Measuring Global Disease with Wikipedia: Success, Failure, and a Research Agenda. - Reid Priedhorsky; Dave Osthus; Ashlynn R. Daughton; Kelly R. Moran; Nicholas Generous; Geoffrey Fairchild; Alina Deshpande; Sara Y. Del Valle (CSCW, 2017)

Deceptiveness of internet data for disease surveillance. - Reid Priedhorsky; Dave Osthus; Ashlynn R. Daughton; Kelly R. Moran; Aron Culotta (CoRR, 2017)

Identifying Backbones in Three-Dimensional Discrete Fracture Networks: A Bipartite Graph-Based Approach. - Jeffrey D. Hyman; Aric A. Hagberg; Dave Osthus; Shriram Srinivasan; Hari S. Viswanathan; Gowri Srinivasan (Multiscale Model. Simul., 2018)

A tale of four cities: exploring the soul of State College, Detroit, Milledgeville and Biloxi. - Karsten Maurer; Dave Osthus; Adam Loy (Comput. Stat., 2019)

Prediction Uncertainties beyond the Range of Experience: A Case Study in Inertial Confinement Fusion Implosion Experiments. - Dave Osthus; Scott A. Vander Wiel; Nelson M. Hoffman; Frederick J. Wysocki (SIAM/ASA J. Uncertain. Quantification, 2019)

Even a good influenza forecasting model can benefit from internet-based nowcasts, but those benefits are limited. - Dave Osthus; Ashlynn R. Daughton; Reid Priedhorsky (PLoS Comput. Biol., 2019)

Estimating influenza incidence using search query deceptiveness and generalized ridge regression. - Reid Priedhorsky; Ashlynn R. Daughton; Martha Barnard; Fiona O'Connell; Dave Osthus (PLoS Comput. Biol., 2019)

Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S. - Nicholas G. Reich; Craig J. McGowan; Teresa K. Yamana; Abhinav Tushar; Evan L. Ray; Dave Osthus; Sasikiran Kandula; Logan C. Brooks; Willow Crawford-Crudell; Graham Casey Gibson; Evan Moore; Rebecca Silva; et al. (PLoS Comput. Biol., 2019)

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling. - Nishant Panda; Dave Osthus; Gowri Srinivasan; Daniel O'Malley; Viet T. Chau; Diane Oyen; Humberto Godinez (J. Comput. Phys., 2020)

A Probabilistic Clustering Approach for Identifying Primary Subnetworks of Discrete Fracture Networks with Quantified Uncertainty. - Dave Osthus; Jeffrey D. Hyman; Satish Karra; Nishant Panda; Gowri Srinivasan (SIAM/ASA J. Uncertain. Quantification, 2020)

Improving probabilistic infectious disease forecasting through coherence. - Graham Casey Gibson; Kelly R. Moran; Nicholas G. Reich; Dave Osthus (PLoS Comput. Biol., 2021)

Addressing delayed case reporting in infectious disease forecast modeling. - Lauren J. Beesley; Dave Osthus; Sara Y. Del Valle (PLoS Comput. Biol., 2022)

Fast and accurate influenza forecasting in the United States with Inferno. - Dave Osthus (PLoS Comput. Biol., 2022)

Analyzing count data with measurement error. - Michael S. Hamada; Emily M. Casleton; Dave Osthus; Brian P. Weaver; Stefan H. Steiner (Qual. Reliab. Eng. Int., 2022)

Challenges of COVID-19 Case Forecasting in the US, 2020-2021. - Velma K. Lopez; Estee Y. Cramer; Robert Pagano; John M. Drake; Eamon B. O'Dea; Madeline Adee; Turgay Ayer; Jagpreet Chhatwal; Ozden O. Dalgic; Mary A. Ladd; Benjamin P. Linas; Peter P. Mueller; et al. (PLoS Comput. Biol., 2024)

Towards Improved Heliosphere Sky Map Estimation with Theseus. - Dave Osthus; Brian P. Weaver; Lauren J. Beesley; Kelly R. Moran; Madeline A. Stricklin; Eric J. Zirnstein; Paul H. Janzen; Daniel B. Reisenfeld (Technometrics, 2024)

Monotonic warpings for additive and deep Gaussian processes. - Steven D. Barnett; Lauren J. Beesley; Annie S. Booth; Robert B. Gramacy; Dave Osthus (Stat. Comput., 2025)