Infection Risk Score: Identifying the risk of infection propagation based on human contact

• URL: http://arxiv.org/abs/2009.12588v1
• Date: Sat, 26 Sep 2020 13:25:06 GMT
• Title: Infection Risk Score: Identifying the risk of infection propagation based on human contact
• Authors: Rachit Agarwal, Abhik Banerjee
• Abstract summary: We introduce an infection risk score that provides an estimate of the infection risk arising from human contacts. Using a real-world human contact dataset, we show that the proposed risk score can provide a realistic estimate of the level of risk in the population.
• Score: 4.080171822768553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A wide range of approaches have been applied to manage the spread of global pandemic events such as COVID-19, which have met with varying degrees of success. Given the large-scale social and economic impact coupled with the increasing time span of the pandemic, it is important to not only manage the spread of the disease but also put extra efforts on measures that expedite resumption of social and economic life. It is therefore important to identify situations that carry high risk, and act early whenever such situations are identified. While a large number of mobile applications have been developed, they are aimed at obtaining information that can be used for contact tracing, but not at estimating the risk of social situations. In this paper, we introduce an infection risk score that provides an estimate of the infection risk arising from human contacts. Using a real-world human contact dataset, we show that the proposed risk score can provide a realistic estimate of the level of risk in the population. We also describe how the proposed infection risk score can be implemented on smartphones. Finally, we identify representative use cases that can leverage the risk score to minimize infection propagation.

Related papers

• Fuzzy Rule based Intelligent Cardiovascular Disease Prediction using Complex Event Processing [0.8668211481067458]
  Cardiovascular disease (CVDs) is a rapidly rising global concern due to unhealthy diets, lack of physical activity, and other factors. Recent research has focused on accurate and timely disease prediction to reduce risk and fatalities. We propose a fuzzy rule-based system for monitoring clinical data to provide real-time decision support.
  arXiv  Detail & Related papers  (2024-09-19T16:36:24Z)
• Boosting the interpretability of clinical risk scores with intervention predictions [59.22442473992704]
  We propose a joint model of intervention policy and adverse event risk as a means to explicitly communicate the model's assumptions about future interventions. We show how combining typical risk scores, such as the likelihood of mortality, with future intervention probability scores leads to more interpretable clinical predictions.
  arXiv  Detail & Related papers  (2022-07-06T19:49:42Z)
• Epidemic inference through generative neural networks [0.0]
  We present a new generative neural networks framework that can sample the most probable infection cascades compatible with observations. The framework can infer the parameters governing the spreading of infections.
  arXiv  Detail & Related papers  (2021-11-05T10:40:10Z)
• Towards Accurate Spatiotemporal COVID-19 Risk Scores using High Resolution Real-World Mobility Data [15.302926747159557]
  We develop a Hawkes process-based technique to assign relatively fine-grain spatial and temporal risk scores. We focus on developing risk scores based on location density and mobility behaviour. Our results show that fine-grain risk scores based on high-resolution mobility data can provide useful insights and facilitate safe re-opening.
  arXiv  Detail & Related papers  (2020-12-14T06:31:28Z)
• 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)
• 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)
• A Deep Q-learning/genetic Algorithms Based Novel Methodology For Optimizing Covid-19 Pandemic Government Actions [63.669642197519934]
  We use the SEIR epidemiological model to represent the evolution of the virus COVID-19 over time in the population. The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system. We prove that our methodology is a valid tool to discover actions governments can take to reduce the negative effects of a pandemic in both senses.
  arXiv  Detail & Related papers  (2020-05-15T17:17:45Z)
• Digital Ariadne: Citizen Empowerment for Epidemic Control [55.41644538483948]
  The COVID-19 crisis represents the most dangerous threat to public health since the H1N1 pandemic of 1918. Technology-assisted location and contact tracing, if broadly adopted, may help limit the spread of infectious diseases. We present a tool, called 'diAry' or 'digital Ariadne', based on voluntary location and Bluetooth tracking on personal devices.
  arXiv  Detail & Related papers  (2020-04-16T15:53:42Z)

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.