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Title: Distributed micro-releases of bioterror pathogens : threat characterizations and epidemiology from uncertain patient observables.

Abstract

Terrorist attacks using an aerosolized pathogen preparation have gained credibility as a national security concern since the anthrax attacks of 2001. The ability to characterize the parameters of such attacks, i.e., to estimate the number of people infected, the time of infection, the average dose received, and the rate of disease spread in contemporary American society (for contagious diseases), is important when planning a medical response. For non-contagious diseases, we address the characterization problem by formulating a Bayesian inverse problem predicated on a short time-series of diagnosed patients exhibiting symptoms. To keep the approach relevant for response planning, we limit ourselves to 3.5 days of data. In computational tests performed for anthrax, we usually find these observation windows sufficient, especially if the outbreak model employed in the inverse problem is accurate. For contagious diseases, we formulated a Bayesian inversion technique to infer both pathogenic transmissibility and the social network from outbreak observations, ensuring that the two determinants of spreading are identified separately. We tested this technique on data collected from a 1967 smallpox epidemic in Abakaliki, Nigeria. We inferred, probabilistically, different transmissibilities in the structured Abakaliki population, the social network, and the chain of transmission. Finally, we developed an individual-basedmore » epidemic model to realistically simulate the spread of a rare (or eradicated) disease in a modern society. This model incorporates the mixing patterns observed in an (American) urban setting and accepts, as model input, pathogenic transmissibilities estimated from historical outbreaks that may have occurred in socio-economic environments with little resemblance to contemporary society. Techniques were also developed to simulate disease spread on static and sampled network reductions of the dynamic social networks originally in the individual-based model, yielding faster, though approximate, network-based epidemic models. These reduced-order models are useful in scenario analysis for medical response planning, as well as in computationally intensive inverse problems.« less

Authors:
 [1]; ; ; ; ;
  1. University of Illinois, Urbana-Champaign, Urbana, IL
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
945910
Report Number(s):
SAND2008-6044
TRN: US200904%%190
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEFENSE; DISEASES; EPIDEMIOLOGY; NATIONAL SECURITY; PATHOGENS; PATIENTS; PLANNING; SYMPTOMS; Biological weapons.; Terrorism-Forecasting.; Terrorism-Health aspects.; Terrorism-Risk assessment-Mathematical models.

Citation Formats

Wolf, Michael M, Marzouk, Youssef M, Adams, Brian M, Devine, Karen Dragon, Ray, Jaideep, and Najm, Habib N. Distributed micro-releases of bioterror pathogens : threat characterizations and epidemiology from uncertain patient observables.. United States: N. p., 2008. Web. doi:10.2172/945910.
Wolf, Michael M, Marzouk, Youssef M, Adams, Brian M, Devine, Karen Dragon, Ray, Jaideep, & Najm, Habib N. Distributed micro-releases of bioterror pathogens : threat characterizations and epidemiology from uncertain patient observables.. United States. doi:10.2172/945910.
Wolf, Michael M, Marzouk, Youssef M, Adams, Brian M, Devine, Karen Dragon, Ray, Jaideep, and Najm, Habib N. Wed . "Distributed micro-releases of bioterror pathogens : threat characterizations and epidemiology from uncertain patient observables.". United States. doi:10.2172/945910. https://www.osti.gov/servlets/purl/945910.
@article{osti_945910,
title = {Distributed micro-releases of bioterror pathogens : threat characterizations and epidemiology from uncertain patient observables.},
author = {Wolf, Michael M and Marzouk, Youssef M and Adams, Brian M and Devine, Karen Dragon and Ray, Jaideep and Najm, Habib N},
abstractNote = {Terrorist attacks using an aerosolized pathogen preparation have gained credibility as a national security concern since the anthrax attacks of 2001. The ability to characterize the parameters of such attacks, i.e., to estimate the number of people infected, the time of infection, the average dose received, and the rate of disease spread in contemporary American society (for contagious diseases), is important when planning a medical response. For non-contagious diseases, we address the characterization problem by formulating a Bayesian inverse problem predicated on a short time-series of diagnosed patients exhibiting symptoms. To keep the approach relevant for response planning, we limit ourselves to 3.5 days of data. In computational tests performed for anthrax, we usually find these observation windows sufficient, especially if the outbreak model employed in the inverse problem is accurate. For contagious diseases, we formulated a Bayesian inversion technique to infer both pathogenic transmissibility and the social network from outbreak observations, ensuring that the two determinants of spreading are identified separately. We tested this technique on data collected from a 1967 smallpox epidemic in Abakaliki, Nigeria. We inferred, probabilistically, different transmissibilities in the structured Abakaliki population, the social network, and the chain of transmission. Finally, we developed an individual-based epidemic model to realistically simulate the spread of a rare (or eradicated) disease in a modern society. This model incorporates the mixing patterns observed in an (American) urban setting and accepts, as model input, pathogenic transmissibilities estimated from historical outbreaks that may have occurred in socio-economic environments with little resemblance to contemporary society. Techniques were also developed to simulate disease spread on static and sampled network reductions of the dynamic social networks originally in the individual-based model, yielding faster, though approximate, network-based epidemic models. These reduced-order models are useful in scenario analysis for medical response planning, as well as in computationally intensive inverse problems.},
doi = {10.2172/945910},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2008},
month = {10}
}

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