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Child birth via Cesarean section accounts for approximately 32% of all births each year in the United States. A variety of risk factors and complications can lead caregivers and patients to plan for a Cesarean delivery in advance before onset of labor. However, a non-trivial subset of Cesarean sections (∼25%) are unplanned and occur after an initial trial of labor is attempted. Unfortunately, patients who deliver via unplanned Cesarean sections have increased maternal morbidity and mortality rates and higher rates of neonatal intensive care admissions. In an effort to develop models aimed at improving health outcomes in labor and delivery, this work seeks to explore the use of national vital statistics data to quantify the likelihood of an unplanned Cesarean section based on 22 maternal characteristics. Machine learning techniques are used to ascertain influential features, train and evaluate models, and assess accuracy against available test data. Based on cross-validation results from a large training cohort ( n = 6,530,467 births), the gradient-boosted tree algorithm was identified as the best performer and was evaluated on a large test cohort ( n = 10,613,877 births) for two prediction scenarios. Area under the receiver operating characteristic curves of 0.77 or higher and recall scores of 0.78 or higher were obtained and the resulting models are well calibrated. Combined with feature importance analysis to explain why certain maternal characteristics lead to a specific prediction in individual patients, the developed analysis pipeline provides additional quantitative information to aid in the decision process on whether to plan for a Cesarean section in advance, a substantially safer option among women at a high risk of unplanned Cesarean delivery during labor. 

Evidence suggests that signatures of health and disease, or digital biomarkers, exist within the heterogeneous, temporally-dense data gathered from smartphone sensors and wearable devices that can be leveraged for medical applications. Modern smartphones contain a collection of energy-efficient sensors capable of capturing the device’s movement, orientation, and location as well characteristics of its external environment (e.g. ambient temperature, sound, pressure). When paired with peripheral wearable devices like smart watches, smartphones can also facilitate the collection/aggregation of important vital signs like heart rate and oxygen saturation. Here we discuss our recent experiences with deploying an open-source, cloud-native framework to monitor and collect smartphone sensor data from a cohort of pregnant women over a period of one year. We highlight two open-source integrations into the pipeline we found particularly useful: 1) a dashboard–built with Grafana and backed by Graphite–to monitor and manage production server loads and data collection metrics across the study cohort and 2) a back-end storage solution with InfluxDB, a multi-tenant time series database and data exploration ecosystem, to support biomarker discovery efforts of a multidisciplinary research team. 

Supporting Pacific Indigenous Computing Excellence (SPICE) is based on unique expertise and proven models established through a partnership between the Texas Advanced Computing Center at the University of Texas at Austin, Chaminade University of Honolulu and Georgia Institute of Technology (Georgia Tech). The SPICE program leverages shared partnership experiences to address two goals: 1) Perform original research and program development to bridge computation and culture -- developing culturally-consistent conceptual and practical frameworks for thinking about big data problems and communicating student outcomes and attainment to family, community and kupuna (Hawaiian wisdom figures); and 2) Implement an in situ Data Science, Analytics and Visualization (DSAV) Summer Immersion Experience (SIE) as a summer program in Hawai‘i to provide a month-long summer immersion program in data science, visualization, and virtual reality to Native Hawaiian and Pacific Islander (NHPI) and disadvantaged students. In this paper, we present the framework for this effort, with relevant educational, and cultural research to justify decisions made to date. 

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages. 

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.