scholarly journals COVID-19 Insights Partnership: Leveraging Big Data from the Department of Veterans Affairs and Supercomputers at the Department of Energy under the Public Health Authority

2021 ◽  
Author(s):  
Rachel Ramoni ◽  
Molly Klote ◽  
Sumitra Muralidhar ◽  
Cynthia Brandt ◽  
Maya A Bernstein ◽  
...  
Keyword(s):  
Public Health ◽  
Big Data ◽  
Health Authority ◽  
Veterans Affairs ◽  
Department Of Energy ◽  
Public Health Authority ◽  
Department Of Veterans Affairs ◽  
The Public

scholarly journals How should we present the epidemic curve for COVID-19?

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jean-Paul R. Soucy ◽  
Sarah A. Buchan ◽  
Kevin A. Brown
Keyword(s):  
Public Health ◽  
Symptom Onset ◽  
Health Authority ◽  
Public Reporting ◽  
Decision Makers ◽  
Public Health Authority ◽  
Onset Date ◽  
The Public ◽  
Epidemic Curve ◽  
Incident Cases

Epidemic curves are used by decision makers and the public to infer the trajectory of the COVID-19 pandemic and to understand the appropriateness of response measures. Symptom onset date is commonly used to date incident cases on the epidemic curve in public health reports and dashboards; however, third-party trackers date cases by the date they were publicly reported by the public health authority. These two curves create very different impressions of epidemic progression. On April 1, 2020, the epidemic curve based on public reporting date for Ontario, Canada showed an accelerating epidemic, whereas the curve based on a proxy variable for symptom onset date showed a rapidly declining epidemic. This illusory downward trend is a feature of epidemic curves anchored using date variables earlier in time than the date a case was publicly reported, such as the symptom onset date. Delays between the onset of symptoms and the detection of a case by the public health authority mean that recent days will always have incomplete case data, creating a downward bias. Public reporting date is not subject to this bias and can be used to visualize real-time epidemic curves meant to inform the public and decision makers.

Navigating the Ethics of Big Data in Public Health

2019 ◽  
pp. 353-367
Author(s):  
Effy Vayena ◽  
Lawrence Madoff
Keyword(s):  
Public Health ◽  
Big Data ◽  
Credit Card ◽  
Health Sector ◽  
Public Health Research ◽  
Ethical Challenges ◽  
The Public ◽  
Health Community ◽  
Digital Disease Detection ◽  
The Public Sphere

“Big data,” which encompasses massive amounts of information from both within the health sector (such as electronic health records) and outside the health sector (social media, search queries, cell phone metadata, credit card expenditures), is increasingly envisioned as a rich source to inform public health research and practice. This chapter examines the enormous range of sources, the highly varied nature of these data, and the differing motivations for their collection, which together challenge the public health community in ethically mining and exploiting big data. Ethical challenges revolve around the blurring of three previously clearer boundaries: between personal health data and nonhealth data; between the private and the public sphere in the online world; and, finally, between the powers and responsibilities of state and nonstate actors in relation to big data. Considerations include the implications for privacy, control and sharing of data, fair distribution of benefits and burdens, civic empowerment, accountability, and digital disease detection.

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