| NSF Org: |
SES Division of Social and Economic Sciences |
| Recipient: |
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| Initial Amendment Date: | September 11, 2008 |
| Latest Amendment Date: | September 11, 2008 |
| Award Number: | 0841112 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Fahmida Chowdhury
fchowdhu@nsf.gov (703)292-4672 SES Division of Social and Economic Sciences SBE Directorate for Social, Behavioral and Economic Sciences |
| Start Date: | September 1, 2008 |
| End Date: | August 31, 2009 (Estimated) |
| Total Intended Award Amount: | $50,001.00 |
| Total Awarded Amount to Date: | $50,001.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1601 VATTIER STREET MANHATTAN KS US 66506-2504 (785)532-6804 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1601 VATTIER STREET MANHATTAN KS US 66506-2504 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Cross-Directorate Activities |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.075 |
ABSTRACT
The objective of this proposed exploratory and potentially transformative research is to use complex network theory to model and analyze the spread of epidemics in rural regions, with special emphasis on the study of graph characteristics and dynamics, and their impact on the speed and direction of the epidemic.
The social and economic costs of epidemics may be less well understood today than ever, yet infection of only a few animals or humans can have serious implications for international trade and policies. Furthermore, the loss of far greater numbers of livestock or people is quite possible, with inherently
enormous social and economic costs. Moreover, methods for detecting and forecasting epidemics that may have worked in the past in urban or rural overseas regions may not apply to rural regions in the Plains states today.
The overarching goal of this research is to develop optimized guidelines that administrators can use to establish procedures and realign resources to help mitigate the effects of an outbreak in rural regions, caused by a malicious attack or by natural occurrences.
The research team will start working on the following four research tasks:
(1) collect empirical data on rural Kansas and create the underlying networks, (2) extend the underlying network to families of graphs and study the graph-theoretical metrics of those networks to predict their behavior and dynamics during an epidemic,
(3) create accurate and portable simulators running on PCs, and
(4) develop optimized guidelines to control outbreaks.
Intellectual merit. This research is intended to lead to scientific breakthroughs in complex network theory and analysis. In particular, families of networks will be analyzed to determine critical structures for the spread of epidemics. New metrics will be proposed to quantitatively measure the network robustness relative to epidemic spreading. Additionally, a new type of analysis for the rate at which an infection spreads will also be generated using weighted and asymmetric networks. This new analysis should provide greater accuracy in predicting the spread of an epidemic. This knowledge can then be incorporated into policies/plans to curb the spread of an infectious disease.
Broader impacts. This research is intended to have a broad impact on society and related research. For example, society will benefit by having more effective policies to decrease the effects of an epidemic. To clarify, in all cases, disease epidemics in human or animal populations may cause extensive social and economic losses. Being able to have an environment that is intrinsically robust to these type of attacks would provide a strong defense against malicious individuals as well as protection against natural events/disasters. Thus, the proposed work will foster interdisciplinary collaboration among rural sociologists and network experts. Finally, the research team will continue to mentor and recruit minorities and females into their research group, which is an interdisciplinary team.
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