Utility of human judgment ensembles during times of pandemic uncertainty: A case study during the COVID-19 Omicron BA.1 wave in the USA

Responding to a rapidly evolving pandemic like COVID-19 is challenging, and involves anticipating novel variants, vaccine uptake, and behavioral adaptations. Human judgment systems can complement computational models by providing valuable real-time forecasts. We report findings from a study conducted on Metaculus, a community forecasting platform, in partnership with the Virginia Department of Health, involving six rounds of forecasting during the Omicron BA.1 wave in the United States from November 2021 to March 2022. We received 8355 probabilistic predictions from 129 unique users across 60 questions pertaining to cases, hospitalizations, vaccine uptake, and peak/trough activity. We observed that the case forecasts performed on par with national multi-model ensembles and the vaccine uptake forecasts were more robust and accurate compared to baseline models. We also identified qualitative shifts in Omicron BA.1 wave prognosis during the surge phase, demonstrating rapid adaptation of such systems. Finally, we found that community estimates of variant characteristics such as growth rate and timing of dominance were in line with the scientific consensus. The observed accuracy, timeliness, and scope of such systems demonstrates the value of incorporating them into pandemic policymaking workflows.

Aggregating probabilistic predictions of the safety, efficacy, and timing of a COVID-19 vaccine

Safe, efficacious vaccines were developed to reduce the transmission of SARS-CoV-2 during the COVID-19 pandemic. But in the middle of 2020, vaccine effectiveness, safety, and the timeline for when a vaccine would be approved and distributed to the public was uncertain. To support public health decision making, we solicited trained forecasters and experts in vaccinology and infectious disease to provide monthly probabilistic predictions from July to September of 2020 of the efficacy, safety, timing, and delivery of a COVID-19 vaccine. We found, that despite sparse historical data, a consensus--a combination of human judgment probabilistic predictions--can quantify the uncertainty in clinical significance and timing of a potential vaccine. The consensus underestimated how fast a therapy would show a survival benefit and the high efficacy of approved COVID-19 vaccines. However, the consensus did make an accurate prediction for when a vaccine would be approved by the FDA. Compared to individual forecasters, the consensus was consistently above the 50th percentile of the most accurate forecasts. A consensus is a fast and versatile method to build probabilistic predictions of a developing vaccine that is robust to poor individual predictions. Though experts and trained forecasters did underestimate the speed of development and the high efficacy of a SARS-CoV-2 vaccine, consensus predictions can improve situational awareness for public health officials and for the public make clearer the risks, rewards, and timing of a vaccine.