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Forecasts of the trajectory of an infectious agent can help guide publichealth decision making. A traditional approach to forecasting fits acomputational model to structured data and generates a predictive distribution.However, human judgment has access to the same data as computational modelsplus experience, intuition, and subjective data. We propose a chimeric ensemble-- a combination of computational and human judgment forecasts -- as a novelapproach to predicting the trajectory of an infectious agent. Each month fromJanuary, 2021 to June, 2021 we asked two generalist crowds, using the samecriteria as the COVID-19 Forecast Hub, to submit a predictive distribution overincident cases and deaths at the US national level either two or three weeksinto the future and combined these human judgment forecasts with forecasts fromcomputational models submitted to the COVID-19 Forecasthub into a chimericensemble. We find a chimeric ensemble compared to an ensemble including onlycomputational models improves predictions of incident cases and shows similarperformance for predictions of incident deaths. A chimeric ensemble is aflexible, supportive public health tool and shows promising results forpredictions of the spread of an infectious agent.

Chimeric forecasting: combining probabilistic predictions from computational models and human judgment