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AI-DRAFTED — PHYSICIAN REVIEW IN PROGRESSThe citation for this entry was checked against Crossref and PubMed, but its summary and interpretation were drafted by an AI model from the published abstract and have not yet been individually read by a physician. Read the linked source before relying on this operationally.
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Predicting resource use at mass gatherings using a simplified stratification scoring model[1]

Hartman N, Williamson A, Sojka B, Alibertis K, et al · 2009 · The American Journal of Emergency Medicine

Summary

A retrospective analysis of 55 varied mass-gathering events near a large mid-Atlantic university scored each on weather, attendance, alcohol presence, participant demographic, and crowd intentions, stratifying them as minor, intermediate, or major. The 12 minor, 20 intermediate, and 23 major events averaged 2.3, 6.3, and 71 total patient contacts respectively, with consistent trends for transports. The scoring system correctly predicted resource demand across the three classes but described minor and intermediate events more accurately than major ones.

So what

A simple a-priori scoring system lets planners triage events into resource tiers before the gates open. Reliable for routine small-to-mid events; treat its estimates for large, high-risk events with caution and add margin.

Key findings

  • Score built from weather, attendance, alcohol, demographic, and crowd intentions.
  • Average contacts: minor 2.3, intermediate 6.3, major 71.
  • Correctly predicted resource demand across classes; least accurate for major events.

Caveats & model limits

  • Hartman (2009) — stratification score Retrospective, 55 events, single region; factors are weighted equally by design (authors note real-world weighting may vary case-by-case).
  • Hartman (2009) — stratification score Least accurate for major events (major events were less well described); the intermediate class is the most variable.
  • Hartman (2009) — stratification score Major-class means are dominated by ~60,000-attendance college football, so venues of that scale are unusually well matched to this dataset.
  • Hartman (2009) — stratification score Session 6: verified from Hartman 2009 AJEM PDF (BA). Methods-text cutoffs. Heat from WBGT, crowd age from demographics, crowd-intention proxied from crowd mobility.

Read the paper

Domains

PPR & Prediction