Discipline: Biological Sciences
Subcategory: STEM Research
Katharina Wollenberg Valero - Bethune-Cookman University
Co-Author(s): Noah E. Douglas, Brianna Johnson, Ayana McGill, Kiara Wootson, Seenith Sivasundaram, and Raphael Isokpehi, Bethune-Cookman University, Daytona Beach, FL
In Fall 2015, ten undergraduate students and three faculty members of Bethune-Cookman University participated in a NSF-sponsored student workshop series about integrating mathematics with quantitative Biology. Two teams of student researchers from Freshman to Senior year belonging to different majors worked on the evaluation of molecular networks, and predictive modeling and dynamics of viral epidemics. The workshop series contributed to the goal of the College of Science, Engineering and Mathematics to achieving academic excellence in Data Analytics (HBCU-UP Targeted Infusion ‘QEUBiC’ project). This sub-project involved students majoring in Nursing, Biology, and Mathematics. Our goal was to deliver a proof of concept that sporadic spatiotemporal host-switch events in zoonotic diseases with unknown natural hosts can be predicted using eco-environmental data. Ebola Virus disease outbreaks in mammals constitute such a case of sporadic emergence, but the factors leading to spill-over events from natural host to mammal including humans and great apes, are yet unknown. Ebola Virus belongs to the family Filoviridae that has a wide range of natural hosts, and is unstable once outside its host. It is therefore directly linked to properties of its host’s ecosystem and its environment. This phenomenon is for example also known from Rabies Virus. We set out to prove that spatiotemporal fluctuations of a set of unrelated eco-environmental variables describing the dynamics of the host ecosystem will be able to accurately predict spillover events. We compiled data of ecological and environmental variables, including rainfall, temperature, climate anomaly, phenology, and animal migration patterns from the literature, together with information on Ebola Virus disease spill-overs. From these we generated data sets showing annual and monthly patterns. We analyzed their predictive power for spill-over events and identified significant predictor variables. Spatial occurrence probability data is available from a recent publication on a Ebola Virus Environmental Niche Model. These variables were used to generate first a graphical model, and lastly, a neural network model. By visualizing the data set and generating a graphical model, we were already able to pin-point a set of critical biotic and abiotic conditions that might enable cross-taxon spill-over events. We plan to integrate this model to include the spatial predictive GIS data, in order to obtain a spatiotemporal predictive model that can be adapted to other emerging zoonotic diseases.
Funder Acknowledgement(s): NSF HBCU-UP: 1435186
Faculty Advisor: None Listed,