Syndromic Surveillance of Respiratory Disease in Free-Living Chimpanzees

Tiffany M Wolf; Randall Singer; Elizabeth V. Lonsdorf; Richard F Maclehose; Thomas R. Gillespie; Iddi Lipende; Jane Raphael; Karen Terio; Carson Murray; Anne Pusey; Beatrice H. Hahn; Shadrack Kamenya; Deus Mjungu; Dominic A Travis

doi:10.1007/s10393-019-01400-y

Syndromic Surveillance of Respiratory Disease in Free-Living Chimpanzees

Tiffany M Wolf, Randall Singer, Elizabeth V. Lonsdorf, Richard F Maclehose, Thomas R. Gillespie, Iddi Lipende, Jane Raphael, Karen Terio, Carson Murray, Anne Pusey, Beatrice H. Hahn, Shadrack Kamenya, Deus Mjungu, Dominic A Travis

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

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.

Original language	English (US)
Pages (from-to)	275-286
Number of pages	12
Journal	EcoHealth
Volume	16
Issue number	2
DOIs	https://doi.org/10.1007/s10393-019-01400-y
State	Published - Jun 15 2019

Bibliographical note

Funding Information:
We thank the staff of the Jane Goodall Institute?s Gombe Stream Research Centre for collecting daily health observations, as well as to the Gombe Ecosystem Health Project staff who manage and organize the data as it originates from the field. Thanks also to Andres Perez for insight and direction on time series analysis and Richard Maclehose for the constructive feedback on other aspects of the biostatistical analyses. Alex Krupnick, Emma Finestone, Emma Lantz, Emily Seidl, Anna Sjodin, and Edward Wilkerson provided key data management support. We also thank the Tanzania Commission for Science and Technology (COSTECH), Tanzania Wildlife Research Institute (TAWIRI), and Tanzania National Parks Association (TANAPA) for their continued support and approval of this research. 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.

Publisher Copyright:
© 2019, EcoHealth Alliance.

Keywords

Respiratory disease
Surveillance
Wildlife epidemiology
Wildlife health

PubMed: MeSH publication types

Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

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10.1007/s10393-019-01400-y

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6684380

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title = "Syndromic Surveillance of Respiratory Disease in Free-Living Chimpanzees",

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.",

keywords = "Respiratory disease, Surveillance, Wildlife epidemiology, Wildlife health",

author = "Wolf, {Tiffany M} and Randall Singer and Lonsdorf, {Elizabeth V.} and Maclehose, {Richard F} and Gillespie, {Thomas R.} and Iddi Lipende and Jane Raphael and Karen Terio and Carson Murray and Anne Pusey and Hahn, {Beatrice H.} and Shadrack Kamenya and Deus Mjungu and Travis, {Dominic A}",

note = "Funding Information: We thank the staff of the Jane Goodall Institute?s Gombe Stream Research Centre for collecting daily health observations, as well as to the Gombe Ecosystem Health Project staff who manage and organize the data as it originates from the field. Thanks also to Andres Perez for insight and direction on time series analysis and Richard Maclehose for the constructive feedback on other aspects of the biostatistical analyses. Alex Krupnick, Emma Finestone, Emma Lantz, Emily Seidl, Anna Sjodin, and Edward Wilkerson provided key data management support. We also thank the Tanzania Commission for Science and Technology (COSTECH), Tanzania Wildlife Research Institute (TAWIRI), and Tanzania National Parks Association (TANAPA) for their continued support and approval of this research. 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. Publisher Copyright: {\textcopyright} 2019, EcoHealth Alliance.",

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doi = "10.1007/s10393-019-01400-y",

language = "English (US)",

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AU - Wolf, Tiffany M

AU - Singer, Randall

AU - Lonsdorf, Elizabeth V.

AU - Maclehose, Richard F

AU - Gillespie, Thomas R.

AU - Lipende, Iddi

AU - Raphael, Jane

AU - Terio, Karen

AU - Murray, Carson

AU - Pusey, Anne

AU - Hahn, Beatrice H.

AU - Kamenya, Shadrack

AU - Mjungu, Deus

AU - Travis, Dominic A

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PY - 2019/6/15

Y1 - 2019/6/15

N2 - 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.

AB - 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.

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ER -

Syndromic Surveillance of Respiratory Disease in Free-Living Chimpanzees

Abstract

Bibliographical note

Keywords

PubMed: MeSH publication types

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