dc.contributor.author |
Harvey, Emily P |
|
dc.contributor.author |
Trent, Joel A |
|
dc.contributor.author |
Mackenzie, Frank |
|
dc.contributor.author |
Turnbull, Steven M |
|
dc.contributor.author |
O'Neale, Dion RJ |
|
dc.coverage.spatial |
Netherlands |
|
dc.date.accessioned |
2023-01-22T22:42:38Z |
|
dc.date.available |
2023-01-22T22:42:38Z |
|
dc.date.issued |
2022-01 |
|
dc.identifier.citation |
(2022). MethodsX, 9, 101820-. |
|
dc.identifier.issn |
2215-0161 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/62506 |
|
dc.description.abstract |
This article describes a new method for estimating weekly incidence (new onset) of symptoms consistent with Influenza and COVID-19, using data from the Flutracking survey. The method mitigates some of the known self-selection and symptom-reporting biases present in existing approaches to this type of participatory longitudinal survey data. The key novel steps in the analysis are: <b>1)</b> Identifying new onset of symptoms for three different Symptom Groupings: COVID-like illness (CLI1+, CLI2+), and Influenza-like illness (ILI), for responses reported in the Flutracking survey. <b>2)</b> Adjusting for <i>symptom reporting bias</i> by restricting the analysis to a sub-set of responses from those participants who have consistently responded for a number of weeks prior to the analysis week. <b>3)</b> Weighting responses by age to adjust for <i>self-selection bias</i> in order to account for the under- and over-representation of different age groups amongst the survey participants. This uses the survey package [22] in R [30]. <b>4)</b> Constructing 95% point-wise confidence bands for incidence estimates using weighted logistic regression from the survey package [21] in R [28]. In addition to describing these steps, the article demonstrates an application of this method to Flutracking data for the 12 months from 27th April 2020 until 25th April 2021. |
|
dc.format.medium |
Print-Electronic |
|
dc.language |
eng |
|
dc.publisher |
Elsevier |
|
dc.relation.ispartofseries |
MethodsX |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.rights.uri |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
dc.subject |
COVID-19 |
|
dc.subject |
Flutracking |
|
dc.subject |
Incidence |
|
dc.subject |
Influenza-like illness |
|
dc.subject |
Logistic regression |
|
dc.subject |
Participatory epidemiology |
|
dc.subject |
Reporting bias |
|
dc.subject |
Survey analysis |
|
dc.subject |
Survey re-weighting |
|
dc.subject |
Syndromic surveillance |
|
dc.subject |
Prevention |
|
dc.subject |
Biodefense |
|
dc.subject |
Infectious Diseases |
|
dc.subject |
Pneumonia & Influenza |
|
dc.subject |
Behavioral and Social Science |
|
dc.subject |
Vaccine Related |
|
dc.subject |
Influenza |
|
dc.subject |
Emerging Infectious Diseases |
|
dc.subject |
0912 Materials Engineering |
|
dc.title |
Calculating incidence of Influenza-like and COVID-like symptoms from Flutracking participatory survey data. |
|
dc.type |
Journal Article |
|
dc.identifier.doi |
10.1016/j.mex.2022.101820 |
|
pubs.begin-page |
101820 |
|
pubs.volume |
9 |
|
dc.date.updated |
2022-12-03T02:34:31Z |
|
dc.rights.holder |
Copyright: The authors |
en |
dc.identifier.pmid |
35993031 (pubmed) |
|
pubs.author-url |
https://www.ncbi.nlm.nih.gov/pubmed/35993031 |
|
pubs.publication-status |
Published |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
research-article |
|
pubs.subtype |
Journal Article |
|
pubs.elements-id |
917714 |
|
pubs.org-id |
Science |
|
pubs.org-id |
Physics |
|
dc.identifier.eissn |
2215-0161 |
|
dc.identifier.pii |
S2215-0161(22)00200-X |
|
pubs.number |
101820 |
|
pubs.record-created-at-source-date |
2022-12-03 |
|
pubs.online-publication-date |
2022-08-17 |
|