Mixture Likelihood Ratio Scan Statistic for Disease Outbreak Detection

TL;DR

This paper introduces a novel mixture likelihood ratio scan statistic for early disease outbreak detection, effectively reducing detection time and false positives by modeling outbreak profiles with a Poisson-based approach.

Contribution

It develops a new statistical method that leverages characteristic outbreak profiles to improve early detection and accuracy in disease surveillance.

Findings

Effective detection with early alerts in simulated outbreak data

Low false positive rate demonstrated across datasets

Applicable to various disease outbreak scenarios

Abstract

Early detection of disease outbreaks is of paramount importance to implementing intervention strategies to mitigate the severity and duration of the outbreak. We build methodology that utilizes the characteristic profile of disease outbreaks to reduce the time to detection and false positive rate. We model daily counts through a Poisson distribution with additive background plus outbreak components. The outbreak component has a parametric form with unknown underlying parameters. A mixture likelihood ratio scan statistic is developed to maximize parameters over a window in time. This provides an alert statistic with early time to detection and low false positive rate. The methodology is demonstrated on three simulated data sets meant to represent E. coli, Cryptosporidium, and Influenza outbreaks.