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Predicting meningococcal disease outbreaks in structured populations.

J Ranta1, P H Mäkelä, E Arjas

  • 1Rolf Nevanlinna Institute, University of Helsinki, P.O. Box 4, FIN-00014, Finland. jukka.ranta@rni.helsinki.fi

Statistics in Medicine
|March 18, 2004
PubMed
Summary

Accurate prediction of meningococcal disease outbreaks is challenging. Utilizing data on asymptomatic carriers alongside disease cases significantly improves outbreak predictions in closed populations.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Predicting the natural progression of meningococcal epidemics is crucial for intervention decisions.
  • Early-stage outbreak predictions are often unreliable, hindering timely control measures.

Purpose of the Study:

  • To develop and assess an adaptive stochastic model for predicting meningococcal disease outbreaks in closed populations.
  • To evaluate the impact of incorporating asymptomatic carrier data on prediction accuracy.

Main Methods:

  • A stochastic discrete time epidemic model was applied to simulated meningococcal epidemics in a garrison setting.
  • Predictions of disease cases, carrier numbers, and new infections were computed.
  • The model's performance was assessed using data from simulated outbreaks with varying characteristics.

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Main Results:

  • Predictions based solely on observed disease cases were found to be inaccurate.
  • Incorporating temporal observations of asymptomatic carriers alongside disease data significantly improved prediction accuracy.
  • Sampling 15% of all units provided substantial improvement compared to full sampling of only some units.

Conclusions:

  • Adaptive modeling incorporating asymptomatic carrier data enhances meningococcal outbreak prediction in closed populations.
  • Strategic sampling of carriers across units is more effective than intensive sampling within limited units.
  • Markov chain Monte Carlo methods are essential for fully leveraging this data.