Calculating incidence of Influenza-like and COVID-like symptoms from Flutracking participatory survey data

TL;DR

This paper introduces a new method to estimate weekly incidence of influenza-like and COVID-like symptoms from participatory survey data, addressing biases and providing confidence intervals, demonstrated on a year-long dataset.

Contribution

The paper presents a novel approach for bias mitigation and incidence estimation in participatory surveys, including new onset detection, bias adjustments, and confidence band construction.

Findings

Effective bias correction methods demonstrated on real data

Accurate weekly incidence estimates with confidence intervals

Application to a year-long dataset shows practical utility

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.