A Framework for Inferring Unobserved Multistrain Epidemic Subpopulations Using Synchronization Dynamics

Eric Forgoston, Leah B. Shaw, Ira B. Schwartz

Research output: Contribution to journalArticle

1 Scopus citations


A new method is proposed to infer unobserved epidemic subpopulations by exploiting the synchronization properties of multistrain epidemic models. A model for dengue fever is driven by simulated data from secondary infective populations. Primary infective populations in the driven system synchronize to the correct values from the driver system. Most hospital cases of dengue are secondary infections, so this method provides a way to deduce unobserved primary infection levels. We derive center manifold equations that relate the driven system to the driver system and thus motivate the use of synchronization to predict unobserved primary infectives. Synchronization stability between primary and secondary infections is demonstrated through numerical measurements of conditional Lyapunov exponents and through time series simulations.

Original languageEnglish
Pages (from-to)1437-1455
Number of pages19
JournalBulletin of Mathematical Biology
Issue number7
Publication statusPublished - 7 Aug 2015



  • Center manifolds
  • Inferring unobserved populations
  • Multistrain disease models
  • Synchronization

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