| Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States EY Cramer, EL Ray, VK Lopez, J Bracher, A Brennen, ... Proceedings of the National Academy of Sciences 119 (15), e2113561119, 2022 | 328* | 2022 |
| A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States NG Reich, LC Brooks, SJ Fox, S Kandula, CJ McGowan, E Moore, ... Proceedings of the National Academy of Sciences 116 (8), 3146-3154, 2019 | 280 | 2019 |
| Ensemble forecasts of coronavirus disease 2019 (COVID-19) in the US EL Ray, N Wattanachit, J Niemi, AH Kanji, K House, EY Cramer, J Bracher, ... MedRXiv, 2020.08. 19.20177493, 2020 | 220 | 2020 |
| Forecasting seasonal influenza with a state-space SIR model D Osthus, KS Hickmann, PC Caragea, D Higdon, SY Del Valle The annals of applied statistics 11 (1), 202, 2017 | 188 | 2017 |
| Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the US NG Reich, CJ McGowan, TK Yamana, A Tushar, EL Ray, D Osthus, ... PLoS computational biology 15 (11), e1007486, 2019 | 150 | 2019 |
| Epidemic forecasting is messier than weather forecasting: The role of human behavior and internet data streams in epidemic forecast KR Moran, G Fairchild, N Generous, K Hickmann, D Osthus, ... The Journal of infectious diseases 214 (suppl_4), S404-S408, 2016 | 143 | 2016 |
| Validity of 24-h physical activity recall: physical activity measurement survey GJ Welk, Y Kim, B Stanfill, DA Osthus, AM Calabro, SM Nusser, ... Medicine and science in sports and exercise 46 (10), 2014 | 88 | 2014 |
| Dynamic Bayesian influenza forecasting in the United States with hierarchical discrepancy (with discussion) D Osthus, J Gattiker, R Priedhorsky, SY Del Valle | 77 | 2019 |
| Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations K Sherratt, H Gruson, H Johnson, R Niehus, B Prasse, F Sandmann, ... Elife 12, e81916, 2023 | 62 | 2023 |
| Quantifying topological uncertainty in fractured systems using graph theory and machine learning G Srinivasan, JD Hyman, DA Osthus, BA Moore, D O’Malley, S Karra, ... Scientific reports 8 (1), 11665, 2018 | 61 | 2018 |
| Identifying backbones in three-dimensional discrete fracture networks: A bipartite graph-based approach JD Hyman, A Hagberg, D Osthus, S Srinivasan, H Viswanathan, ... Multiscale Modeling & Simulation 16 (4), 1948-1968, 2018 | 58 | 2018 |
| Measuring global disease with Wikipedia: Success, failure, and a research agenda R Priedhorsky, D Osthus, AR Daughton, KR Moran, N Generous, ... Proceedings of the 2017 ACM Conference on Computer Supported Cooperative …, 2017 | 40 | 2017 |
| Calibrating the stress-time curve of a combined finite-discrete element method to a Split Hopkinson Pressure Bar experiment D Osthus, HC Godinez, E Rougier, G Srinivasan International Journal of Rock Mechanics and Mining Sciences 106, 278-288, 2018 | 38 | 2018 |
| Even a good influenza forecasting model can benefit from internet-based nowcasts, but those benefits are limited D Osthus, AR Daughton, R Priedhorsky PLoS computational biology 15 (2), e1006599, 2019 | 35 | 2019 |
| Multiscale influenza forecasting D Osthus, KR Moran Nature communications 12 (1), 2991, 2021 | 28 | 2021 |
| Google Health Trends performance reflecting dengue incidence for the Brazilian states D Romero-Alvarez, N Parikh, D Osthus, K Martinez, N Generous, ... BMC infectious diseases 20, 1-15, 2020 | 22 | 2020 |
| Fourier amplitude sensitivity test applied to dynamic combined finite‐discrete element methods–based simulations HC Godinez, E Rougier, D Osthus, Z Lei, E Knight, G Srinivasan International Journal for Numerical and Analytical Methods in Geomechanics …, 2019 | 22 | 2019 |
| Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction KM Holcomb, S Mathis, JE Staples, M Fischer, CM Barker, CB Beard, ... Parasites & Vectors 16 (1), 11, 2023 | 19 | 2023 |
| Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil LA Castro, N Generous, W Luo, A Pastore y Piontti, K Martinez, ... PLoS neglected tropical diseases 15 (5), e0009392, 2021 | 19 | 2021 |
| Physics-informed machine learning for backbone identification in discrete fracture networks S Srinivasan, E Cawi, J Hyman, D Osthus, A Hagberg, H Viswanathan, ... Computational Geosciences 24, 1429-1444, 2020 | 19 | 2020 |