Elucidating user behaviours in a digital health surveillance system to correct prevalence estimates Article Swipe
Dennis Liu
,
Lewis Mitchell
,
Robert C. Cope
,
Sandra J. Carlson
,
Joshua V. Ross
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.epidem.2020.100404
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.epidem.2020.100404
Estimating seasonal influenza prevalence is of undeniable public health importance, but remains challenging with traditional datasets due to cost and timeliness. Digital epidemiology has the potential to address this challenge, but can introduce sampling biases that are distinct to traditional systems. In online participatory health surveillance systems, the voluntary nature of the data generating process must be considered to address potential biases in estimates. Here we examine user behaviours in one such platform, FluTracking, from 2011 to 2017. We build a Bayesian model to estimate probabilities of an individual reporting in each week, given their past reporting behaviour, and to infer the weekly prevalence of influenza-like-illness (ILI) in Australia. We show that a model that corrects for user behaviour can substantially affect ILI estimates. The model examined here elucidates several factors, such as the status of having ILI and consistency of prior reporting, that are strongly associated with the likelihood of participating in online health surveillance systems. This framework could be applied to other digital participatory health systems where participation is inconsistent and sampling bias may be of concern.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.epidem.2020.100404
- OA Status
- gold
- Cited By
- 2
- References
- 26
- Related Works
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- OpenAlex ID
- https://openalex.org/W3084058483
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3084058483Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.epidem.2020.100404Digital Object Identifier
- Title
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Elucidating user behaviours in a digital health surveillance system to correct prevalence estimatesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-09-09Full publication date if available
- Authors
-
Dennis Liu, Lewis Mitchell, Robert C. Cope, Sandra J. Carlson, Joshua V. RossList of authors in order
- Landing page
-
https://doi.org/10.1016/j.epidem.2020.100404Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.epidem.2020.100404Direct OA link when available
- Concepts
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Consistency (knowledge bases), Bayesian probability, Computer science, Public health, Influenza-like illness, Sampling (signal processing), Environmental health, Data science, Medicine, Econometrics, Artificial intelligence, Mathematics, Computer vision, Virus, Filter (signal processing), Nursing, VirologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
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2022: 1, 2021: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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