Risk-based decision making for staggered bioterrorist attacks : resource allocation and risk reduction in "reload" scenarios.

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Description

Staggered bioterrorist attacks with aerosolized pathogens on population centers present a formidable challenge to resource allocation and response planning. The response and planning will commence immediately after the detection of the first attack and with no or little information of the second attack. In this report, we outline a method by which resource allocation may be performed. It involves probabilistic reconstruction of the bioterrorist attack from partial observations of the outbreak, followed by an optimization-under-uncertainty approach to perform resource allocations. We consider both single-site and time-staggered multi-site attacks (i.e., a reload scenario) under conditions when resources (personnel and equipment which ... continued below

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68 p.

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Lemaster, Michelle Nicole; Gay, David M. (Sandia National Laboratories, Albuquerque, NM); Ehlen, Mark Andrew (Sandia National Laboratories, Albuquerque, NM); Boggs, Paul T. & Ray, Jaideep October 1, 2009.

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Description

Staggered bioterrorist attacks with aerosolized pathogens on population centers present a formidable challenge to resource allocation and response planning. The response and planning will commence immediately after the detection of the first attack and with no or little information of the second attack. In this report, we outline a method by which resource allocation may be performed. It involves probabilistic reconstruction of the bioterrorist attack from partial observations of the outbreak, followed by an optimization-under-uncertainty approach to perform resource allocations. We consider both single-site and time-staggered multi-site attacks (i.e., a reload scenario) under conditions when resources (personnel and equipment which are difficult to gather and transport) are insufficient. Both communicable (plague) and non-communicable diseases (anthrax) are addressed, and we also consider cases when the data, the time-series of people reporting with symptoms, are confounded with a reporting delay. We demonstrate how our approach develops allocations profiles that have the potential to reduce the probability of an extremely adverse outcome in exchange for a more certain, but less adverse outcome. We explore the effect of placing limits on daily allocations. Further, since our method is data-driven, the resource allocation progressively improves as more data becomes available.

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68 p.

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  • Report No.: SAND2009-6008
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/993625 | External Link
  • Office of Scientific & Technical Information Report Number: 993625
  • Archival Resource Key: ark:/67531/metadc1014158

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  • October 1, 2009

Added to The UNT Digital Library

  • Oct. 14, 2017, 8:36 a.m.

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  • Oct. 25, 2017, 11:58 a.m.

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Lemaster, Michelle Nicole; Gay, David M. (Sandia National Laboratories, Albuquerque, NM); Ehlen, Mark Andrew (Sandia National Laboratories, Albuquerque, NM); Boggs, Paul T. & Ray, Jaideep. Risk-based decision making for staggered bioterrorist attacks : resource allocation and risk reduction in "reload" scenarios., report, October 1, 2009; United States. (digital.library.unt.edu/ark:/67531/metadc1014158/: accessed November 12, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.