Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots

• URL: http://arxiv.org/abs/2004.07641v6
• Date: Thu, 10 Nov 2022 14:12:13 GMT
• Title: Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots
• Authors: Lars Lorch, Heiner Kremer, William Trouleau, Stratis Tsirtsis, Aron Szanto, Bernhard Sch\"olkopf, and Manuel Gomez-Rodriguez
• Abstract summary: Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19. We introduce a temporal point process modeling framework that specifically represents visits to the sites where individuals get in contact and infect each other. Under our model, the number of infections caused by an infectious individual naturally emerges to be overdispersed.
• Score: 18.227721607607183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19. However, most of the existing epidemiological models fail to capture this aspect by neither representing the sites visited by individuals explicitly nor characterizing disease transmission as a function of individual mobility patterns. In this work, we introduce a temporal point process modeling framework that specifically represents visits to the sites where individuals get in contact and infect each other. Under our model, the number of infections caused by an infectious individual naturally emerges to be overdispersed. Using an efficient sampling algorithm, we demonstrate how to estimate the transmission rate of infectious individuals at the sites they visit and in their households using Bayesian optimization and longitudinal case data. Simulations using fine-grained and publicly available demographic data and site locations from Bern, Switzerland showcase the flexibility of our framework. To facilitate research and analyses of other cities and regions, we release an open-source implementation of our framework.

Related papers

• Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold [83.18058549195855]
  We argue that multiple processes in natural sciences have to be represented as vector fields on the Wasserstein manifold of probability densities. In particular, this is crucial for personalized medicine where the development of diseases and their respective treatment response depends on the microenvironment of cells specific to each patient. We propose Meta Flow Matching (MFM), a practical approach to integrating along these vector fields on the Wasserstein manifold by amortizing the flow model over the initial populations.
  arXiv  Detail & Related papers  (2024-08-26T20:05:31Z)
• 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)
• 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)
• Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data [57.19441629270029]
  In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of survival analysis models. In the realistic setting of small medical datasets and only a few data centers, this noise makes it harder for the models to converge. We propose DPFed-post which adds a post-processing stage to the private federated learning scheme.
  arXiv  Detail & Related papers  (2022-02-08T10:03:24Z)
• 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)
• An Extended Epidemic Model on Interconnected Networks for COVID-19 to Explore the Epidemic Dynamics [2.89591830279936]
  The pandemic control necessitates epidemic models that capture the trends and impacts on infectious individuals. Many exciting models can implement this but they lack practical interpretability. This study combines the epidemiological and network theories and proposes a framework with causal interpretability.
  arXiv  Detail & Related papers  (2021-04-10T06:46:01Z)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)
• 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)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• 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)
• A General Framework for Survival Analysis and Multi-State Modelling [70.31153478610229]
  We use neural ordinary differential equations as a flexible and general method for estimating multi-state survival models. We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting.
  arXiv  Detail & Related papers  (2020-06-08T19:24:54Z)

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.