More Like this

1. Participatory COVID-19 Surveillance Tool in Rural Appalachia: Real-Time Disease Monitoring and Regional Response

   https://doi.org/10.1177/0033354921990372  

   Runkle, Jennifer_D ; Sugg, Maggie_M ; Graham, Garrett ; Hodge, Bryan ; March, Terri ; Mullendore, Jennifer ; Tove, Fletcher ; Salyers, Martha ; Valeika, Steve ; Vaughan, Ellis ( February 2021 , Public Health Reports®)

   Few US studies have examined the usefulness of participatory surveillance during the coronavirus disease 2019 (COVID-19) pandemic for enhancing local health response efforts, particularly in rural settings. We report on the development and implementation of an internet-based COVID-19 participatory surveillance tool in rural Appalachia.

   A regional collaboration among public health partners culminated in the design and implementation of the COVID-19 Self-Checker, a local online symptom tracker. The tool collected data on participant demographic characteristics and health history. County residents were then invited to take part in an automated daily electronic follow-up to monitor symptom progression, assess barriers to care and testing, and collect data on COVID-19 test results and symptom resolution.

   Nearly 6500 county residents visited and 1755 residents completed the COVID-19 Self-Checker from April 30 through June 9, 2020. Of the 579 residents who reported severe or mild COVID-19 symptoms, COVID-19 symptoms were primarily reported among women (n = 408, 70.5%), adults with preexisting health conditions (n = 246, 70.5%), adults aged 18-44 (n = 301, 52.0%), and users who reported not having a health care provider (n = 131, 22.6%). Initial findings showed underrepresentation of some racial/ethnic and non–English-speaking groups.

   This low-cost internet-based platform provided a flexible means to collect participatory surveillance data on local changes in COVID-19 symptoms and adapt to guidance. Data from this tool can be used to monitor the efficacy of public health response measures at the local level in rural Appalachia.

    

   more » « less
2. Surveillance and the experience of the COVID-19 pandemic for formerly incarcerated individuals

   https://doi.org/10.1177/14624745221080696  

   Vuolo, Mike ; Schneider, Lesley E. ; LaPlant, Eric G. ( February 2022 , Punishment & Society)

   To date, most criminal justice research on COVID-19 has examined the rapid spread within prisons. We shift the focus to reentry via in-depth interviews with formerly incarcerated individuals in central Ohio, specifically focusing on how criminal justice contact affected the pandemic experience. In doing so, we use the experience of the pandemic to build upon criminological theories regarding surveillance, including both classic theories on surveillance during incarceration as well as more recent scholarship on community surveillance, carceral citizenship, and institutional avoidance. Three findings emerged. First, participants felt that the total institution of prison “prepared” them for similar experiences such as pandemic-related isolation. Second, shifts in community supervision formatting, such as those forced by the pandemic, lessened the coercive nature of community supervision, expressed by participants as an increase in autonomy. Third, establishment of institutional connections while incarcerated alleviated institutional avoidance resulting from hyper-surveillance, specifically in the domain of healthcare, which is critical when a public health crisis strikes. While the COVID-19 pandemic affected all, this article highlights how theories of surveillance inform unique aspects of the pandemic for formerly incarcerated individuals, while providing pathways forward for reducing the impact of surveillance.

    

   more » « less
3. County augmented transformer for COVID-19 state hospitalizations prediction

   https://doi.org/10.1038/s41598-023-36378-9  

   Er, Siawpeng ; Yang, Shihao ; Zhao, Tuo ( December 2023 , Scientific Reports)

   The prolonged COVID-19 pandemic has tied up significant medical resources, and its management poses a challenge for the public health care decision making. Accurate predictions of the hospitalizations are crucial for the decision makers to make informed decision for the medical resource allocation. This paper proposes a method named County Augmented Transformer (CAT). To generate accurate predictions of four-week-ahead COVID-19 related hospitalizations for every states in the United States. Inspired by the modern deep learning techniques, our method is based on a self-attention model (known as the transformer model) that is actively used in Natural Language Processing. Our transformer based model can capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model is a data based approach that utilizes the publicly available information including the COVID-19 related number of confirmed cases, deaths, hospitalizations data, and the household median income data. Our numerical experiments demonstrate the strength and the usability of our model as a potential tool for assisting the medical resources allocation.

    

   more » « less
4. From 5Vs to 6Cs: Operationalizing Epidemic Data Management with COVID-19 Surveillance

   https://doi.org/10.1109/BigData50022.2020.9378435  

   Peddireddy, Akhil Sai ; Xie, Dawen ; Patil, Pramod ; Wilson, Mandy L. ; Machi, Dustin ; Venkatramanan, Srinivasan ; Klahn, Brian ; Porebski, Przemyslaw ; Bhattacharya, Parantapa ; Dumbre, Shirish ; et al ( December 2020 , 2020 IEEE International Conference on Big Data (Big Data))

   Abstract—The COVID-19 pandemic brought to the forefront an unprecedented need for experts, as well as citizens, to visualize spatio-temporal disease surveillance data. Web application dashboards were quickly developed to fill t his g ap, b ut a ll of these dashboards supported a particular niche view of the pandemic (ie, current status or specific r egions). I n t his paper, we describe our work developing our COVID-19 Surveillance Dashboard, which offers a unique view of the pandemic while also allowing users to focus on the details that interest them. From the beginning, our goal was to provide a simple visual tool for comparing, organizing, and tracking near-real-time surveillance data as the pandemic progresses. In developing this dashboard, we also identified 6 key metrics which we propose as a standard for the design and evaluation of real-time epidemic science dashboards. Our dashboard was one of the first r eleased t o the public, and continues to be actively visited. Our own group uses it to support federal, state and local public health authorities, and it is used by individuals worldwide to track the evolution of the COVID-19 pandemic, build their own dashboards, and support their organizations as they plan their responses to the pandemic. 

   more » « less
5. Examining School Accountability: Discriminating Schools' A-F Report Card Grades

   Folger, T. ; Roberts, A. ; Bostic, J. ( October 2022 , Midwestern educational researcher)

   The COVID-19 pandemic disrupted many school accountability systems that rely on student-level achievement data. Many states encountered uncertainty about how to meet federal accountability requirements without typical school data. Prior research provides evidence that student achievement is correlated to students’ social background, which raises concerns about the predictive bias of accountability systems. This mixed-methods study (a) examines the predictive ability of non-achievement-based variables (i.e., students’ social background) on school districts’ report card letter grade in Ohio, and (b) explores educators’ perceptions of report card grades. Results suggest that social background and community demographic variables have a significant impact on measures of school accountability. 

   more » « less