Improving Outbreak Detection by Signal Integration

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Improving Outbreak Detection by Signal Integration*

Paola Sebastiani,^1,2 L. Wang,^1,2 K.D. Mandl,^2,3 M. Ramoni^2,3

1Boston University, Boston, Massachusetts; ²Center for Biopreparedness at Children's Hospital, Boston, Massachusetts; ³Harvard Medical School, Boston, Massachusetts

Corresponding author: Paola Sebastiani, Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Boston MA 02118. Telephone: 617-638-5877; Fax: 617-638-6484; E-mail: sebas@bu.edu.

Abstract

Introduction: A critical problem of surveillance systems is the trade-off between true and false detections. Integration of different monitors and information from exogenous sources can increase the true-detection rate by limiting the false-detection rate.

Objective: The authors introduce a probabilistic architecture able to achieve a substantial detection rate while keeping false detections low.

Methods: The architecture is a Bayesian network that encodes probabilistic information through a directed graph. The nodes and arrows represent variables and stochastic dependencies quantified by probability distributions. The integration of two systems for syndromic surveillance at a pediatric and adult hospital is illustrated by using a respiratory illness outbreak (Figure). Empirical evaluations have demonstrated that true and false-alert rates are affected by influenza epidemics, by air quality as measured by pollen level, and by whether the alert day is a holiday. The network integrates the sources of information to compute the probability of an outbreak (given that one or both systems generate alerts) and what is known about the other variables. The probability tables quantifying the network were obtained from data contaminated with different simulated outbreaks. The integrator was validated on 84 simulated outbreaks.

Results and Conclusions: This study indicates that the integration of the two monitoring systems with exogenous information has a 73% true-detection rate with an 8% false-detection rate in limited outbreaks (i.e., an average of four ill persons/day), and 97% true-detection rate with 10% false-detection rate in more substantial outbreaks (i.e., an average of eight ill persons/day).

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