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1. A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States

   https://doi.org/10.1038/s41598-021-84145-5  

   Güemes, Amparo; Ray, Soumyajit; Aboumerhi, Khaled; Desjardins, Michael R.; Kvit, Anton; Corrigan, Anne E.; Fries, Brendan; Shields, Timothy; Stevens, Robert D.; Curriero, Frank C.; et al (December 2021, Scientific Reports)

   Abstract Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control. 

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2. Learning Outcomes and Insights from a Chocolate-based Undergraduate Materials Science Course and Other Topical Outreach Activities

   https://doi.org/10.1557/adv.2017.635  

   Dailey, Jennifer (January 2018, MRS Advances)

   ABSTRACT Those involved in STEM outreach, from elementary schools through undergraduate students, all use varying teaching styles in an effort to instruct and inspire students. However, it is incredibly difficult to gauge or compare learning outcomes from new teaching techniques in situ. In this work, we describe the outcomes of a new undergraduate mini-course at Johns Hopkins University, Chocolate: An Introduction to Materials Science. In particular, the outcomes of teaching binary phase diagrams in this course using topical food examples were compared to the outcomes of the same instructor teaching a similar control group of students using standard textbook examples, reducing a number of confounding factors and allowing us to objectively analyze the benefits of using an atypical, popular approach to teach a standard subject. Results indicate that the students in the Chocolate course were not only more excited and engaged in the lecture, but they had identical or potentially greater learning gains than the control group. 

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3. OceanSpy: A Python package to facilitate ocean model data analysis and visualization

   https://doi.org/10.21105/joss.01491  

   Mattia Almansi, Renske Gelderloos (January 2019, Journal of open source software)

   OceanSpy is an open-source and user-friendly Python package that enables scientists and interested amateurs to analyze and visualize oceanographic data sets. OceanSpy builds on software packages developed by the Pangeo community, in particular Xarray (Hoyer & Hamman, 2017), Dask (Dask Development Team, 2016), and Xgcm (“Xgcm,” n.d.). The integration of Dask facilitates scalability, which is important for the petabyte-scale simulations that are becoming available. OceanSpy can be used as a standalone package for analysis of local circulation model output, or it can be run on a remote data-analysis cluster, such as the Johns Hopkins University SciServer system (Medvedev, Lemson, & Rippin, 2016), which hosts several simulations and is publicly available. OceanSpy enables extraction, processing, and visualization of model data to (i) compare with oceanographic observations, and (ii) portray the kinematic and dynamic space-time properties of the circulation. 

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4. Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks

   https://doi.org/10.1371/journal.pone.0264537  

   Craley, Jeff; Jouny, Christophe; Johnson, Emily; Hsu, David; Ahmed, Raheel; Venkataraman, Archana (February 2022, PLOS ONE)

   Bernhardt, Boris C (Ed.)

   We propose a novel neural network architecture, SZTrack, to detect and track the spatio-temporal propagation of seizure activity in multichannel EEG. SZTrack combines a convolutional neural network encoder operating on individual EEG channels with recurrent neural networks to capture the evolution of seizure activity. Our unique training strategy aggregates individual electrode level predictions for patient-level seizure detection and localization. We evaluate SZTrack on a clinical EEG dataset of 201 seizure recordings from 34 epilepsy patients acquired at the Johns Hopkins Hospital. Our network achieves similar seizure detection performance to state-of-the-art methods and provides valuable localization information that has not previously been demonstrated in the literature. We also show the cross-site generalization capabilities of SZTrack on a dataset of 53 seizure recordings from 14 epilepsy patients acquired at the University of Wisconsin Madison. SZTrack is able to determine the lobe and hemisphere of origin in nearly all of these new patients without retraining the network . To our knowledge, SZTrack is the first end-to-end seizure tracking network using scalp EEG. 

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5. Comparing and integrating US COVID-19 data from multiple sources with anomaly detection and repairing

   https://doi.org/10.1080/02664763.2021.1928016  

   Wang, Guannan; Gu, Zhiling; Li, Xinyi; Yu, Shan; Kim, Myungjin; Wang, Yueying; Gao, Lei; Wang, Li (July 2021, Journal of Applied Statistics)

   null (Ed.)

   Over the past few months, the outbreak of Coronavirus disease (COVID-19) has been expanding over the world. A reliable and accurate dataset of the cases is vital for scientists to conduct related research and policy-makers to make better decisions. We collect the United States COVID-19 daily reported data from four open sources: the New York Times, the COVID-19 Data Repository by Johns Hopkins University, the COVID Tracking Project at the Atlantic, and the USAFacts, then compare the similarities and differences among them. To obtain reliable data for further analysis, we first examine the cyclical pattern and the following anomalies, which frequently occur in the reported cases: (1) the order dependencies violation, (2) the point or period anomalies, and (3) the issue of reporting delay. To address these detected issues, we propose the corresponding repairing methods and procedures if corrections are necessary. In addition, we integrate the COVID-19 reported cases with the county-level auxiliary information of the local features from official sources, such as health infrastructure, demographic, socioeconomic, and environmental information, which are also essential for understanding the spread of the virus. 

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