About: An Aberration Detection-Based Approach for Sentinel Syndromic Surveillance of COVID-19 and Other Novel Influenza-Like Illnesses

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	An Aberration Detection-Based Approach for Sentinel Syndromic Surveillance of COVID-19 and Other Novel Influenza-Like Illnesses
Creator	Huang, Ming Liu, Hongfang Liu, Sijia Shen, Feichen Wang, Yanshan Fan, Jungwei Fu, Sunyang He, Huan Kaggal, Vinod Kugel, Jacob Sohn, Sunghwan Wang, Liwei Wen, Andrew
Source	MedRxiv; Medline; PMC
abstract	Coronavirus Disease 2019 (COVID-19) has emerged as a significant global concern, triggering harsh public health restrictions in a successful bid to curb its exponential growth. As discussion shifts towards relaxation of these restrictions, there is significant concern of second-wave resurgence. The key to managing these outbreaks is early detection and intervention, and yet there is significant lag time associated with usage of laboratory confirmed cases for surveillance purposes. To address this, syndromic surveillance can be considered to provide a timelier alternative for first-line screening. Existing syndromic surveillance solutions are however typically focused around a known disease and have limited capability to distinguish between outbreaks of individual diseases sharing similar syndromes. This poses a challenge for surveillance of COVID-19 as its active periods are tend to overlap temporally with other influenza-like illnesses. In this study we explore performing sentinel syndromic surveillance for COVID-19 and other influenza-like illnesses using a deep learning-based approach. Our methods are based on aberration detection utilizing autoencoders that leverages symptom prevalence distributions to distinguish outbreaks of two ongoing diseases that share similar syndromes, even if they occur concurrently. We first demonstrate that this approach works for detection of outbreaks of influenza, which has known temporal boundaries. We then demonstrate that the autoencoder can be trained to not alert on known and well-managed influenza-like illnesses such as the common cold and influenza. Finally, we applied our approach to 2019–2020 data in the context of a COVID-19 syndromic surveillance task to demonstrate how implementation of such a system could have provided early warning of an outbreak of a novel influenza-like illness that did not match the symptom prevalence profile of influenza and other known influenza-like illnesses.
has issue date	2020-06-09(xsd:dateTime)
bibo:doi	10.1101/2020.06.08.20124990
bibo:pmid	32577704
has license	cc-by-nc-nd
sha1sum (hex)	0c29abae315fac291c6d5d9eed0620d4c4b0af3d
schema:url	https://doi.org/10.1101/2020.06.08.20124990
resource representing a document's title	An Aberration Detection-Based Approach for Sentinel Syndromic Surveillance of COVID-19 and Other Novel Influenza-Like Illnesses
has PubMed Central identifier	PMC7302403
has PubMed identifier	32577704
schema:publication	medRxiv
resource representing a document's body	covid:0c29abae315fac291c6d5d9eed0620d4c4b0af3d#body_text
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