  Forecasting new cases, hospitalizations, and disease-induced deaths is an
important part of infectious disease surveillance and helps guide health
officials in implementing effective countermeasures. For disease surveillance
in the U.S., the Centers for Disease Control and Prevention (CDC) combine more
than 65 individual forecasts of these numbers in an ensemble forecast at
national and state levels. We collected data on CDC ensemble forecasts of
COVID-19 fatalities in the United States, and compare them with easily
interpretable ``Euler'' forecasts serving as a model-free benchmark that is
only based on the local rate of change of the incidence curve. The term ``Euler
method'' is motivated by the eponymous numerical integration scheme that
calculates the value of a function at a future time step based on the current
rate of change. Our results show that CDC ensemble forecasts are not more
accurate than ``Euler'' forecasts on short-term forecasting horizons of one
week. However, CDC ensemble forecasts show a better performance on longer
forecasting horizons. Using the current rate of change in incidences as
estimates of future incidence changes is useful for epidemic forecasting on
short time horizons. An advantage of the proposed method over other forecasting
approaches is that it can be implemented with a very limited amount of work and
without relying on additional data (e.g., human mobility and contact patterns)
and high-performance computing systems.
