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 Citation (Scopus)

Abstract

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
Volume77
Issue number7
DOIs
StatePublished - 7 Aug 2015

Fingerprint

dengue
Dengue
subpopulation
Coinfection
Synchronization
Infection
dengue fever
Population
Driver
time series analysis
Center Manifold
Time series
Epidemic Model
methodology
infection
Lyapunov Exponent
time series
Deduce
Predict
simulation

Keywords

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

Cite this

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A Framework for Inferring Unobserved Multistrain Epidemic Subpopulations Using Synchronization Dynamics. / Forgoston, Eric; Shaw, Leah B.; Schwartz, Ira B.

In: Bulletin of Mathematical Biology, Vol. 77, No. 7, 07.08.2015, p. 1437-1455.

Research output: Contribution to journalArticle

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