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Dave R, Gupta R. Data Quality and Network Considerations for Mobile Contact Tracing and Health Monitoring. Front Digit Health 2022; 3:590194. [PMID: 34977855 PMCID: PMC8715913 DOI: 10.3389/fdgth.2021.590194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/23/2021] [Indexed: 12/22/2022] Open
Abstract
Machine Learning (ML) has been a useful tool for scientific advancement during the COVID-19 pandemic. Contact tracing apps are just one area reaping the benefits, as ML can use location and health data from these apps to forecast virus spread, predict “hotspots,” and identify vulnerable groups. However, to do so, it is first important to ensure that the dataset these apps yield is accurate, free of biases, and reliable, as any flaw can directly influence ML predictions. Given the lack of criteria to help ensure this, we present two requirements for those exploring using ML to follow. The requirements we presented work to uphold international data quality standards put forth for ML. We then identify where our requirements can be met, as countries have varying contact tracing apps and smartphone usages. Lastly, the advantages, limitations, and ethical considerations of our approach are discussed.
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Affiliation(s)
- Riya Dave
- Cognitive and Behavioural Neuroscience Laboratory, Department of Humanities and Social Sciences, Indian Institute of Technology Bombay, Mumbai, India
| | - Rashmi Gupta
- Cognitive and Behavioural Neuroscience Laboratory, Department of Humanities and Social Sciences, Indian Institute of Technology Bombay, Mumbai, India
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O'Connell J, Abbas M, Beecham S, Buckley J, Chochlov M, Fitzgerald B, Glynn L, Johnson K, Laffey J, McNicholas B, Nuseibeh B, O'Callaghan M, O'Keeffe I, Razzaq A, Rekanar K, Richardson I, Simpkin A, Storni C, Tsvyatkova D, Walsh J, Welsh T, O'Keeffe D. Best Practice Guidance for Digital Contact Tracing Apps: A Cross-disciplinary Review of the Literature. JMIR Mhealth Uhealth 2021; 9:e27753. [PMID: 34003764 PMCID: PMC8189288 DOI: 10.2196/27753] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/17/2021] [Accepted: 04/05/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Digital contact tracing apps have the potential to augment contact tracing systems and disrupt COVID-19 transmission by rapidly identifying secondary cases prior to the onset of infectiousness and linking them into a system of quarantine, testing, and health care worker case management. The international experience of digital contact tracing apps during the COVID-19 pandemic demonstrates how challenging their design and deployment are. OBJECTIVE This study aims to derive and summarize best practice guidance for the design of the ideal digital contact tracing app. METHODS A collaborative cross-disciplinary approach was used to derive best practice guidance for designing the ideal digital contact tracing app. A search of the indexed and gray literature was conducted to identify articles describing or evaluating digital contact tracing apps. MEDLINE was searched using a combination of free-text terms and Medical Subject Headings search terms. Gray literature sources searched were the World Health Organization Institutional Repository for Information Sharing, the European Centre for Disease Prevention and Control publications library, and Google, including the websites of many health protection authorities. Articles that were acceptable for inclusion in this evidence synthesis were peer-reviewed publications, cohort studies, randomized trials, modeling studies, technical reports, white papers, and media reports related to digital contact tracing. RESULTS Ethical, user experience, privacy and data protection, technical, clinical and societal, and evaluation considerations were identified from the literature. The ideal digital contact tracing app should be voluntary and should be equitably available and accessible. User engagement could be enhanced by small financial incentives, enabling users to tailor aspects of the app to their particular needs and integrating digital contact tracing apps into the wider public health information campaign. Adherence to the principles of good data protection and privacy by design is important to convince target populations to download and use digital contact tracing apps. Bluetooth Low Energy is recommended for a digital contact tracing app's contact event detection, but combining it with ultrasound technology may improve a digital contact tracing app's accuracy. A decentralized privacy-preserving protocol should be followed to enable digital contact tracing app users to exchange and record temporary contact numbers during contact events. The ideal digital contact tracing app should define and risk-stratify contact events according to proximity, duration of contact, and the infectiousness of the case at the time of contact. Evaluating digital contact tracing apps requires data to quantify app downloads, use among COVID-19 cases, successful contact alert generation, contact alert receivers, contact alert receivers that adhere to quarantine and testing recommendations, and the number of contact alert receivers who subsequently are tested positive for COVID-19. The outcomes of digital contact tracing apps' evaluations should be openly reported to allow for the wider public to review the evaluation of the app. CONCLUSIONS In conclusion, key considerations and best practice guidance for the design of the ideal digital contact tracing app were derived from the literature.
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Affiliation(s)
- James O'Connell
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
| | - Manzar Abbas
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
| | - Sarah Beecham
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
| | - Jim Buckley
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
| | - Muslim Chochlov
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
| | - Brian Fitzgerald
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
| | - Liam Glynn
- School of Medicine, University of Limerick, Limerick, Ireland
| | - Kevin Johnson
- Department of Nursing and Midwifery, University of Limerick, Limerick, Ireland
| | - John Laffey
- School of Medicine, National University of Ireland Galway, Galway, Ireland
- University Hospital Galway, Saolta, Health Services Executive, Galway, Ireland
| | - Bairbre McNicholas
- School of Medicine, National University of Ireland Galway, Galway, Ireland
- University Hospital Galway, Saolta, Health Services Executive, Galway, Ireland
| | - Bashar Nuseibeh
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
- School of Computing and Communications, The Open University, Milton Keynes, United Kingdom
| | | | - Ian O'Keeffe
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
| | - Abdul Razzaq
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
| | - Kaavya Rekanar
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
| | - Ita Richardson
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
| | - Andrew Simpkin
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, Galway, Ireland
| | - Cristiano Storni
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
| | - Damyanka Tsvyatkova
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
| | - Jane Walsh
- School of Psychology, National University of Ireland Galway, Galway, Ireland
| | - Thomas Welsh
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
| | - Derek O'Keeffe
- Lero, Science Foundation Ireland Research Centre for Software, University of Limerick, Limerick, Ireland
- School of Medicine, National University of Ireland Galway, Galway, Ireland
- University Hospital Galway, Saolta, Health Services Executive, Galway, Ireland
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