  Mathematical models of epidemiological systems enable investigation of and
predictions about potential disease outbreaks. However, commonly used models
are often highly simplified representations of incredibly complex systems.
Because of these simplifications, the model output, of say new cases of a
disease over time, or when an epidemic will occur, may be inconsistent with
available data. In this case, we must improve the model, especially if we plan
to make decisions based on it that could affect human health and safety, but
direct improvements are often beyond our reach. In this work, we explore this
problem through a case study of the Zika outbreak in Brazil in 2016. We propose
an embedded discrepancy operator---a modification to the model equations that
requires modest information about the system and is calibrated by all relevant
data. We show that the new enriched model demonstrates greatly increased
consistency with real data. Moreover, the method is general enough to easily
apply to many other mathematical models in epidemiology.
