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Article overview
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Calculating incidence of Influenza-like and COVID-like symptoms from Flutracking participatory survey data | | Emily P. Harvey
; Joel A. Trent
; Frank Mackenzie
; Steven M. Turnbull
; Dion R.J. O'Neale
; | | Date: |
1 Aug 2022 | | 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:
1) 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.
2) Adjusting for symptom reporting bias 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.
3) Weighting responses by age to adjust for self-selection bias in order to
account for the under- and over-representation of different age groups amongst
the survey participants. This uses the survey package in R.
4) Constructing 95% point-wise confidence bands for incidence estimates using
weighted logistic regression from the survey package in R.
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. | | Source: | arXiv, 2208.00568 | | Services: | Forum | Review | PDF | Favorites | |
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