Constructing rigorous and broad biosurveillance networks for detecting emerging zoonotic outbreaks
Abstract
Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (butmore »
- Authors:
-
- Univ. of California-Santa Barbara, Santa Barbara, CA (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Tulane Univ., New Orleans, LA (United States)
- Univ. of New Mexico, Albuquerque, NM (United States)
- Minnesota Pollution Control Agency, St. Paul, MN (United States)
- Univ. of British Columbia, Vancouver, BC (Canada)
- National Institutes of Health, New York, NY (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1214724
- Grant/Contract Number:
- AC52-06NA25396
- Resource Type:
- Accepted Manuscript
- Journal Name:
- PLoS ONE
- Additional Journal Information:
- Journal Volume: 10; Journal Issue: 5; Journal ID: ISSN 1932-6203
- Publisher:
- Public Library of Science
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; infectious disease surveillance; infectious disease epidemiology; H5N1; Nigeria; avian influenza; poultry; spatial epidemiology; zoonotic pathogens
Citation Formats
Brown, Mac, Moore, Leslie, McMahon, Benjamin, Powell, Dennis, LaBute, Montiago, Hyman, James M., Rivas, Ariel, Jankowski, Mark, Berendzen, Joel, Loeppky, Jason, Manore, Carrie, Fair, Jeanne, and Viboud, Cecile. Constructing rigorous and broad biosurveillance networks for detecting emerging zoonotic outbreaks. United States: N. p., 2015.
Web. doi:10.1371/journal.pone.0124037.
Brown, Mac, Moore, Leslie, McMahon, Benjamin, Powell, Dennis, LaBute, Montiago, Hyman, James M., Rivas, Ariel, Jankowski, Mark, Berendzen, Joel, Loeppky, Jason, Manore, Carrie, Fair, Jeanne, & Viboud, Cecile. Constructing rigorous and broad biosurveillance networks for detecting emerging zoonotic outbreaks. United States. https://doi.org/10.1371/journal.pone.0124037
Brown, Mac, Moore, Leslie, McMahon, Benjamin, Powell, Dennis, LaBute, Montiago, Hyman, James M., Rivas, Ariel, Jankowski, Mark, Berendzen, Joel, Loeppky, Jason, Manore, Carrie, Fair, Jeanne, and Viboud, Cecile. Wed .
"Constructing rigorous and broad biosurveillance networks for detecting emerging zoonotic outbreaks". United States. https://doi.org/10.1371/journal.pone.0124037. https://www.osti.gov/servlets/purl/1214724.
@article{osti_1214724,
title = {Constructing rigorous and broad biosurveillance networks for detecting emerging zoonotic outbreaks},
author = {Brown, Mac and Moore, Leslie and McMahon, Benjamin and Powell, Dennis and LaBute, Montiago and Hyman, James M. and Rivas, Ariel and Jankowski, Mark and Berendzen, Joel and Loeppky, Jason and Manore, Carrie and Fair, Jeanne and Viboud, Cecile},
abstractNote = {Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain) and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic.},
doi = {10.1371/journal.pone.0124037},
journal = {PLoS ONE},
number = 5,
volume = 10,
place = {United States},
year = {Wed May 06 00:00:00 EDT 2015},
month = {Wed May 06 00:00:00 EDT 2015}
}
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