Prediction markets as a collective expectation signal for disease surveillance

Public health increasingly uses unofficial signals such as rumors, media, search, and self-reported symptoms to detect emergencies before official counts. Commercial prediction markets, where people bet on disease events, are a candidate new source, built for forecasting; but the expectation they price is itself a further unofficial signal. Their use as a public health signal is unvalidated in this domain and ethically contested: no study has yet shown that these prices anticipate official indicators, and whether public health should read bets on disease at all is disputed. A 2026 study judged them poor forecasters, but accuracy and surveillance value differ. Using thirteen Kalshi markets, we describe what such a reading would involve (a watchlist, an implied distribution, and fast repricing) as a collective expectation signal adjacent to participatory surveillance, and we set out the validation and governance agenda that would have to precede any use.

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Publication Details

Journal
npj Digital Public Health
Published
2026-10-01
DOI
https://doi.org/10.1038/s44482-026-00042-6
Primary Topic
Data-Driven Disease Surveillance
Type
article
Field-Weighted Citation Impact
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article

Prediction markets as a collective expectation signal for disease surveillance

Onicio Batista Leal Neto
npj Digital Public Health
Data-Driven Disease Surveillance
article

Prediction markets as a collective expectation signal for disease surveillance

Onicio Batista Leal Neto
article en

Abstract

Public health increasingly uses unofficial signals such as rumors, media, search, and self-reported symptoms to detect emergencies before official counts. Commercial prediction markets, where people bet on disease events, are a candidate new source, built for forecasting; but the expectation they price is itself a further unofficial signal. Their use as a public health signal is unvalidated in this domain and ethically contested: no study has yet shown that these prices anticipate official indicators, and whether public health should read bets on disease at all is disputed. A 2026 study judged them poor forecasters, but accuracy and surveillance value differ. Using thirteen Kalshi markets, we describe what such a reading would involve (a watchlist, an implied distribution, and fast repricing) as a collective expectation signal adjacent to participatory surveillance, and we set out the validation and governance agenda that would have to precede any use.

npj Digital Public HealthVol. 1(1)
University of Arizona (US)
Quality Education
Openalex Percentile: Top 11%
Data-Driven Disease Surveillance
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Prediction markets as a collective expectation signal for disease surveillance — Onicio Batista Leal Neto · npj Digital Public Health (2026) | TGRS Research Map | TGRS