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Collaboration among multiple teams has played a major role in probabilisticforecasting events of influenza outbreaks, the COVID-19 pandemic, other diseaseoutbreaks, and in many other fields. When collecting forecasts from individualteams, ensuring that each team's model represents forecast uncertaintyaccording to the same format allows for direct comparison of forecasts as wellas methods of constructing multi-model ensemble forecasts. This paper outlinesseveral common probabilistic forecast representation formats includingparametric distributions, sample distributions, bin distributions, andquantiles and compares their use in the context of collaborative projects. Wepropose the use of a discrete mixture distribution format in collaborativeforecasting in place of other formats. The flexibility in distribution shape,the ease for scoring and building ensemble models, and the reasonably low levelof computer storage required to store such a forecast make the discrete mixturedistribution an attractive alternative to the other representation formats.

Mixture distributions for probabilistic forecasts of disease outbreaks