Continuous Learning and Inference of Individual Probability of SARS-CoV-2 Infection Based on Interaction Data

• URL: http://arxiv.org/abs/2006.04646v3
• Date: Sun, 31 Jan 2021 07:43:10 GMT
• Title: Continuous Learning and Inference of Individual Probability of SARS-CoV-2 Infection Based on Interaction Data
• Authors: Shangching Liu (1), Koyun Liu (1), Hwaihai Chiang (1), Jianwei Zhang (2), Tsungyao Chang (1) ((1) Synergies Intelligent Systems, Inc., (2) University of Hamburg)
• Abstract summary: This study presents a new approach to determine the likelihood of asymptomatic carriers of the SARS-CoV-2 virus by using interaction-based continuous learning and inference of individual probability (CLIIP) for contagious ranking. Compared to traditional contact tracing methods, our approach significantly reduces the screening and quarantine required to search for the potential asymptomatic virus carriers by as much as 94%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study presents a new approach to determine the likelihood of asymptomatic carriers of the SARS-CoV-2 virus by using interaction-based continuous learning and inference of individual probability (CLIIP) for contagious ranking. This approach is developed based on an individual directed graph (IDG), using multi-layer bidirectional path tracking and inference searching. The IDG is determined by the appearance timeline and spatial data that can adapt over time. Additionally, the approach takes into consideration the incubation period and several features that can represent real-world circumstances, such as the number of asymptomatic carriers present. After each update of confirmed cases, the model collects the interaction features and infers the individual person's probability of getting infected using the status of the surrounding people. The CLIIP approach is validated using the individualized bidirectional SEIR model to simulate the contagion process. Compared to traditional contact tracing methods, our approach significantly reduces the screening and quarantine required to search for the potential asymptomatic virus carriers by as much as 94%.

Related papers

• Estimating Individual Dose-Response Curves under Unobserved Confounders from Observational Data [6.166869525631879]
  We present ContiVAE, a novel framework for estimating causal effects of continuous treatments, measured by individual dose-response curves. We show that ContiVAE outperforms existing methods by up to 62%, demonstrating its robustness and flexibility.
  arXiv  Detail & Related papers  (2024-10-21T07:24:26Z)
• 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)
• Adaptive Sequential Surveillance with Network and Temporal Dependence [1.7205106391379026]
  Strategic test allocation plays a major role in the control of both emerging and existing pandemics. Infectious disease surveillance presents unique statistical challenges. We propose an Online Super Learner for adaptive sequential surveillance.
  arXiv  Detail & Related papers  (2022-12-05T17:04:17Z)
• TsFeX: Contact Tracing Model using Time Series Feature Extraction and Gradient Boosting [0.0]
  This research presents an automated machine learning system for identifying individuals who may have come in contact with others infected with COVID-19. This paper describes the different approaches followed in arriving at an optimal solution model that effectually predicts whether a person has been in close proximity to an infected individual.
  arXiv  Detail & Related papers  (2021-11-29T11:12:38Z)
• Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories [55.786207368853084]
  We address the problem of modeling constrained hospital resources in the midst of the COVID-19 pandemic. For broad applicability, we focus on the common yet challenging scenario where patient-level data for a region of interest are not available. We propose an aggregate count explicit-duration hidden Markov model, nicknamed the ACED-HMM, with an interpretable, compact parameterization.
  arXiv  Detail & Related papers  (2021-04-28T15:32:42Z)
• COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing [68.68882022019272]
  COVI-AgentSim is an agent-based compartmental simulator based on virology, disease progression, social contact networks, and mobility patterns. We use COVI-AgentSim to perform cost-adjusted analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features.
  arXiv  Detail & Related papers  (2020-10-30T00:47:01Z)
• Predicting Infectiousness for Proactive Contact Tracing [75.62186539860787]
  Large-scale digital contact tracing is a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. This paper develops and test methods that can be deployed to a smartphone to proactively predict an individual's infectiousness.
  arXiv  Detail & Related papers  (2020-10-23T17:06:07Z)
• 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)
• Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [94.37521840642141]
  We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
  arXiv  Detail & Related papers  (2020-07-07T21:04:55Z)
• CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data [6.0785913977668935]
  We present a probabilistic graphical model for COVID-19 infection spread through a population based on the SEIR model. Our micro-level model keeps track of the infection state for each individual at every point in time, ranging from susceptible, exposed, infectious to recovered. This is the first model with efficient inference for COVID-19 infection spread based on individual-level contact data.
  arXiv  Detail & Related papers  (2020-06-09T04:25:56Z)

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.