Document Type
Article
Publication Date
9-1-2013
Journal / Book Title
Bulletin of mathematical biology
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.
DOI
10.1007/s11538-013-9855-0
MSU Digital Commons Citation
Forgoston, Eric and Schwartz, Ira B., "Predicting Unobserved Exposures from Seasonal Epidemic Data" (2013). Department of Applied Mathematics and Statistics Faculty Scholarship and Creative Works. 105.
https://digitalcommons.montclair.edu/appliedmath-stats-facpubs/105
Published Citation
Forgoston, E., & Schwartz, I. B. (2013). Predicting unobserved exposures from seasonal epidemic data. Bulletin of mathematical biology, 75(9), 1450-1471.