Symptom data help predict COVID-19 admissions

Date:
April 21, 2022
Source:
Uppsala University
Summary:
Researchers are conducting one of the largest citizen science
projects in Sweden to date. Since the start of the pandemic, study
participants have used an app to report how they feel daily even
if they are well. This symptom data could be used to estimate
COVID-19 infection trends across Sweden and predict hospital
admissions due to COVID-19 a week in advance.



FULL STORY ========================================================================== Researchers at Lund University and Uppsala University are conducting
one of the largest citizen science projects in Sweden to date. Since the
start of the pandemic, study participants have used an app to report how
they feel daily even if they are well. This symptom data could be used
to estimate COVID-19 infection trends across Sweden and predict hospital admissions due to COVID-19 a week in advance. The results have now been published in the scientific journal Nature Communications.


==========================================================================
The analyses included more than 10 million daily reports from participants
in COVID Symptom Study Sweden from April 2020 to February 2021. The
scope of the study was to develop and evaluate a framework to estimate
the regional prevalence of COVID-19 using symptom-based surveillance,
and to test if these prevalence estimates could be used to predict
subsequent trends in COVID-19 hospital admissions.

"We show for the first time that symptom data can be informative in
predicting subsequent regional trends in hospital admissions due to
COVID-19, and confirm previous reports that trends in symptoms are related
to community infection rates. These symptoms-based methods could be particularly useful in time periods and areas with low COVID-19-testing,"
says Tove Fall, Professor of Molecular Epidemiology at the Department
of Medical Sciences, Uppsala University, one of the lead authors of
the study.

The app used for data collection was originally developed by ZOE, a
health science company, with support from physicians and researchers
at King's College London and Guy's and St Thomas' Hospitals, for
non-commercial purposes. The ZOE COVID Study was first launched in the
UK and the US in March 2020. It was adapted and introduced in Sweden,
where it is known as COVID Symptom Study Sweden, in April 2020. Any
adult in Sweden can participate by downloading the app and providing
in-app consent. Participants fill in a general baseline health survey,
and can then report how they feel each day, even if they are well. Over
209,000 participants in Sweden have contributed so far, providing daily
reports on symptoms, COVID-19 test results and vaccinations.

"This project would not have been possible without the dedication, hard
work and collaborative spirit of our team members and colleagues in the
UK and US.

Above all, we have to thank each and every study participant for
their contributions. Performing 'real-time' science is challenging,
but of utmost importance during a pandemic. We are proud that we have
been able to share real-time national and regional COVID-19 prevalence estimates on our dashboard almost every day since May 2020, and that
COVID Symptom Study Sweden data was useful to Swedish municipalities and
county councils. With over 4.7 million contributors globally, the ZOE
COVID Study is one of the largest ongoing public science projects of its
kind and has shown us the power of citizen science," says Maria Gomez, Professor of Physiology at the Department of Clinical Sciences and Lund University Diabetes Centre, one of the lead authors of the study.

Researchers developed and validated a model to understand which
symptoms were associated with a positive COVID-19 test, using data
from participants who had reported symptoms and results from COVID-19 PCR-tests. That model could then be employed to estimate daily national
and regional COVID-19 prevalence in the entire study population, as well
as subsequently in the Swedish adult population. Combining app-based
prevalence estimates with information on current hospital admissions, researchers were also able to predict future hospital admissions with
moderate accuracy. Furthermore, the same model could be successfully
applied to an English dataset to predict hospital admissions across the
seven English healthcare regions, highlighting the transferability of
the model to other countries.

"Real-time and granular pandemic surveillance requires combining
multiple sources of data," says Beatrice Kennedy, research fellow at the Department of Medical Sciences, Uppsala University and first author of the study. "Our findings highlight how app-based symptom-based surveillance
may constitute a scalable and dynamic tool to monitor infection trends,
and as such it should be considered in future pandemic preparedness
plans."

========================================================================== Story Source: Materials provided by Uppsala_University. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Beatrice Kennedy, Hugo Fitipaldi, Ulf Hammar, Marlena Maziarz, Neli
Tsereteli, Nikolay Oskolkov, Georgios Varotsis, Camilla A. Franks,
Diem Nguyen, Lampros Spiliopoulos, Hans-Olov Adami, Jonas Bjo"rk,
Stefan Engblom, Katja Fall, Anna Grimby-Ekman, Jan-Eric Litton,
Mats Martinell, Anna Oudin, Torbjo"rn Sjo"stro"m, Toomas Timpka,
Carole H. Sudre, Mark S.

Graham, Julien Lavigne du Cadet, Andrew T. Chan, Richard Davies,
Sajaysurya Ganesh, Anna May, Se'bastien Ourselin, Joan Capdevila
Pujol, Somesh Selvachandran, Jonathan Wolf, Tim D. Spector, Claire
J. Steves, Maria F. Gomez, Paul W. Franks, Tove Fall. App-based
COVID-19 syndromic surveillance and prediction of hospital
admissions in COVID Symptom Study Sweden. Nature Communications,
2022; 13 (1) DOI: 10.1038/s41467-022- 29608-7 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/04/220421094055.htm

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