Developing Mitigation Strategies for Pandemic Outbreaks
Research Abstract
As evident from the recent worldwide person-to-person spread of swine flu (H1N1) virus, a pandemic outbreak of a virulent influenza strain (H5N1) will have a potentially devastating impact. This is attributed to the lack of adequate containment resources and decision aids for policy makers. In its recent "Modeling Community Containment for Pandemic Influenza" report, the Institute of Medicine (IOM) observed that "because of the significant constraints placed on the (existing) models" (e.g., MIDAS), "the scope of the models should be expanded." The report urges to: 1) "develop decision-aid models that can ... provide real-time feedback", 2) "incorporate broader outcome measures", and 3) "include a broader range of closure options" for social distancing. Our proposed research is aimed at addressing the IOM recommendations. We will devise a methodology to support development of real-time predictive mitigation strategies subsuming a broader spectrum of interventions and outcome measures. This development is already under way by our cross-disciplinary research team with expertise in public
health, social networks, parallel & distributed computing, and systems engineering.
To address natural and social problems involving pandemic outbreaks, we must understand complexity of real-time evolution of virus epidemiology and social dynamics. The national academies recognize that such an understanding can be achieved via comprehensive cyber-enabled simulation models driven by real-time data extracted from demographical, social-behavioral, and epidemiological surveillance databases. We will address the following research objectives.
1) Build a cyber-enabled simulation model that emulates social and viral dynamics of cross-regional pandemic outbreaks, and incorporates real-time data from demographical, social-behavioral, and epidemiological surveillance databases.
2) Build an optimization model, driven by the simulation, to generate dynamic predictive strategies for pharmaceutical and non-pharmaceutical interventions.
3) Develop a parallel & distributed computing framework to support synthesis of an efficient cyber-enabled computational strategy for the baseline simulation and optimization models.
4) Demonstrate how strategies generated by our methodology can be translated into implementable field guidelines by accommodating practical constraints and existing protocols.
5) Develop educational and dissemination plans encompassing K-12, undergraduate and graduate levels, and professional ranks in cyber-enabled computing and pandemic response.
Figure: A Spread Model with Two-Tier Mitigation Approach
Publications
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Das, T. K.,
Savachkin, A., and Zhu, Y. 2007.
A Large Scale Simulation Model of Pandemic Influenza Outbreaks
for Development of Dynamic Mitigation Strategies. IIE Transactions, Vol. 40, 9, pp. 893-905.
- Savachkin, A., Uribe, A, Prieto, D. and Das, T. K., 2009. Dynamic Mitigation Strategies for Pandemic Outbreaks: a Four-County Testbed in the State of Florida.
- Prieto, D., Das, T. K., Savachkin, A. and Uribe, A, 2009. Real-Time Implementation Needs of Pandemic Models for Effective Mitigation Decision Support. (In review with Journal of Influenza and Other Respiratory Viruses).
Conference Presentations
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Das, T. K.,
Savachkin, A, Prieto, D., and Uribe, A.
A Simulation-based Optimization
Framework for Pandemic Impact Mitigation. INFORMS Annual Meeting 2007, Seattle,
WA.
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Savachkin, A, Das, T. K., Prieto, D., and Uribe, A.
A
Simulation-Based Optimization Model for Dynamic Resource
Utilization at Federal and Local Levels During Cross-Regional
Pandemic Outbreaks.
INFORMS Annual Meeting 2007, Seattle, WA.
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