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A spatial epidemic model for disease spread over a heterogeneous spatial support.

Aaron T Porter1, Jacob J Oleson2

  • 1Department of Applied Mathematics and Statistics, Colorado School of Mines, 1500 Illinois St., Golden, CO 80401, U.S.A.

Statistics in Medicine
|September 15, 2015
PubMed
Summary

This study models the 2006 Iowa mumps epidemic using a spatial compartmental model. Spring break travel significantly increased disease transmission rates, highlighting the impact of population mixing on epidemic spread.

Keywords:
BayesianSEIRconditional autoregressivemumpsrank reduction

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Spatial Analysis

Background:

  • Mumps outbreaks pose public health challenges, requiring robust models for understanding transmission dynamics.
  • Analyzing epidemic data on a spatial lattice with sparse temporal counts necessitates advanced modeling approaches.
  • Lack of person-to-person contact data requires deriving contact structure from spatial relationships.

Purpose of the Study:

  • To develop and apply a spatial compartmental epidemic model (spatial PS SEIR) for analyzing the 2006 Iowa mumps epidemic.
  • To account for spatial heterogeneity in population mixing and non-exponentially distributed latent/infectious periods.
  • To assess the impact of population dispersal (spring break) and public awareness on mumps transmission.

Main Methods:

  • Utilized a spatial compartmental epidemic model with general latent time distributions (spatial PS SEIR).
  • Analyzed spatio-temporal data from the Iowa mumps epidemic collected on a spatial lattice.
  • Modeled contact structure using the spatial graph to address data sparsity and heterogeneity.

Main Results:

  • The spatial PS SEIR model effectively smoothed contact structure and accounted for spatial heterogeneity.
  • Spring break significantly increased the population mixing rate, accelerating disease spread.
  • Spatial transmission pathways were identified, revealing spread across multiple conduits.

Conclusions:

  • The spatial PS SEIR model is suitable for analyzing sparse spatio-temporal epidemic data.
  • Population mobility events, like spring break, critically influence epidemic trajectories.
  • Understanding spatial mixing is crucial for effective mumps epidemic control strategies.