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- Title
Predicting Unobserved Exposures from Seasonal Epidemic Data.
- Authors
Forgoston, Eric; Schwartz, Ira B.
- 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.
- Subjects
EPIDEMIOLOGICAL models; SEASONAL affective disorder; NONLINEAR analysis; STOCHASTIC analysis; DETERMINISTIC processes; BIOMATHEMATICS; HIGH-dimensional model representation
- Publication
Bulletin of Mathematical Biology, 2013, Vol 75, Issue 9, p1450
- ISSN
0092-8240
- Publication type
Article
- DOI
10.1007/s11538-013-9855-0