Workshop 2
Hands-on with ADD models and data: sensitivity, bias, and some improvements to consider
The purpose of this workshop will be to demonstrate consequences of certain assumptions, temperature variability, and sampling approaches to the output of ADD models. Real and simulated temperature data will be provided, along with spreadsheets and code that automates the processing of temperature data into ADD. Attendees will be able to see how hypothetical error in lower and/or upper developmental threshold estimates interacts with temperature variability through time to affect ADD, and how random vs. non-probability sampling of larvae from populations can bias estimates of temporal intervals. Some proposed ways of reducing sensitivity to error in threshold estimation will be demonstrated and discussed. Attendees should bring computers able to run Microsoft Excel or similar. Simulation and analysis will also be conducted using SAS, working in Base SAS for anyone using the platform, or in SAS On Demand for Academics online (https://welcome.oda.sas.com/)
July 24, 2024 1:00pm-4:00pm
Workshop limited to 20 participants.
Instructor:
Dr. Thomas Chappell
Thomas Chappell is an assistant professor in the Department of Plant Pathology and Microbiology at Texas A&M University in College Station, with research specialization in modeling plant disease epidemiology and arthropod (often vector) phenology. He sees forensic entomology as the leading discipline in which phenological models are used specifically for prediction with emphasis on case-wise accuracy, and collaborates with forensic entomologists on modeling dynamics of forensically relevant insects. He received his PhD in biology from Duke University and postdoctoral training in entomology at NC State University. Current research projects involve phenological prediction of agricultural vector and pest arthropods, and adaptive/behavioral responses to host or environmental change and heterogeneity by insects and pathogens. He teaches undergraduate and graduate courses on applied modeling and epidemiology."