Modeling a Rift Valley fever risk surveillance system and testing efficient mitigation strategies for the United States

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- Compared 2010 livestock and human incidence data in three provinces of South Africa with Monte Carlo simulation results.
 
- Compared 2010 livestock and human incidence data in three provinces of South Africa with Monte Carlo simulation results.
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- Through a large-scale maximum entropy optimization formulation, we estimated cattle movement parameters to characterize the movements of cattle across 10 Central States and 1034 counties of the United States. Inputs to the estimation problem are taken from the United States Department of Agriculture National Agricultural Statistics Service database. We compared stochastic subpopulation-based movements generated from the estimated parameters to operation-based movements published by the United States Department of Agriculture. For future Census of Agriculture distributions, we proposed a series of questions that enable improvements for our method without compromising the privacy of cattle operations. Our novel method to estimate cattle movements across large US regions characterizes county-level stratified subpopulations of cattle for data-driven livestock modeling. Our estimated movement parameters suggest a significant risk level for US cattle systems.
  
  
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Journal of Theoretical Biology, Volume 306, Pages 129–144, 2012
 
Journal of Theoretical Biology, Volume 306, Pages 129–144, 2012
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'''Cattle Movement Estimates'''
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[[File:CattleMov.xlsx]]

Revision as of 13:29, 20 August 2014

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People

Ling Xue, Phillip Schumm, Caterina Scoglio, Morgan Scott, Lee Cohnstaedt.


Supported by the Kansas Bioscience Authority for the DHS Center of Excellence for Emerging and Zoonotic Animal Diseases


Abstract


Rift Valley Fever virus (RVFv) has been expanding its geographical distribution with important implications for both human and animal health. The emergence of Rift Valley Fever (RVF) in the Middle East, and its continuing presence in many areas of Africa, has negatively impacted both medical and veterinary morbidity, mortality, and economic endpoints. Furthermore, worldwide attention should be directed towards the broader infection dynamics of RVFv, since suitable host, vector and environmental conditions for additional epidemics likely exist on other continents; including Asia, Europe and the Americas. We propose a new compartmentalized ordinary differential equation model of RVF to assess disease spread in both time and in space; with the latter driven as a function of contact networks. Humans and livestock hosts and two species of vector mosquitoes are included in the model. The model is based on weighted contact networks, where nodes of the networks represent geographical regions and the weights represent the level of contact between regional pairings for each set of species. The inclusion of specifics on human, animal, and vector movements among regions is new to modeling RVF. The movement of the infected individuals is not only treated as a possibility, but also an actuality that can be incorporated into the model. We have tested, calibrated, and evaluated the model using data from the recent 2010 RVF outbreak in South Africa as case study; mapping the epidemic spread within and among three South African provinces. An extensive set of simulation results shows the potential of the proposed approach for accurately modeling the RVF spreading process in additional regions of the world. The benefits of our proposed model are twofold: not only can our model differentiate the maximum number of infected individuals among different provinces, but also it can reproduce the different starting times of the outbreak in multiple locations.

The following are recent achievements in this project:

- Derived the network level reproduction number and its bounds for Rift Valley fever meta-population model, and studied the role of livestock movement rates and disease parameters on the epidemic threshold of networks,

- Generalized an explicit expression of a network level reproduction number for a disease involving multiple species with both vertical and horizontal transmission,

- Completed case study of hypothetical Rift Valley fever virus spread in ranching areas of Texas on hierarchical large scale networks,

- Developed a stochastic meta-population model to study dynamic transmission of Rift Valley fever,

- Compared 2010 livestock and human incidence data in three provinces of South Africa with Monte Carlo simulation results.

- Through a large-scale maximum entropy optimization formulation, we estimated cattle movement parameters to characterize the movements of cattle across 10 Central States and 1034 counties of the United States. Inputs to the estimation problem are taken from the United States Department of Agriculture National Agricultural Statistics Service database. We compared stochastic subpopulation-based movements generated from the estimated parameters to operation-based movements published by the United States Department of Agriculture. For future Census of Agriculture distributions, we proposed a series of questions that enable improvements for our method without compromising the privacy of cattle operations. Our novel method to estimate cattle movements across large US regions characterizes county-level stratified subpopulations of cattle for data-driven livestock modeling. Our estimated movement parameters suggest a significant risk level for US cattle systems.


Publications

L. Xue, C. Scoglio

The network level reproduction number for infectious diseases with both vertical and horizontal transmission

Mathematical Biosciences, 243: 67-80, 2013


L. Xue, L. W. Cohnstaedt, H. M. Scott, C. Scoglio

A hierarchical network approach for modeling Rift Valley fever epidemics with applications in North America

PLoS ONE, PLOS ONE 8:e62049, 2013


L. Xue, H. M. Scott, L. W. Cohnstaedt, C. Scoglio

A network-based meta-population approach to model Rift Valley fever epidemics

Journal of Theoretical Biology, Volume 306, Pages 129–144, 2012


Cattle Movement Estimates

File:CattleMov.xlsx

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