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Combining syndromic surveillance and ILI data using particle filter for epidemic state estimation


Designing effective mitigation strategies against influenza outbreak requires an accurate prediction of a disease’s future course of spreading. Real time information such as syndromic surveillance data and influenza-like-illness (ILI) reports by clinicians can be used to generate estimates of the current state of spreading of a disease. Syndromic surveillance data are immediately available, in contrast to ILI reports that require data collection and processing. On the other hand, they are less credible than ILI data because they are essentially behavioral responses from a community. In this paper, we present a method to combine immediately-available-but-less-reliable syndromic surveillance data with reliable-but-time-delayed ILI data. This problem is formulated as a non-linear stochastic filtering problem, and solved by a particle filtering method. Our experimental results from hypothetical pandemic scenarios show that state estimation is improved by utilizing both sets of data compared to when using only one set. However, the amount of improvement depends on the relative credibility and length of delay in ILI data. An analysis for a linear, Gaussian case is presented to support the results observed in the experiments.

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    We also tested other values for \(\sigma _q\) and \(\sigma _\gamma\) and find the results were qualitatively similar. Result data for the additional tests will be provided upon request.

  2. 2.

    In the context of epidemic state estimation, this assumption may not be required since storing the entire history of particles is most likely feasible: (1) measurement sampling frequency is in the order of day or week, and thus the size of measurement data is not huge, and (2) epidemic state estimation does not require real-time computation.

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    Slight variations visible in the figures are due to non-systematic causes.


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This research was supported by the Public Welfare & Safety Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (No.2011-0029881) and by Basic Science Research Program through NRF funded by the Ministry of Education (NRF-2010-0025224).

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Correspondence to Hayong Shin.

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Lee, T., Shin, H. Combining syndromic surveillance and ILI data using particle filter for epidemic state estimation. Flex Serv Manuf J 28, 233–253 (2016).

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  • Epidemic
  • Syndromic surveillance
  • Particle filter
  • Data fusion