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Disease surveillance using a hidden Markov model.

Rochelle E Watkins¹, Serryn Eagleson, Bert Veenendaal

¹Curtin Health Innovation Research Institute, Curtin University of Technology, Perth, Australia. Rochelle.Watkins@curtin.edu.au

BMC Medical Informatics and Decision Making

|August 12, 2009

Summary

This summary is machine-generated.

This study developed a flexible hidden Markov model (HMM) for disease surveillance. The HMM effectively detects disease outbreaks in sparse data, especially at low false alarm rates, outperforming existing methods.

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Area of Science:

Public Health
Epidemiology
Biostatistics

Background:

Routine surveillance of disease notification data is crucial for early detection of localized outbreaks.
Hidden Markov models (HMMs) are suitable for disease surveillance data but underutilized in practice.
A need exists for a simple, flexible HMM for sparse, small-area count data with minimal baseline requirements.

Purpose of the Study:

To develop and evaluate a simple, flexible hidden Markov model (HMM) for disease surveillance.
To assess the HMM's suitability for sparse, small-area count data.
To compare the HMM's outbreak detection performance against established methods.

Main Methods:

A Bayesian HMM was designed to monitor notifiable disease data aggregated by residential postcode.
Semi-synthetic data were utilized for algorithm evaluation.
Performance was compared with Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum.

Main Results:

HMM performance varied with the desired false alarm rate.
At low false alarm rates (around 0.01), HMMs showed higher sensitivity and shorter detection times for larger outbreaks compared to cusum methods.
The 14-day HMM demonstrated a significantly greater area under the receiver operator characteristic curve than EARS C3 and 7-day negative binomial cusum.

Conclusions:

The HMM is an effective method for notifiable disease surveillance in sparse, small-area data at low false alarm rates.
Further research is needed to evaluate HMM performance across diverse diseases and surveillance contexts.