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Updated: Sep 13, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Detecting outbreaks using a spatial latent field.

Cosmin Safta1, Jaideep Ray1, Wyatt Bridgman1

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Summary
This summary is machine-generated.

This study introduces a novel spatial-temporal field method for estimating disease infection rates using Bayesian inference and Gaussian random fields. Incorporating neighboring region data improves accuracy, outperforming traditional case-count methods for epidemic wave detection.

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Area of Science:

  • Epidemiology
  • Spatial Statistics
  • Computational Statistics

Background:

  • Estimating disease infection rates across multiple regions presents challenges due to data variability.
  • Existing epidemiological models often focus on single areal units, limiting spatial analysis.
  • Accurate spatial-temporal disease surveillance is crucial for public health interventions.

Purpose of the Study:

  • To develop a robust method for estimating the spatial-temporal infection rate of a disease.
  • To extend single-unit epidemiological models to a multi-unit framework using Bayesian approaches.
  • To create an anomaly detection system for identifying new epidemic waves.

Main Methods:

  • Utilized time-series case-counts from multiple areal units.
  • Extended a single-unit epidemiological model to a multi-unit context.
  • Employed Bayesian inference with a Gaussian random field prior.
  • Applied adaptive Markov chain Monte Carlo for parameter sampling.
  • Validated the model using COVID-19 case data from New Mexico counties.

Main Results:

  • Spatial correlation between neighboring regions regularizes estimations, especially with high-variance data.
  • The calibrated model accurately forecasts infection rates across areal units.
  • An anomaly detector based on estimated infection rates significantly outperforms methods relying solely on case-counts.
  • Demonstrated improved epidemic wave detection capabilities.

Conclusions:

  • Integrating spatial dependencies enhances the reliability of infection rate estimations.
  • The proposed Bayesian framework provides a powerful tool for spatial-temporal disease surveillance.
  • The developed anomaly detection method offers a more sensitive approach to identifying emerging epidemic waves.