ORCID Profile
0000-0001-6819-1796
Current Organisation
University of California, San Diego
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Publisher: American Psychiatric Association Publishing
Date: 06-2021
Publisher: Springer Science and Business Media LLC
Date: 30-04-2021
DOI: 10.1186/S12910-021-00616-4
Abstract: Ethics review is the process of assessing the ethics of research involving humans. The Ethics Review Committee (ERC) is the key oversight mechanism designated to ensure ethics review. Whether or not this governance mechanism is still fit for purpose in the data-driven research context remains a debated issue among research ethics experts. In this article, we seek to address this issue in a twofold manner. First, we review the strengths and weaknesses of ERCs in ensuring ethical oversight. Second, we map these strengths and weaknesses onto specific challenges raised by big data research. We distinguish two categories of potential weakness. The first category concerns persistent weaknesses, i.e., those which are not specific to big data research, but may be exacerbated by it. The second category concerns novel weaknesses, i.e., those which are created by and inherent to big data projects. Within this second category, we further distinguish between purview weaknesses related to the ERC’s scope (e.g., how big data projects may evade ERC review) and functional weaknesses, related to the ERC’s way of operating. Based on this analysis, we propose reforms aimed at improving the oversight capacity of ERCs in the era of big data science. We believe the oversight mechanism could benefit from these reforms because they will help to overcome data-intensive research challenges and consequently benefit research at large.
Publisher: Public Library of Science (PLoS)
Date: 18-08-2023
DOI: 10.1371/JOURNAL.PONE.0287368
Abstract: Digital methods to augment traditional contact tracing approaches were developed and deployed globally during the COVID-19 pandemic. These “Exposure Notification (EN)” systems present new opportunities to support public health interventions. To date, there have been attempts to model the impact of such systems, yet no reports have explored the value of real-time system data for predictive epidemiological modeling. We investigated the potential to short-term forecast COVID-19 caseloads using data from California’s implementation of the Google Apple Exposure Notification (GAEN) platform, branded as CA Notify. CA Notify is a digital public health intervention leveraging resident’s smartphones for anonymous EN. We extended a published statistical model that uses prior case counts to investigate the possibility of predicting short-term future case counts and then added EN activity to test for improved forecast performance. Additional predictive value was assessed by comparing the pandemic forecasting models with and without EN activity to the actual reported caseloads from 1–7 days in the future. Observation of time series presents noticeable evidence for temporal association of system activity and caseloads. Incorporating earlier ENs in our model improved prediction of the caseload counts. Using Bayesian inference, we found nonzero influence of EN terms with probability one. Furthermore, we found a reduction in both the mean absolute percentage error and the mean squared prediction error, the latter of at least 5% and up to 32% when using ENs over the model without. This preliminary investigation suggests smartphone based ENs can significantly improve the accuracy of short-term forecasting. These predictive models can be readily deployed as local early warning systems to triage resources and interventions.
Publisher: SAGE Publications
Date: 24-07-2019
Publisher: JMIR Publications Inc.
Date: 28-06-2017
DOI: 10.2196/MHEALTH.6494
Location: United States of America
No related grants have been discovered for Camille Nebeker.