Predicting Unobserved Exposures from Seasonal Epidemic Data

Eric Forgoston, Ira B. Schwartz

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

We consider a stochastic Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological model with a contact rate that fluctuates seasonally. Through the use of a nonlinear, stochastic projection, we are able to analytically determine the lower dimensional manifold on which the deterministic and stochastic dynamics correctly interact. Our method produces a low dimensional stochastic model that captures the same timing of disease outbreak and the same amplitude and phase of recurrent behavior seen in the high dimensional model. Given seasonal epidemic data consisting of the number of infectious individuals, our method enables a data-based model prediction of the number of unobserved exposed individuals over very long times.

Original languageEnglish
Pages (from-to)1450-1471
Number of pages22
JournalBulletin of Mathematical Biology
Volume75
Issue number9
DOIs
StatePublished - 1 Sep 2013

Fingerprint

Epidemiological Model
Stochastic Dynamics
Prediction Model
Disease Outbreaks
Stochastic Model
Timing
High-dimensional
Projection
Contact
Stochastic models
prediction
exposure
methodology
Model
method
rate

Keywords

  • Epidemics with seasonality and noise
  • Model reduction

Cite this

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Predicting Unobserved Exposures from Seasonal Epidemic Data. / Forgoston, Eric; Schwartz, Ira B.

In: Bulletin of Mathematical Biology, Vol. 75, No. 9, 01.09.2013, p. 1450-1471.

Research output: Contribution to journalArticle

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