  Auxiliary data sources have become increasingly important in epidemiological
surveillance, as they are often available at a finer spatial and temporal
resolution, larger coverage, and lower latency than traditional surveillance
signals. We describe the problem of spatial and temporal heterogeneity in these
signals derived from these data sources, where spatial and/or temporal biases
are present. We present a method to use a ``guiding'' signal to correct for
these biases and produce a more reliable signal that can be used for modeling
and forecasting. The method assumes that the heterogeneity can be approximated
by a low-rank matrix and that the temporal heterogeneity is smooth over time.
We also present a hyperparameter selection algorithm to choose the parameters
representing the matrix rank and degree of temporal smoothness of the
corrections. In the absence of ground truth, we use maps and plots to argue
that this method does indeed reduce heterogeneity. Reducing heterogeneity from
auxiliary data sources greatly increases their utility in modeling and
forecasting epidemics.
