In response to the rapid spread of the novel coronavirus, SARS-CoV-2, the U.S. has largely delegated implementation of non-pharmaceutical interventions (NPIs) to local governments on the state and county level. This staggered implementation combined with the heterogeneity of the U.S. complicates quantification the effect of NPIs on the reproductive rate of SARS-CoV-2. We describe a data-driven approach to quantify the effect of NPIs that relies on county-level similarities to specialize a Bayesian mechanistic model based on observed fatalities. Using this approach, we estimate change in reproductive rate, R_t, due to implementation of NPIs in 1,417 U.S. counties. We estimate that as of May 28th, 2020 1,177 out of the considered 1,417 U.S. counties have reduced the reproductive rate of SARS-CoV-2 to below 1.0. The estimated effect of any individual NPI, however, is different across counties. Stay-at-home orders were estimated as the only effective NPI in metropolitan and urban counties, while advisory NPIs were estimated to be effective in more rural counties. The expected level of infection predicted by the model ranges from 0 to 28.7% and is far from herd immunity even in counties with advanced spread. Our results suggest that local conditions are pertinent to containment and re-opening decisions.

Automated X-ray image segmentation would accelerate research and development in diagnostic and interventional precision medicine. Prior efforts have contributed task-specific models capable of solving specific image analysis problems, but the utility of these models is restricted to their particular task domain, and expanding to broader use requires additional data, labels, and retraining efforts. Recently, foundation models (FMs) -machine learning models trained on large amounts of highly variable data thus enabling broad applicability -have emerged as promising tools for automated image analysis. Existing FMs for medical image analysis focus on scenarios and modalities where objects are clearly defined by visually apparent boundaries, such as surgical tool segmentation in endoscopy. X-ray imaging, by contrast, does not generally offer such clearly delineated boundaries or structure priors. During X-ray image formation, complex 3D structures are projected in transmission onto the imaging plane, resulting in overlapping features of varying opacity and shape. To pave the way toward an FM for comprehensive and automated analysis of arbitrary medical X-ray images, we develop FluoroSAM, a language-aligned variant of the Segment-Anything Model, trained from scratch on 1.6M synthetic X-ray images from a wide variety of human anatomies, X-ray projection geometries, energy spectra, and viewing angles. FluoroSAM is trained on data including masks for 128 organ types and 464 non-anatomical objects, such as tools and implants. In real X-ray images of cadaveric specimens, FluoroSAM is able to segment bony anatomical structures based on text-only prompting with 0.51 and 0.79 DICE with point-based refinement, outperforming competing SAM variants for all structures. FluoroSAM is also capable of zero-shot generalization to segmenting classes beyond the training set thanks to its language alignment, which we demonstrate for full lung segmentation on real chest X-rays. Code, data, and model weights are available. 1

In response to the rapid spread of the novel coronavirus, SARS-CoV-2, the U.S. has largely delegated implementation and rollback of non-pharmaceutical interventions (NPIs) to local governments on the state and county level. This asynchronous response combined with the heterogeneity of the U.S. complicates quantification of the effect of NPIs on the reproductive ratio of SARS-CoV-2 on a national level. We describe a data-driven approach to quantify the effect of NPIs that relies on county-level similarities to specialize a Bayesian mechanistic model based on observed fatalities. Using this approach, we estimate the effect of NPIs on the reproductive ratio R_t in 1,904 U.S. counties incorporating implementation, subsequent rollback, and mask mandate efficacy. We estimate that at some point before Aug 2nd, 2020, 1,808 out of the considered 1,904 U.S. counties had reduced the reproductive ratio of SARS-CoV-2 to below 1.0. However, on Aug 2nd, the reproductive ration remained below that threshold for only 702 counties. The estimated effect of any individual NPI is different across counties. Public school closings were estimated to be effective in metropolitan, urban, and suburban counties, while advisory NPIs were estimated to be effective in more rural counties. The cumulative prevalence predicted by the model ranges from 0 to 58.6% across the counties examined. The median is 2.6% while the 25th and 75th percentile are 1.3% and 44.6% respectively, indicating that most counties are far from herd immunity. Our results suggest that local conditions, including socioeconomic, demographic and infrastructural factors, in addition to the cumulative prevalence are pertinent to containment and re-opening decisions.