Ronan Kelly
Retired 2020
Using network theory to identify the causes of disease outbreaks of unknown origin
Tiffany L. Bogich1,2,3,†⇓, Sebastian Funk3,4,5,†⇓, Trent R. Malcolm1, Nok Chhun1, Jonathan H. Epstein1, Aleksei A. Chmura1, A. Marm Kilpatrick6, John S. Brownstein7, O. Clyde Hutchison4, Catherine Doyle-Capitman1,8, Robert Deaville4, Stephen S. Morse9, Andrew A. Cunningham4 and Peter Daszak1⇓
+ Author Affiliations
1EcoHealth Alliance, 460 West 34th Street, 17th Floor, New York, NY 10001, USA
2Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
3Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
4Institute of Zoology, Zoological Society of London, Regent's Park, London NW1 4RY, UK
5London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
6Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA 95064, USA
7Childrens’ Hospital Boston, Harvard University, Boston, MA 02115, USA
8Department of Mammalogy, American Museum of Natural History, Central Park West, 79th Street, New York, NY 10024, USA
9Department of Epidemiology, Columbia University, Mailman School of Public Health, 722 West 168th Street, New York, NY 10032, USA
e-mail: tbogich@princeton.edu
e-mail: sf7@princeton.edu
e-mail: daszak@ecohealthalliance.org
↵† These authors contributed equally to this study and are listed alphabetically.
Abstract
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.
emerging infectious disease encephalitis complex networks South Asia cluster analysis early warning systems
Footnotes
Received November 5, 2012.
Accepted January 15, 2013.
© 2013 The Author(s) Published by the Royal Society. All rights reserved.
http://rsif.royalsocietypublishing.org/content/10/81/20120904.short
Tiffany L. Bogich1,2,3,†⇓, Sebastian Funk3,4,5,†⇓, Trent R. Malcolm1, Nok Chhun1, Jonathan H. Epstein1, Aleksei A. Chmura1, A. Marm Kilpatrick6, John S. Brownstein7, O. Clyde Hutchison4, Catherine Doyle-Capitman1,8, Robert Deaville4, Stephen S. Morse9, Andrew A. Cunningham4 and Peter Daszak1⇓
+ Author Affiliations
1EcoHealth Alliance, 460 West 34th Street, 17th Floor, New York, NY 10001, USA
2Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
3Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
4Institute of Zoology, Zoological Society of London, Regent's Park, London NW1 4RY, UK
5London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
6Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA 95064, USA
7Childrens’ Hospital Boston, Harvard University, Boston, MA 02115, USA
8Department of Mammalogy, American Museum of Natural History, Central Park West, 79th Street, New York, NY 10024, USA
9Department of Epidemiology, Columbia University, Mailman School of Public Health, 722 West 168th Street, New York, NY 10032, USA
e-mail: tbogich@princeton.edu
e-mail: sf7@princeton.edu
e-mail: daszak@ecohealthalliance.org
↵† These authors contributed equally to this study and are listed alphabetically.
Abstract
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.
emerging infectious disease encephalitis complex networks South Asia cluster analysis early warning systems
Footnotes
Received November 5, 2012.
Accepted January 15, 2013.
© 2013 The Author(s) Published by the Royal Society. All rights reserved.
http://rsif.royalsocietypublishing.org/content/10/81/20120904.short