MATRICS: A System for Human-Machine Hybrid Forecasting of Geopolitical Events

Huber, David; Stepp, Nigel; Jammalamadaka, Aruna; Kim, Tiffany; Johnson, Samuel; Warmsley, Dana; Lu, Tsai-Ching

doi:10.1109/bigdata47090.2019.9006134

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…In recent years, a complementary approach to model-driven epidemiological forecasting has emerged in the form of human judgment. Building upon the success of ‘wisdom-of-crowds’ approaches in other contexts [9, 10, 11], crowdsourced efforts for forecasting infectious disease outbreaks have been developed and have found reasonable success and adoption. Examples of early work in this area include [12, 13, 14].…”

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confidence: 99%

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

Venkatramanan

Cambeiro²,

Liptay³

et al. 2022

Preprint

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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.

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confidence: 99%