Symptom clusters from participatory surveillance data align with specific respiratory pathogens in Dutch and Italian cohorts
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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.
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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
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