Bayesian tracking of emerging epidemics using ensemble optimal statistical interpolation

Spat Spatiotemporal Epidemiol. 2014 Jul:10:39-48. doi: 10.1016/j.sste.2014.06.004. Epub 2014 Jul 9.

Abstract

We present a preliminary test of the Ensemble Optimal Statistical Interpolation (EnOSI) method for the statistical tracking of an emerging epidemic, with a comparison to its popular relative for Bayesian data assimilation, the Ensemble Kalman Filter (EnKF). The spatial data for this test was generated by a spatial susceptible-infectious-removed (S-I-R) epidemic model of an airborne infectious disease. Both tracking methods in this test employed Poisson rather than Gaussian noise, so as to handle epidemic data more accurately. The EnOSI and EnKF tracking methods worked well on the main body of the simulated spatial epidemic, but the EnOSI was able to detect and track a distant secondary focus of infection that the EnKF missed entirely.

Keywords: Bayesian statistical tracking; Data assimilation; Emerging epidemics; Ensemble Kalman filter; Optimal statistical interpolation; Spatial S-I-R epidemic model.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Bayes Theorem
  • Communicable Diseases, Emerging / epidemiology*
  • Communicable Diseases, Emerging / prevention & control
  • Environmental Monitoring / statistics & numerical data*
  • Epidemics*
  • Global Health
  • Humans
  • Spatial Analysis*