Document Type
Article
Publication Date
8-7-2015
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.
DOI
10.1007/s11538-015-0091-7
MSU Digital Commons Citation
Forgoston, Eric; Shaw, Leah B.; and Schwartz, Ira B., "A Framework for Inferring Unobserved Multistrain Epidemic Subpopulations Using Synchronization Dynamics" (2015). Department of Applied Mathematics and Statistics Faculty Scholarship and Creative Works. 1.
https://digitalcommons.montclair.edu/appliedmath-stats-facpubs/1
Published Citation
Forgoston, E., Shaw, L. B., & Schwartz, I. B. (2015). A Framework for Inferring Unobserved Multistrain Epidemic Subpopulations Using Synchronization Dynamics. Bulletin of mathematical biology, 77(7), 1437-1455.