6 papers
Transfer Learning using 66 Diseases for Disease Forecasting Applications
Lauren J Beesley, Alexander C Murph, Dave Osthus +1
Disease forecasting models typically rely on a single data stream, making models brittle when histories are short or noisy. Recent top-performing models have shown that synthesizin…
Leveraging Synthetic and Genetic Data to Improve Epidemic Forecasting
Dave Osthus, Alexander C. Murph, Emma E. Goldberg +4
Forecasting infectious disease outbreaks is hard. Forecasting emerging infectious diseases with limited historical data is even harder. In this paper, we investigate ways to improv…
Bayesian Statistical Inversion for High-Dimensional Computer Model Output and Spatially Distributed Counts
Steven D. Barnett, Robert B. Gramacy, Lauren J. Beesley +4
Data collected by the Interstellar Boundary Explorer (IBEX) satellite, recording heliospheric energetic neutral atoms (ENAs), exhibit a phenomenon that has caused space scientists…
Monotonic warpings for additive and deep Gaussian processes
Steven D. Barnett, Lauren J. Beesley, Annie S. Booth +2
Gaussian processes (GPs) are canonical as surrogates for computer experiments because they enjoy a degree of analytic tractability. But that breaks when the response surface is con…
Mapping Incidence and Prevalence Peak Data for SIR Forecasting Applications
Alexander C. Murph, G. Casey Gibson, Lauren J. Beesley +4
Infectious disease modeling and forecasting have played a key role in helping assess and respond to epidemics and pandemics. Recent work has leveraged data on disease peak infectio…
Moving Towards Automated Interstellar Boundary Explorer Data Selection with LOTUS
Madeline A. Stricklin, Lauren J. Beesley, Brian P. Weaver +5
The Interstellar Boundary Explorer (IBEX) satellite collects data on energetic neutral atoms (ENAs) that provide insight into the heliosphere, the region surrounding our solar syst…