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Aggregating statistical models and human judgment

Project Aim

This project aims to provide public health officials metaforecasts---a combination of probabilistic predictions from computational models, subject matter experts, and trained forecasters---of the COVID-19 outbreak, and an expert consensus of the most effective interventions to prevent the spread of SARS-CoV-2 and impact of COVID-19.

Available at this repository are metaforecasts of the COVID-19 outbreak and consensuses on effective interventions sent to the public and public health officials. This repository also contains software used to aggregate predictions.

Human judgment has the potential to reduce the COVID-19 burden on the US in two ways. Predictions from experts and trained forecasters combine objective data, available to computational models, and unstructured data, only available to humans, allowing public health officials access to a broader pool of data on the virus. Most computational models are restricted to making passive forecasts, predictions of an evolving target through time, but humans can make active forecasts of possibly effective interventions to guide public health strategy, policy, and decision making.

Reports on metaforecasts fo the US COVID-19 outbreak

Report Number Dates conducted PDF press
01 2021-01-06 to 2021-01-16 Summary Report 01 Lehigh News
02 2021-02-03 to 2021-02-15 Summary Report 02 Lehigh News
03 2021-03-04 to 2021-03-15 Summary Report 03
04 2021-04-07 to 2021-04-20 Summary Report 04
05 2021-05-05 to 2021-05-18 Summary Report 05
06 2021-06-02 to 2021-06-16 Summary Report 06

Reports on potentially effective interventions to guide public health decision making

Report Number Dates conducted PDF press
01 2021-01-07 to 2021-01-28 Summary Report 01
02 2021-02-12 to 2021-02-26 Summary Report 02

Data / Predictions

Data and predictions are available in the data folder and posted on the Harvard Dataverse at the foloowing URL =

Contact information

Questions and comments can be sent to thomas mcandrew (

Funding / Support

This research is supported through the MIDAS Coordination Center (MIDASNI2020- 1) by a grant from the National Institute of General Medical Science (3U24GM132013-02S2).


Shield: CC BY 4.0

This work is licensed under a Creative Commons Attribution 4.0 International License.

CC BY 4.0