  As an emergent infectious disease outbreak unfolds, public health response is
reliant on information on key epidemiological quantities, such as transmission
potential and serial interval. Increasingly, transmission models fit to
incidence data are used to estimate these parameters and guide policy. Some
widely-used modeling practices lead to potentially large errors in parameter
estimates and, consequently, errors in model-based forecasts. Even more
worryingly, in such situations, confidence in parameter estimates and forecasts
can itself be far over-estimated, leading to the potential for large errors
that mask their own presence. Fortunately, straightforward and computationally
inexpensive alternatives exist that avoid these problems. Here, we first use a
simulation study to demonstrate potential pitfalls of the standard practice of
fitting deterministic models to cumulative incidence data. Next, we demonstrate
an alternative based on stochastic models fit to raw data from an early phase
of 2014 West Africa Ebola Virus Disease outbreak. We show not only that bias is
thereby reduced, but that uncertainty in estimates and forecasts is better
quantified and that, critically, lack of model fit is more readily diagnosed.
We conclude with a short list of principles to guide the modeling response to
future infectious disease outbreaks.
