Using network theory to identify disease outbreaks of unknown etiology
نویسندگان
چکیده
The identification of undiagnosed disease outbreaks is critical for mobilizing efforts to prevent widespread transmission of novel virulent pathogens. Recent developments in online surveillance systems allow for the rapid communication of the earliest reports of emerging infectious diseases and tracking of their spread. The efficacy of these programs, however, is inhibited by the anecdotal nature of informal reporting and uncertainty of pathogen identity in the early stages of emergence. We developed theory to connect disease outbreaks of known etiology in a network using an array of properties including symptoms, seasonality and case-fatality ratio. We tested the method with 125 reports of outbreaks of ten known infectious diseases causing encephalitis in South Asia, and showed that different diseases frequently form distinct clusters within the networks and the approach can correctly identify unknown disease outbreaks with an average sensitivity of 76% and specificity of 88%. Outbreaks of some diseases, such as Nipah virus encephalitis, were well identified (sensitivity = 100%, PPV = 80%), whereas others (e.g., Chandipura encephalitis) were more difficult to distinguish. These results suggest that unknown outbreaks in resource-poor settings could be evaluated in real-time, potentially leading to more rapid responses and reducing the risk of an outbreak becoming a pandemic.
منابع مشابه
Using network theory to identify the causes of disease outbreaks of unknown origin.
The identification of undiagnosed disease outbreaks is critical for mobilizing efforts to prevent widespread transmission of novel virulent pathogens. Recent developments in online surveillance systems allow for the rapid communication of the earliest reports of emerging infectious diseases and tracking of their spread. The efficacy of these programs, however, is inhibited by the anecdotal natu...
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