$2.5M Grant to Study How Infectious Diseases Become Epidemics
News Nov 26, 2015
Caterina Scoglio, professor of electrical and computer engineering and Faryad Darabi Sahneh, research assistant professor of electrical and computer engineering, are part of a larger group including colleagues from Oregon State University, North Carolina State University, the U.S. Department of Agriculture and two universities in England. The group was awarded a $2.5 million grant through the Ecology and Evolution of Infectious Diseases, or EEID, program jointly funded by the National Science Foundation, the U.S. Department of Agriculture's National Institute of Food and Agriculture, the National Institutes of Health and the U.K.'s Biotechnology and Biological Sciences Research Council. The program supports projects that study how large-scale environmental events such as habitat destruction or pollution alter risks of viral, parasitic and bacterial disease emergence.
The Kansas State University team will study data for vector-borne infectious diseases to model how these types of epidemics spread. Vector-borne diseases are spread by infectious microbes transmitted by ticks, mosquitos or other insects or parasites. Kansas State University researchers are particularly interested in the role of long-distance dispersal in the spread of diseases. They will evaluate the efficacy of different control methods, such as limiting animal movements or reducing the vector population. As models are compared and refined, they will help researchers develop rules of thumb for controlling outbreaks.
Scoglio, professor of electrical and computer engineering, said the project combines scientists with expertise in plant pathology, livestock diseases and vector-borne diseases to identify similarities in how the different types of diseases spread.
"We come from different disease modeling frameworks, but the point is to see if these frameworks can be translated — if there are unifying aspects in any spreading process," Scoglio said.
"The role of long-range dispersal is important to examine because sometimes the diseases don't spread as a wave in a population, but they jump to far locations because an infected animal is transferred to a distant farm or an exposed person travels from one city to another, maybe on a different continent," she said.
Sahneh is excited to work with the group of investigators.
"We at K-State want to collaborate with this team to seek the universal knowledge in transmission of infectious diseases despite the apparently disparate models describing distinct domains," Sahneh said.
Computer scientists at Carnegie Mellon University say neural networks and supervised machine learning techniques can efficiently characterize cells that have been studied using single cell RNA-sequencing (scRNA-seq). This finding could help researchers identify new cell subtypes and differentiate between healthy and diseased cells.