COVID-19 Tests Gone Rogue: Privacy, Efficacy, Mismanagement and Misunderstandings

• URL: http://arxiv.org/abs/2101.01693v3
• Date: Fri, 7 May 2021 14:25:18 GMT
• Title: COVID-19 Tests Gone Rogue: Privacy, Efficacy, Mismanagement and Misunderstandings
• Authors: Manuel Morales, Rachel Barbar, Darshan Gandhi, Sanskruti Landage, Joseph Bae, Arpita Vats, Jil Kothari, Sheshank Shankar, Rohan Sukumaran, Himi Mathur, Krutika Misra, Aishwarya Saxena, Parth Patwa, Sethuraman T. V., Maurizio Arseni, Shailesh Advani, Kasia Jakimowicz, Sunaina Anand, Priyanshi Katiyar, Ashley Mehra, Rohan Iyer, Srinidhi Murali, Aryan Mahindra, Mikhail Dmitrienko, Saurish Srivastava, Ananya Gangavarapu, Steve Penrod, Vivek Sharma, Abhishek Singh and Ramesh Raskar
• Abstract summary: We review the current landscape of COVID-19 testing, identify four key challenges, and discuss the consequences of the failure to address these challenges. The current infrastructure around testing and information propagation is highly privacy-invasive and does not leverage scalable digital components.
• Score: 8.154109462429988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: COVID-19 testing, the cornerstone for effective screening and identification of COVID-19 cases, remains paramount as an intervention tool to curb the spread of COVID-19 both at local and national levels. However, the speed at which the pandemic struck and the response was rolled out, the widespread impact on healthcare infrastructure, the lack of sufficient preparation within the public health system, and the complexity of the crisis led to utter confusion among test-takers. Invasion of privacy remains a crucial concern. The user experience of test takers remains low. User friction affects user behavior and discourages participation in testing programs. Test efficacy has been overstated. Test results are poorly understood resulting in inappropriate follow-up recommendations. Herein, we review the current landscape of COVID-19 testing, identify four key challenges, and discuss the consequences of the failure to address these challenges. The current infrastructure around testing and information propagation is highly privacy-invasive and does not leverage scalable digital components. In this work, we discuss challenges complicating the existing covid-19 testing ecosystem and highlight the need to improve the testing experience for the user and reduce privacy invasions. Digital tools will play a critical role in resolving these challenges.

Related papers

• Engaging Developers in Exploratory Unit Testing through Gamification [11.077232808482128]
  We show challenges and quests generated by the Gamekins system to make testing more engaging and seamlessly blend it with regular coding tasks. In a 60-minute experiment, we evaluated Gamekins' impact on test suite quality and bug detection. Results show that participants actively interacted with the tool, achieving nearly 90% line coverage and detecting 11 out of 14 bugs.
  arXiv  Detail & Related papers  (2024-08-09T08:00:41Z)
• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
  This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images [66.63200823918429]
  COVID-Net USPro monitors and detects COVID-19 positive cases with high precision and recall from minimal ultrasound images. The network achieves 99.65% overall accuracy, 99.7% recall and 99.67% precision for COVID-19 positive cases when trained with only 5 shots.
  arXiv  Detail & Related papers  (2023-01-04T16:05:51Z)
• When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning Framework in Classification of Medical Images on Limited Data: A COVID-19 Case Study [77.34726150561087]
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. CNN has been widely utilized and verified in analyzing medical images.
  arXiv  Detail & Related papers  (2022-03-24T02:09:41Z)
• Reducing Risk and Uncertainty of Deep Neural Networks on Diagnosing COVID-19 Infection [1.3701366534590498]
  This work introduces uncertainty estimation to detect confusing cases for expert referral. In collaboration with medical professionals, we further validate the results to ensure the viability of the best performing method in clinical practice.
  arXiv  Detail & Related papers  (2021-04-28T21:36:25Z)
• HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data [56.53715632642495]
  Clinical trials face uncertain outcomes due to issues with efficacy, safety, or problems with patient recruitment. In this paper, we propose Hierarchical INteraction Network (HINT) for more general, clinical trial outcome predictions.
  arXiv  Detail & Related papers  (2021-02-08T15:09:07Z)
• Digital Landscape of COVID-19 Testing: Challenges and Opportunities [8.445546861208243]
  Digital technology, currently being used for COVID-19 testing include certain mobile apps, web dashboards, and online self-assessment tools. We summarize the challenges experienced using these tools in terms of quality of information, privacy, and user-centric issues.
  arXiv  Detail & Related papers  (2020-12-03T09:01:51Z)
• A Partially Observable MDP Approach for Sequential Testing for Infectious Diseases such as COVID-19 [29.84897273754802]
  We show that the testing problem can be cast as a sequential learning-based resource allocation problem with constraints. We then develop efficient learning strategies that minimize the number of infected individuals.
  arXiv  Detail & Related papers  (2020-07-25T22:13:37Z)
• Challenges in Combating COVID-19 Infodemic -- Data, Tools, and Ethics [36.203933386216534]
  We present three key challenges in this fight against the COVID-19 infodemic where researchers and practitioners instinctively want to contribute and help. We demonstrate that these three challenges can and will be effectively addressed by collective wisdom, crowdsourcing, and collaborative research.
  arXiv  Detail & Related papers  (2020-05-27T22:41:02Z)
• Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design [63.48989885374238]
  When the infection prevalence of a disease is low, Dorfman showed 80 years ago that testing groups of people can prove more efficient than testing people individually. Our goal in this paper is to propose new group testing algorithms that can operate in a noisy setting.
  arXiv  Detail & Related papers  (2020-04-26T23:41:33Z)
• A Study of Knowledge Sharing related to Covid-19 Pandemic in Stack Overflow [69.5231754305538]
  Study of 464 Stack Overflow questions posted mainly in February and March 2020 and leveraging the power of text mining. Findings reveal that indeed this global crisis sparked off an intense and increasing activity in Stack Overflow with most post topics reflecting a strong interest on the analysis of Covid-19 data.
  arXiv  Detail & Related papers  (2020-04-18T08:19:46Z)

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.