Symptom clusters from participatory surveillance data align with specific respiratory pathogens in Dutch and Italian cohortsCan your cough tell you which virus you have? Symptom patterns may hold clues

medRxiv Published April 4, 2026 Study authors: Carstens, G.; Mazzoli, M.; Gozzi, N.; van Hoek, A. J.; Paolotti, D. DOI ↗ Editorial oversight: Dr. Amelia Tan, PhD · Internal Medicine & Chronic Disease

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Key Takeaway

Consider that symptom clusters from participatory surveillance may align with specific pathogens, but evidence is observational.

This observational cohort study analyzed data from symptomatic participants in participatory surveillance platforms in the Netherlands and Italy over a five-year period (2020-2025). Researchers applied Non-negative Matrix Factorization (NMF) to symptom data, identifying eight symptom clusters in the Dutch cohort. Three clusters showed specific alignment with pathogen detection: one with SARS-CoV-2, one with rhinovirus, and a third with influenza virus, RSV, and seasonal coronaviruses.

When these Dutch-derived symptom clusters were applied to Italian data, researchers found consistency in key components between the two cohorts, particularly for clusters associated with SARS-CoV-2. This suggests potential transferability of symptom patterns across different surveillance systems. The study did not report sample sizes, effect sizes, p-values, confidence intervals, or absolute numbers for these associations.

No safety or tolerability data were reported, as the study analyzed existing surveillance data rather than implementing interventions. The analysis has several limitations: it is observational and cannot establish causality, statistical measures were not reported, and transferability findings are limited to specific pathogens and the studied cohorts during this time period. The researchers note that unsupervised symptom decomposition may help identify trends in co-circulating respiratory pathogens from syndromic surveillance data, potentially providing timely insights into pathogen circulation patterns. However, clinicians should interpret these findings cautiously given the observational nature and lack of reported statistical validation.

Imagine if the symptoms you report when you're sick could help public health officials spot which virus is going around town. That's the idea behind a new look at data from online health surveillance platforms in the Netherlands and Italy. Researchers analyzed symptom reports from volunteers over five years, using a method to find patterns in the data. They found that certain groups of symptoms—or 'clusters'—tended to line up with specific viruses. One cluster was linked to SARS-CoV-2, another to rhinovirus (a common cold virus), and a third to a mix of influenza, RSV, and seasonal coronaviruses. The analysis also showed that the symptom pattern linked to COVID-19 in the Dutch data looked similar when they checked it against data from Italy. This suggests that the way people report symptoms for certain viruses might be consistent across different countries. It's important to remember this was an observational study, meaning it looked at patterns in existing data without testing any interventions. The researchers didn't report key details like the total number of participants or statistical measures of strength. The findings are a promising signal that public symptom tracking could offer timely clues about which pathogens are circulating, but they don't prove cause and effect, and their relevance might be limited to the specific groups and time period studied.

What this means for you:

Online symptom reports show patterns that align with different viruses, hinting at a new way to track outbreaks.

Study Details

Study typeCohort

EvidenceLevel 3

PublishedMar 2026

View Original Abstract ↓

BackgroundThe annual respiratory season in Europe is marked by the co-circulation of multiple respiratory pathogens, such as influenza viruses, rhinoviruses, and coronaviruses. Effective surveillance is necessary but hampered by heterogeneity of case definitions and limited pathogen specificity in existing systems. This study aims to detect pathogen-specific signals in the participatory surveillance of the Netherlands using a sub-set of samples with virological detection. Additionally, we explore a method to use the findings in the Netherlands to enhance the virological interpretation of participatory surveillance data in Italy. MethodsWe analyzed symptom data collected through a participatory surveillance platform in the Netherlands and Italy over five years (2020-2025). Symptom-by-week matrices from the Dutch cohort were aggregated into syndromes and their associated time series using Non-negative Matrix Factorization (NMF). We compared the respective time series of the syndromes with influenza virus, SARS-CoV-2, seasonal coronaviruses, RSV, and rhinovirus incidence estimated from nose- and throat swabs of a subsample of symptomatic participants of the participatory surveillance platform in the Netherlands. We tested the transferability of these components by applying the Dutch-derived components to describe Italian symptom data and extract respective incidences. ResultsNMF identified eight symptom clusters in the Dutch cohort, one aligning with SARS-CoV-2, one aligning with rhinovirus and a third component aligning with influenza virus, RSV and seasonal incidences estimated from collected nose- and throat swabs. Transferring Dutch-derived symptom clusters to Italian data showed consistency in key components between Dutch and Italian cohorts, particularly those associated with SARS-CoV-2. ConclusionThis study demonstrates that unsupervised symptom decomposition can identify co-circulating respiratory pathogens trends from syndromic surveillance data, providing timely pathogen circulation insights. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/26349719v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): [email protected]@ad0bd6org.highwire.dtl.DTLVardef@e44d85org.highwire.dtl.DTLVardef@9d93be_HPS_FORMAT_FIGEXP M_FIG C_FIG

Symptom clusters from participatory surveillance data align with specific respiratory pathogens in Dutch and Italian cohortsCan your cough tell you which virus you have? Symptom patterns may hold clues

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Symptom clusters from participatory surveillance data align with specific respiratory pathogens in Dutch and Italian cohortsCan your cough tell you which virus you have? Symptom patterns may hold clues

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