  Collaboration among multiple teams has played a major role in probabilistic
forecasting events of influenza outbreaks, the COVID-19 pandemic, other disease
outbreaks, and in many other fields. When collecting forecasts from individual
teams, ensuring that each team's model represents forecast uncertainty
according to the same format allows for direct comparison of forecasts as well
as methods of constructing multi-model ensemble forecasts. This paper outlines
several common probabilistic forecast representation formats including
parametric distributions, sample distributions, bin distributions, and
quantiles and compares their use in the context of collaborative projects. We
propose the use of a discrete mixture distribution format in collaborative
forecasting in place of other formats. The flexibility in distribution shape,
the ease for scoring and building ensemble models, and the reasonably low level
of computer storage required to store such a forecast make the discrete mixture
distribution an attractive alternative to the other representation formats.
