Real-time surveillance for abnormal events: the case of influenza outbreaks

Rao, Yao ORCID: 0000-0003-1341-3456 and McCabe, Brendan
(2016) Real-time surveillance for abnormal events: the case of influenza outbreaks. STATISTICS IN MEDICINE, 35 (13). 2206 - 2220.

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This paper introduces a method of surveillance using deviations from probabilistic forecasts. Realised observations are compared with probabilistic forecasts, and the “deviation” metric is based on low probability events. If an alert is declared, the algorithm continues to monitor until an all‐clear is announced. Specifically, this article addresses the problem of syndromic surveillance for influenza (flu) with the intention of detecting outbreaks, due to new strains of viruses, over and above the normal seasonal pattern. The syndrome is hospital admissions for flu‐like illness, and hence, the data are low counts. In accordance with the count properties of the observations, an integer‐valued autoregressive process is used to model flu occurrences. Monte Carlo evidence suggests the method works well in stylised but somewhat realistic situations. An application to real flu data indicates that the ideas may have promise. The model estimated on a short run of training data did not declare false alarms when used with new observations deemed in control, ex post. The model easily detected the 2009 H 1N 1 outbreak. Copyright © 2016 John Wiley & Sons, Ltd.

Item Type: Article
Uncontrolled Keywords: real-time surveillance, early event detection, probability forecasts, Markov process, integer autoregressive model
Depositing User: Symplectic Admin
Date Deposited: 27 Apr 2016 15:32
Last Modified: 19 Jan 2022 08:15
DOI: 10.1002/sim.6857
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