-Temporal Attention Network (MPSTAN). This model aims to improve the accuracy of epidemic forecasting by incorporating multi-patch epidemiological knowledge into a spatio-temporal model and adaptively defining inter-patch interactions. Moreover, we incorporate inter-patch epidemiological knowledge into both the model construction and loss function to help the model learn epidemic transmission dynamics. Extensive experiments conducted on two representative datasets with different epidemiological evolution trends demonstrate that our proposed model outperforms the baselines and provides more accurate and stable short- and long-term forecasting. We confirm the effectiveness of domain knowledge in the learning model and investigate the impact of different ways of integrating domain knowledge on forecasting. We observe that using domain knowledge in both model construction and loss functions leads to more efficient forecasting, and selecting appropriate domain knowledge can improve accuracy further.

Related papers

• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• A Short Survey of Human Mobility Prediction in Epidemic Modeling from Transformers to LLMs [0.0]
  Understanding how people move during epidemics is essential for modeling the spread of diseases. Forecasting population movement is crucial for informing models and facilitating effective response planning in public health emergencies. We review a range of approaches utilizing both pretrained language models like BERT and Large Language Models (LLMs) tailored specifically for mobility prediction tasks.
  arXiv  Detail & Related papers  (2024-04-25T17:52:19Z)
• Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility [14.587916407752719]
  We propose a novel hybrid model called MepoGNN for multi-step multi-region epidemic forecasting. Our model can not only predict the number of confirmed cases but also explicitly learn the epidemiological parameters.
  arXiv  Detail & Related papers  (2023-06-26T17:09:43Z)
• Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
  This survey delves into various data-driven methodological and practical advancements. We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
  arXiv  Detail & Related papers  (2022-07-19T16:15:11Z)
• Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics [2.705025060422369]
  Infectious diseases remain among the top contributors to human illness and death worldwide. Forecasts of epidemics can assist stakeholders in tailoring countermeasures to the situation at hand.
  arXiv  Detail & Related papers  (2022-06-21T19:31:25Z)
• When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting [70.54920804222031]
  Most existing forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions. Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations. We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP.
  arXiv  Detail & Related papers  (2021-06-07T18:31:47Z)
• 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)
• An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
  We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We forecast the epidemic evolution in Italy and France.
  arXiv  Detail & Related papers  (2020-10-28T10:58:59Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany [0.19791587637442667]
  We present a novel combination of epidemiological modeling with specialized neural networks. We are able to obtain reliable probabilistic estimates for important disease characteristics, such as generation time, fraction of undetected infections, likelihood of transmission before symptom onset, and reporting delays using a very moderate amount of real-world observations.
  arXiv  Detail & Related papers  (2020-10-01T11:01:49Z)
• Simulation of Covid-19 epidemic evolution: are compartmental models really predictive? [0.0]
  This paper addresses the question whether a SIR epidemiological model, enriched with asymptomatic and dead individual compartments, could provide reliable predictions on the epidemic evolution. A machine learning approach based on particle swarm optimization (PSO) is proposed to automatically identify the model parameters based on a training set of data of progressive increasing size. The analysis of the scatter in the forecasts shows that model predictions are quite sensitive to the size of the dataset used for training, and that further data are still required to achieve convergent -- and therefore reliable -- predictions.
  arXiv  Detail & Related papers  (2020-04-14T08:42:11Z)

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.