PANDORA: Deep graph learning based COVID-19 infection risk level forecasting

• URL: http://arxiv.org/abs/2406.06618v1
• Date: Fri, 7 Jun 2024 07:27:22 GMT
• Title: PANDORA: Deep graph learning based COVID-19 infection risk level forecasting
• Authors: Shuo Yu, Feng Xia, Yueru Wang, Shihao Li, Falih Febrinanto, Madhu Chetty,
• Abstract summary: A proper forecasting system is arguably important to provide an early signal of the risk of COVID-19 infection. We propose a deep graph learning model, called PANDORA, to predict the infection risks of COVID-19.
• Score: 6.984702599001295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: COVID-19 as a global pandemic causes a massive disruption to social stability that threatens human life and the economy. Policymakers and all elements of society must deliver measurable actions based on the pandemic's severity to minimize the detrimental impact of COVID-19. A proper forecasting system is arguably important to provide an early signal of the risk of COVID-19 infection so that the authorities are ready to protect the people from the worst. However, making a good forecasting model for infection risks in different cities or regions is not an easy task, because it has a lot of influential factors that are difficult to be identified manually. To address the current limitations, we propose a deep graph learning model, called PANDORA, to predict the infection risks of COVID-19, by considering all essential factors and integrating them into a geographical network. The framework uses geographical position relations and transportation frequency as higher-order structural properties formulated by higher-order network structures (i.e., network motifs). Moreover, four significant node attributes (i.e., multiple features of a particular area, including climate, medical condition, economy, and human mobility) are also considered. We propose three different aggregators to better aggregate node attributes and structural features, namely, Hadamard, Summation, and Connection. Experimental results over real data show that PANDORA outperforms the baseline method with higher accuracy and faster convergence speed, no matter which aggregator is chosen. We believe that PANDORA using deep graph learning provides a promising approach to get superior performance in infection risk level forecasting and help humans battle the COVID-19 crisis.

Related papers

• A Data-Driven Predictive Analysis on Cyber Security Threats with Key Risk Factors [1.715270928578365]
  This paper exhibits a Machine Learning(ML) based model for predicting individuals who may be victims of cyber attacks by analyzing socioeconomic factors. We propose a novel Pertinent Features Random Forest (RF) model, which achieved maximum accuracy with 20 features (95.95%) We generated 10 important association rules and presented the framework that is rigorously evaluated on real-world datasets.
  arXiv  Detail & Related papers  (2024-03-28T09:41:24Z)
• Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning [33.77030569632993]
  We focus on developing a framework of privacy-preserving individual-level infection prediction based on federated learning (FL) and graph neural networks (GNN) We propose Falcon, a Federated grAph Learning method for privacy-preserving individual-levelfetion predictiON. Our methodology outperforms state-of-the-art algorithms and is able to protect user privacy against actual privacy attacks.
  arXiv  Detail & Related papers  (2023-11-10T13:22:14Z)
• Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax [40.18153418511126]
  Non-Pharmaceutical Interventions (NPIs) have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of people. In this work, we focus on mobility modeling and aim to predict locations that will be visited by COVID-19 cases. We propose a model named Deep Graph Diffusion Infomax (DGDI), which jointly models variables including a geometric graph, a set of diffusions and a set of locations.
  arXiv  Detail & Related papers  (2022-12-12T05:15:47Z)
• Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data [66.70036251870988]
  The Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus 2019 (CO-19) incidence (hotspots) This paper presents a sparse model for early detection of COVID-19 hotspots (at the county level) in the United States. Deep neural networks are introduced to enhance the model's representative power while still enjoying the interpretability of the kernel.
  arXiv  Detail & Related papers  (2021-05-31T19:28:17Z)
• STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Management [28.205715426050105]
  It is absolutely necessary to develop an analytics framework to deliver insights in improving administrative policy and enhance the preparedness to combat the pandemic. This paper proposes a STOP-temporal knowledge mining framework, named STOP to model the impact of human mobility and contextual information over large geographic area in different temporal scales. The framework has two modules: (i) S-temporal data and computing infrastructure using fog/edge based architecture; and (ii) S-temporal data analytics module to efficiently extract knowledge from heterogeneous data sources.
  arXiv  Detail & Related papers  (2021-04-04T12:29:31Z)
• 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)
• Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread [61.9008166652035]
  We study the spread of COVID-19 using a semi-supervised neural network. We assume a passive part of the population remains isolated from the virus dynamics.
  arXiv  Detail & Related papers  (2020-10-10T19:33:53Z)
• Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19 [75.99038202534628]
  We propose CALI-Net, a neural transfer learning architecture which allows us to'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist. Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
  arXiv  Detail & Related papers  (2020-09-23T22:35:43Z)
• Epidemic mitigation by statistical inference from contact tracing data [61.04165571425021]
  We develop Bayesian inference methods to estimate the risk that an individual is infected. We propose to use probabilistic risk estimation in order to optimize testing and quarantining strategies for the control of an epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact.
  arXiv  Detail & Related papers  (2020-09-20T12:24:45Z)
• COVI White Paper [67.04578448931741]
  Contact tracing is an essential tool to change the course of the Covid-19 pandemic. We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
  arXiv  Detail & Related papers  (2020-05-18T07:40:49Z)
• Neural network based country wise risk prediction of COVID-19 [12.500738729507676]
  Recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Here, we propose a shallow long short-term memory (LSTM) based neural network to predict the risk category of a country. Results show that the proposed pipeline outperforms state-of-the-art methods for data of 180 countries.
  arXiv  Detail & Related papers  (2020-03-31T20:03:10Z)

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.