Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 10, 2025
Date Accepted: May 6, 2026
Harnessing Participatory Surveillance Cohorts and Proxy Indicators to Dynamically Track Epidemic Trends and Undiagnosed COVID-19 Infections in Singapore
ABSTRACT
Background:
Accurate COVID-19 incidence estimates, including undiagnosed cases, are vital for epidemic management but often unavailable in real time. Participatory surveillance can capture community illness episodes, yet quantifying undiagnosed infections remains difficult.
Objective:
We assessed a Singaporean cohort to estimate medically unattended COVID-19 infections by combining symptom models with proxy epidemic indicators.
Methods:
We analysed 11 survey waves (Sep 2021–Nov 2022) from the SOCRATES community cohort (n=1899), spanning Delta and Omicron variant waves. Respondents reported recent illness, symptoms, healthcare use, and COVID-19 diagnoses. Multilevel logistic regression of medically attended episodes estimated the probability of COVID-19 in unattended episodes, incorporating symptoms and external indicators -- wastewater viral-load index (WVI) and healthcare staff surveillance. Estimates of total infections and medically attended fractions were validated against independent serological survey results.
Results:
Among 2284 illness episodes, 756 were diagnosed COVID-19, of which 62.4% were medically attended. Healthcare-seeking declined from ~80% of COVID-19 episodes early in 2022 to <60% by late 2022. Symptom severity differentiated healthcare-seeking in non-COVID episodes, but profiles were similar between attended and unattended COVID-19 episodes. Regression models incorporating WVI achieved close alignment with national notifications and serological estimates, with deviation from serology <7% across variant intervals. Total infection rates were estimated at 1.0×, 1.8×, 2.3× and 2.9× the rate of notified cases during Delta, Omicron BA.1/BA.2, BA.5 and XBB waves, respectively.
Conclusions:
A participatory syndromic surveillance cohort, linked analytically with proxy epidemic indicators, can provide timely, robust estimates of infection burden that supplement traditional notification and serological surveillance. This approach accommodates shifts in healthcare-seeking and testing behaviour, offering a scalable, resource-efficient model for epidemic monitoring beyond COVID-19.
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