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Constructing rigorous and broad biosurveillance networks for detecting emerging zoonotic outbreaks

Journal Article · · PLoS ONE
 [1];  [2];  [2];  [2];  [3];  [4];  [5];  [6];  [2];  [7];  [4];  [2];  [8]
  1. Univ. of California-Santa Barbara, Santa Barbara, CA (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  4. Tulane Univ., New Orleans, LA (United States)
  5. Univ. of New Mexico, Albuquerque, NM (United States)
  6. Minnesota Pollution Control Agency, St. Paul, MN (United States)
  7. Univ. of British Columbia, Vancouver, BC (Canada)
  8. National Institutes of Health, New York, NY (United States)
+ Show Author Affiliations

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.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC52-06NA25396
OSTI ID:
1214724
Journal Information:
PLoS ONE, Journal Name: PLoS ONE Journal Issue: 5 Vol. 10; ISSN 1932-6203
Publisher:
Public Library of ScienceCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
60 APPLIED LIFE SCIENCES
H5N1
Nigeria
avian influenza
infectious disease epidemiology
infectious disease surveillance
poultry
spatial epidemiology
zoonotic pathogens