Kansas Biosciences Authority Funded Projects
Modeling a Rift Valley fever risk surveillance system and testing efficient mitigation strategies for the United States
Kansas State University PI: Catherine Scoglio
Kansas State University Co-PI: Faryad D. Sahneh
USDA-ARS-CMAVE PI: Kenneth Linthicum
USDA-ARS-CMAVE Co-PI: Seth Britch
07/01/2011 – 12/31/2016
The overall goal of this project is to prepare for a potential Rift Valley fever virus (RVFV) introduction into the United States: FL will develop a geographic information system (GIS)/remotely sensed early warning model system for potential Rift Valley fever (RVF) risk in the US using mosquito population surveillance data collected by mosquito control and public health agencies, and climate data measured by satellites, such as vegetation index and land surface temperature, and terrestrial weather stations; KS will develop mathematical and simulative models of RVFV epidemiology in the US to test a variety of mitigation strategies, including vaccine development and deployment, reduction and control of mosquito populations, and livestock movement reduction and ban. Products gained from the FL team will provide actual US potential vector population dynamics knowledge for input into the KS model development and evaluation of mitigation strategies.
A public-private partnership for supporting the control of foreign swine disease incursions and disease emergencies in the United States
University of Minnesota PI: Andres Perez
University of Minnesota Co-PI: Kim VanderWaal
07/01/2013 – 12/31/2016
We propose to develop and implement a public-private partnership that makes use of models, quantitative tools, and methods for supporting the control of eventual foreign animal disease incursions (FMD, ASF, and CSF) and disease emergences (SVV, PED, others) in the United States. This effort is unique because of its potential for building a dynamic and operational system that could effectively operate in real-time during the early phase of an epidemic or emergence through private-public partnerships. Specific objectives include:
1. Applied epidemiology: implement algorithms and results of an NPB-funded initiative to develop a within- and between-farm spread model for FMD and other swine diseases, so that model makes use of Swine Health Monitoring Program (SHMP) data to run simulations in near-real time;
2. Policy: outline, in collaboration with USDA-APHIS and the Swine Health Information Center (SHIC), the role that SHMP may play to support disease control and continuity of business in the context of a swine foreign animal disease incursion or disease emergence.
Real-time risk assessment platform for evaluating the risk of African Swine Fever (ASF) introduction into the United States
Kansas State University PI: Lina Mur Gil
University of California-Davis Co-PI: Beatriz Martínez López
01/01/2016 – 12/31/2016
The aim of this project is to develop a digital platform for the real-time risk assessment of ASF introduction into the US that would be scalable and could be easily adapted to evaluate other Foreign Animal Diseases (FADS). This platform will allow the gathering, update and integration of different epidemiological data for the near real-time risk assessment of ASF introduction into US through different routes. Specifically, it will provide: i) an updated risk profile (where and when) of ASF risk of introduction into the US, ii) the possibility to set up and send notifications when the risk significantly changed and iii) sensitivity analysis and scenario evaluation tools to quantify the impact of specific scenarios (e.g. the impact of change in policies or trade patterns) on the risk estimates.
Model of transmission of Foot and Mouth Disease virus on and from a U.S. beef farm
Kansas State University PI: Victoriya Volkova
Kansas State University Co-PI: Mike Sanderson
01/01/2016 – 12/31/2016
Develop a mathematical model of transmission of Foot and Mouth Disease virus (FMDV) on and from a U.S. beef farm. This will be the first model reflecting the U.S. feedlot production practices. Specific objectives include (1) develop structure of a mathematical model of FMDV transmission within a U.S. beef feedlot, (2) define boundary and distribution for the values of each parameter in the model, (3) apply the model to simulate the expected within-farm FMDV dynamics and risks of FMDV in cattle and fomites leaving the farm for different scenarios of introduction route, virulence of the infecting serotype, and farm characteristics, and (4) apply the model to describe the expected temporal manifestations of FMD in cattle on the farm under the different scenarios. Consider if and which of the disease manifestations may be captured via routine cattle observation, the Enhanced Passive Surveillance platform prior to an outbreak, or Active Observational Surveillance during an outbreak.