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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/447| Elucidating user behaviours in a digital health surveillance system to correct prevalence estimates | |
| Lewis Mitchell Sandra J. Carlson Joshua V. Ross Dennis Liu Robert C. Cope | |
| Novel Coronavirus | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| 10.1101/19003715 | |
| 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 effect 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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Funding and support was provided by the Data To Decisions Collaborative Research Centre (D2D CRC), the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), the National Health and Medical Research Council (NHMRC) Centre of Research Excellence in Policy Relevant Infectious diseases Simulation and Mathematical Modelling (PRISM2), and an Australian Government Research Training Program (RTP) Scholarship. ### Author Declarations All relevant ethical guidelines have been followed and any necessary IRB and/or ethics committee approvals have been obtained. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes Any clinical trials involved have been registered with an ICMJE-approved registry such as ClinicalTrials.gov and the trial ID is included in the manuscript. Not Applicable I have followed all appropriate research reporting guidelines and uploaded the relevant Equator, ICMJE or other checklist(s) as supplementary files, if applicable. Not Applicable Reproduction of this study would require individual level data, which is precluded by ethics approval (The University of Adelaide Human Research Ethics Committee (HREC) H-2017-131). Results, simulations used for validation and code is linked below. <https://tdennisliu.github.io/publications/FTBehaviour/Supplementary_Data.zip> | |
| Cold Spring Harbor Laboratory Press | |
| 2020 | |
| Preimpreso | |
| https://www.medrxiv.org/content/10.1101/19003715v2 | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| Elucidating user behaviours.pdf | 2.54 MB | Adobe PDF | Visualizar/Abrir |