A Bayesian System to Detect and Track Outbreaks of Influenza-Like Illnesses Including Novel Diseases: Algorithm Development and Validation

Abstract

Background:The early identification of outbreaks of both known and novel influenza-like illnesses is an important public health problem. Objective:The design and testing of a tool that detects and tracks outbreaks of both known and novel influenza-like illness, such as COVID-19, accurately and early.Methods: This paper describes the ILI Tracker algorithm that first models the daily occurrence of a set of known influenza-like illnesses in a hospital emergency department using findings extracted from patient care reports using natural language processing. We then show how the algorithm can be extended to detect and track the presence of an unmodeled disease which may represent a novel disease outbreak. Results:We include results based on modeling the diseases influenza, respiratory syncytial virus, human metapneumovirus, and parainfluenza for five emergency departments in Allegheny County Pennsylvania from June 1, 2010 through May 31, 2015. We also include the results of detecting the likely outbreak of an unmodeled disease during the mentioned time period, which in retrospect was very likely an outbreak of Enterovirus D68. Conclusions:The results reported in this paper provide support that ILI Tracker was able to track well four modeled ILI-like diseases over a one-year period, relative to laboratory confirmed cases, and it was computationally efficient in doing so. The system was also able to detect a likely novel outbreak of EV-D68 early in an outbreak that occurred in Allegheny County in 2014, as well as clinically characterize that outbreak disease accurately.