Using network theory to identify the causes of disease outbreaks of unknown origin

作者：Bogich Tiffany L^*; Funk Sebastian; Malcolm Trent R; Chhun Nok; Epstein Jonathan H; Chmura Aleksei A; Kilpatrick A Marm; Brownstein John S; Hutchison O Clyde; Doyle Capitman Catherine; Deaville Robert; Morse Stephen S; Cunningham Andrew A; Daszak Peter

来源：Journal of the Royal Society Interface, 2013, 10(81): 20120904.

DOI：10.1098/rsif.2012.0904

• emerging infectious disease
• encephalitis
• complex networks
• South Asia
• cluster analysis
• early warning systems

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 aetiology 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 10 known infectious diseases causing encephalitis in South Asia, and showed that different diseases frequently form distinct clusters within the networks. The approach correctly identified unknown disease outbreaks with an average sensitivity of 76 per cent and specificity of 88 per cent. Outbreaks of some diseases, such as Nipah virus encephalitis, were well identified (sensitivity = 100%, positive predictive values = 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.

• 出版日期2013-4-6