  Forecasts of the trajectory of an infectious agent can help guide public
health decision making. A traditional approach to forecasting fits a
computational model to structured data and generates a predictive distribution.
However, human judgment has access to the same data as computational models
plus experience, intuition, and subjective data. We propose a chimeric ensemble
-- a combination of computational and human judgment forecasts -- as a novel
approach to predicting the trajectory of an infectious agent. Each month from
January, 2021 to June, 2021 we asked two generalist crowds, using the same
criteria as the COVID-19 Forecast Hub, to submit a predictive distribution over
incident cases and deaths at the US national level either two or three weeks
into the future and combined these human judgment forecasts with forecasts from
computational models submitted to the COVID-19 Forecasthub into a chimeric
ensemble. We find a chimeric ensemble compared to an ensemble including only
computational models improves predictions of incident cases and shows similar
performance for predictions of incident deaths. A chimeric ensemble is a
flexible, supportive public health tool and shows promising results for
predictions of the spread of an infectious agent.
