  Understanding the dynamics of infectious disease spread in a heterogeneous
population is an important factor in designing control strategies. Here, we
develop a novel tensor-driven multi-compartment version of the classic
Susceptible-Infected-Recovered (SIR) model and apply it to Internet data to
reveal information about the complex spatial structure of disease spread. The
model is used to analyze state-level Google search data from the US pertaining
to two viruses, Respiratory Syncytial Virus (RSV), and West Nile Virus (WNV).
We fit the data with correlations of $R^2=0.70$, and $0.52$ for RSV and WNV,
respectively. Although no prior assumptions on spatial structure are made,
human movement patterns in the US explain 27-30\% of the estimated inter-state
transmission rates. The transmission rates within states are correlated with
known demographic indicators, such as population density and average age.
Finally, we show that the patterns of disease load for subsequent seasons can
be predicted using the model parameters estimated for previous seasons and as
few as $7$ weeks of data from the current season. Our results are applicable to
other countries and similar viruses, allowing the identification of disease
spread parameters and prediction of disease load for seasonal viruses earlier
in season.
