• Last updated: Wed, May 28, 2025Status: Ongoing
  • Shomaila Mazhar, Dominik Dietler, and Jonas Björk

Integrating Individual-level Syndromic Surveillance Data in Infectious Disease Modelling: A Framework for Identifying Undetected Infections

Epidemiological models are essential for providing decision-makers with insights into the potential future spread of SARS-CoV-2 under different policy scenarios. In this study, we aim to develop a mathematical model that uses syndromic surveillance data from the 1177 hotline to identify undetected (untested) cases in the population who report symptoms but are not tested. By incorporating individuals who may be missed by traditional testing-based models, the proposed model offers a more comprehensive view of disease transmission. The results will provide more timely forecasts for hospitalizations during the pandemic.

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