COVID-19 Datathon Based on Deidentified Governmental Data as an Approach for Solving Policy Challenges, Increasing Trust, and Building a Community: Case Study

• URL: http://arxiv.org/abs/2108.13068v1
• Date: Mon, 30 Aug 2021 08:58:44 GMT
• Title: COVID-19 Datathon Based on Deidentified Governmental Data as an Approach for Solving Policy Challenges, Increasing Trust, and Building a Community: Case Study
• Authors: Mor Peleg, Amnon Reichman, Sivan Shachar, Tamir Gadot, Meytal Avgil Tsadok, Maya Azaria, Orr Dunkelman, Shiri Hassid, Daniella Partem, Maya Shmailov, Elad Yom-Tov, Roy Cohen
• Abstract summary: Israel's Ministry of Health (MoH) held a virtual Datathon based on deidentified governmental data. The Datathon was designed to develop operationalizable data-driven models to address COVID-19 health-policy challenges. The most positive results were increased trust in the MoH and greater readiness to work with the government.
• Score: 4.643473310978546
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Triggered by the COVID-19 crisis, Israel's Ministry of Health (MoH) held a virtual Datathon based on deidentified governmental data. Organized by a multidisciplinary committee, Israel's research community was invited to offer insights to COVID-19 policy challenges. The Datathon was designed to (1) develop operationalizable data-driven models to address COVID-19 health-policy challenges and (2) build a community of researchers from academia, industry, and government and rebuild their trust in the government. Three specific challenges were defined based on their relevance (significance, data availability, and potential to anonymize the data): immunization policies, special needs of the young population, and populations whose rate of compliance with COVID-19 testing is low. The MoH team extracted diverse, reliable, up-to-date, and deidentified governmental datasets for each challenge. Secure remote-access research environments with relevant data science tools were set on Amazon Web. The MoH screened the applicants and accepted around 80 participants, teaming them to balance areas of expertise as well as represent all sectors of the community. One week following the event, anonymous surveys for participants and mentors were distributed to assess overall usefulness and points for improvement. The 48-hour Datathon and pre-event sessions included 18 multidisciplinary teams, mentored by 20 data scientists, 6 epidemiologists, 5 presentation mentors, and 12 judges. The insights developed by the 3 winning teams are currently considered by the MoH as potential data science methods relevant for national policies. The most positive results were increased trust in the MoH and greater readiness to work with the government on these or future projects. Detailed feedback offered concrete lessons for improving the structure and organization of future government-led datathons.

Related papers

• Pennsieve: A Collaborative Platform for Translational Neuroscience and Beyond [0.5130659559809153]
  Pennsieve is an open-source, cloud-based scientific data management platform. It supports complex multimodal datasets and provides tools for data visualization and analyses. Pennsieve stores over 125 TB of scientific data, with 35 TB of data publicly available across more than 350 high-impact datasets.
  arXiv  Detail & Related papers  (2024-09-16T17:55:58Z)
• DSBench: How Far Are Data Science Agents to Becoming Data Science Experts? [58.330879414174476]
  We introduce DSBench, a benchmark designed to evaluate data science agents with realistic tasks. This benchmark includes 466 data analysis tasks and 74 data modeling tasks, sourced from Eloquence and Kaggle competitions. Our evaluation of state-of-the-art LLMs, LVLMs, and agents shows that they struggle with most tasks, with the best agent solving only 34.12% of data analysis tasks and achieving a 34.74% Relative Performance Gap (RPG)
  arXiv  Detail & Related papers  (2024-09-12T02:08:00Z)
• On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms [56.119374302685934]
  There have been severe concerns over the trustworthiness of AI technologies. Machine and deep learning algorithms depend heavily on the data used during their development. We propose a framework to evaluate the datasets through a responsible rubric.
  arXiv  Detail & Related papers  (2023-10-24T14:01:53Z)
• Assessing Scientific Contributions in Data Sharing Spaces [64.16762375635842]
  This paper introduces the SCIENCE-index, a blockchain-based metric measuring a researcher's scientific contributions. To incentivize researchers to share their data, the SCIENCE-index is augmented to include a data-sharing parameter. Our model is evaluated by comparing the distribution of its output for geographically diverse researchers to that of the h-index.
  arXiv  Detail & Related papers  (2023-03-18T19:17:47Z)
• The Monocular Depth Estimation Challenge [74.0535474077928]
  This paper summarizes the results of the first Monocular Depth Estimation Challenge (MDEC) organized at WACV2103. The challenge evaluated the progress of self-supervised monocular depth estimation on the challenging SYNS-Patches dataset.
  arXiv  Detail & Related papers  (2022-11-22T11:04:15Z)
• A Roadmap for Greater Public Use of Privacy-Sensitive Government Data: Workshop Report [11.431595898012377]
  The workshop specifically focused on challenges and successes in government data sharing at various levels. The first day focused on successful examples of new technology applied to sharing of public data, including formal privacy techniques, synthetic data, and cryptographic approaches.
  arXiv  Detail & Related papers  (2022-06-17T17:20:29Z)
• A Summary of COVID-19 Datasets [1.3490988186255934]
  This research presents a review of main datasets that are developed for COVID-19 research. We hope this collection will continue to bring together members of the computing community, biomedical experts, and policymakers.
  arXiv  Detail & Related papers  (2022-02-06T17:34:26Z)
• Protecting Privacy and Transforming COVID-19 Case Surveillance Datasets for Public Use [0.4462475518267084]
  CDC has collected person-level, de-identified data from jurisdictions and currently has over 8 million records. Data elements were included based on the usefulness, public request, and privacy implications. Specific field values were suppressed to reduce risk of reidentification and exposure of confidential information.
  arXiv  Detail & Related papers  (2021-01-13T14:24:20Z)
• Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy [61.60099467888073]
  We show how linking administrative data can enable auditing mobility data for bias. We show that older and non-white voters are less likely to be captured by mobility data. We show that allocating public health resources based on such mobility data could disproportionately harm high-risk elderly and minority groups.
  arXiv  Detail & Related papers  (2020-11-14T02:04:14Z)
• CovidNet: To Bring Data Transparency in the Era of COVID-19 [9.808021836153712]
  This paper presents CovidNet, a COVID-19 tracking project associated with a large scale epidemic dataset. CovidNet is the only platform providing real-time global case information of more than 4,124 sub-divisions from over 27 countries worldwide. The accuracy and freshness of the dataset is a result of the painstaking efforts from our voluntary teamwork, crowd-sourcing channels, and automated data pipelines.
  arXiv  Detail & Related papers  (2020-05-22T00:05:17Z)
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 [59.30734371401316]
  COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization. We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
  arXiv  Detail & Related papers  (2020-03-25T12:30:33Z)

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.