  A new method is proposed to infer unobserved epidemic sub-populations 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.
