Abstract
Background: The outbreak of COVID-19 in 2019 has rapidly swept the world, causing irreparable loss to human beings. The pandemic has shown that there is still a delay in the early response to disease outbreaks and needs a method for unknown disease outbreak detection. The study's objective is to establish a new medical knowledge representation and reasoning model, and use the model to explore the feasibility of unknown disease outbreak detection.Methods: The study defined abnormal values with diagnostic significances from clinical data as the Features, and defined the Features as the antecedents of inference rules to match with knowledge bases, achieved in detecting known or emerging infectious disease outbreaks. Meanwhile, the study built a syndromic surveillance base to capture the target cases' Features to improve the reliability and fault-tolerant ability of the system.Results: The study combined the method with Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and early COVID-19 outbreaks as empirical studies. The results showed that with suitable surveillance guidelines, the method proposed in this study was capable to detect outbreaks of SARS, MERS, and early COVID-19 pandemics. The quick matching accuracies of confirmed infection cases were 89.1, 26.3–98%, and 82%, and the syndromic surveillance base would capture the Features of the remaining cases to ensure the overall detection accuracies. Based on the early COVID-19 data in Wuhan, this study estimated that the median time of the early COVID-19 cases from illness onset to local authorities' responses could be reduced to 7.0–10.0 days.Conclusions: This study offers a new solution to transfer traditional medical knowledge into structured data and form diagnosis rules, enables the representation of doctors' logistic thinking and the knowledge transmission among different users. The results of empirical studies demonstrate that by constantly inputting medical knowledge into the system, the proposed method will be capable to detect unknown diseases from existing ones and perform an early response to the initial outbreaks.
Highlights
In December 2019, a series of pneumonia cases with unknown etiologies appeared in Wuhan, Hubei province, China
This study used the surveillance guideline for pneumonia with uncertain etiologies in China to evaluate the feasibility of the proposed method
This study suggested that the guideline to some extent can be used to detect certain infectious disease outbreaks, mainly with respiratory syndromes, such as Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and COVID-19
Summary
In December 2019, a series of pneumonia cases with unknown etiologies appeared in Wuhan, Hubei province, China. A disease outbreak may start with a single infectious case that has not been presented for an extended period or caused by unknown agents (e.g., bacterium or virus) in the community or region, or the presence of a previously unknown disease (4). Syndromic surveillance collects data based primarily on non-specific health indicators and non-clinical indicators (5), and analyzes the timespace distortion of these data to achieve early detection and rapid response ability of public health events (6). The surveillance objects are relatively single, unable to detect newly emerging outbreaks with previously unseen patterns of symptoms or other unexpected events of relevance to public health (8). Relying only on syndromic surveillance is unable to detect unknown disease outbreaks, leading to the lag of response toward public health events (10). The study’s objective is to establish a new medical knowledge representation and reasoning model, and use the model to explore the feasibility of unknown disease outbreak detection
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