Syndromic Surveillance of Respiratory Disease in Free-Living Chimpanzees

• Type of Material: Article
• Authors: Tiffany M. Wolf, University of Minnesota; Randall S. Singer, University of Minnesota; Elizabeth V. Lonsdorf, Franklin and Marshall College; Richard Maclehose, University of Minnesota; Thomas R. Gillespie, Emory University; Iddi Lipende, Jane Goodall Institute; Jane Raphael, Tanzania National Parks; Karen Terio, University of Illinois; Carson Murray, George Washington University; Anne Pusey, Duke University; Beatrice H. Hahn, University of Pennsylvania; Shadrack Kamenya, Jane Goodall Institute; Deus Mjungu, Jane Goodall Institute; Dominic A. Travis, University of Minnesota
• Language: English
• Date: 2019-06-15
• Publisher: Springer (part of Springer Nature): Springer Open Choice Hybrid Journals
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2019, EcoHealth Alliance.
• Final Published Version (URL): http://dx.doi.org/10.1007/s10393-019-01400-y
• Title of Journal or Parent Work: EcoHealth
• ISSN: 1612-9202
• Volume: 16
• Issue: 2
• Start Page: 275
• End Page: 286
• Grant/Funding Information: Funding support comes from the Zoetis/Morris Animal Foundation Veterinary Research Fellowship [D10ZO-902]; and the University of Minnesota Doctoral Dissertation Fellowship; the National Institute of Health (R01 AI058715, R01 AI 120810 and R00 HD057992); National Science Foundation (LTREB-1052693); Arcus Foundation; USFWS Great Ape Conservation Fund.; Monetary support and invaluable time and effort were provided by staff and volunteers at Lincoln Park Zoo’s Davee Center for Epidemiology and Endocrinology; and Lester E. Fisher for the Study and Conservation of Apes.
• Keywords: Respiratory disease; Surveillance; Wildlife health; Wildlife epidemiology
• Research Categories: Biology, Zoology; Environmental Sciences

Abstract

Disease surveillance in wildlife is rapidly expanding in scope and methodology, emphasizing the need for formal evaluations of system performance. We examined a syndromic surveillance system for respiratory disease detection in Gombe National Park, Tanzania, from 2004 to 2012, with respect to data quality, disease trends, and respiratory disease detection. Data quality was assessed by examining community coverage, completeness, and consistency. The data were examined for baseline trends; signs of respiratory disease occurred at a mean frequency of less than 1 case per week, with most weeks containing zero observations of abnormalities. Seasonal and secular (i.e., over a period of years) trends in respiratory disease frequency were not identified. These baselines were used to develop algorithms for outbreak detection using both weekly counts and weekly prevalence thresholds and then compared retrospectively on the detection of 13 respiratory disease clusters from 2005 to 2012. Prospective application of outbreak detection algorithms to real-time syndromic data would be useful in triggering a rapid outbreak response, such as targeted diagnostic sampling, enhanced surveillance, or mitigation.