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“To empower additional modeling efforts, the complete time series of all daily PVI scores and data are available at https://github.com/COVID19PVI/data. “

12 Key Indicators

“[The authors] assembled U.S. county- and state-level datasets into 12 key indicators across four major domains: current infection rates (infection prevalence, rate of increase), baseline population concentration (daytime density/traffic, residential density), current interventions (social distancing, testing rates), and health and environmental vulnerabilities (susceptible populations, air pollution, age distribution, comorbidities, health disparities, and hospital beds).”

Three types of modeling

“Our modeling efforts directly address the discussion in [6], by contextualizing factors such as racial differences with corrections for socioeconomic factors, health resource allocation, and co-morbidities, plus highlighting place- based risks and resource deficits that might explain spatial distributions. Specifically, three types of modeling efforts were performed and are regularly updated. First, epidemiological modeling on cumulative case- and death-related outcomes provides insights into the epidemiology of the pandemic. Second, dynamic time-dependent modeling provides similar outcome estimates as national-level models, but with county-level resolution. Finally, a Bayesian machine learning approach provides data-driven, short-term forecasts. “

Blackness and PM 2.5

“With respect to factors affecting COVID-19 related mortality, we find that the proportion of Black residents and the PM2.5 index of small-particulate air pollution are the most significant predictors among those included, reinforcing conclusions from previous reports[7]. An increase of one percentage point of Black residents is associated with a 3.3% increase in the COVID-19 death rate. The effect of a 1 g/m3 increase in PM2.5 is associated with an approximately 16% increase in the COVID-19 death rate, a value at the high end of a previously reported confidence interval from a report in late April 2020[7] when deaths had reached 38% of the current total.”

Machine learning and prediction

“To accurately predict future cases and mortality, it is necessary to account for the fluid nature of the data. Accordingly, we developed a Bayesian spatiotemporal random-effects model that jointly describes the log-observed and log-death counts to build local forecasts. Log-observed cases for a given day are predicted using known covariates (e.g., population density, social distancing metrics), a spatiotemporal random-effect smoothing component, and the time- weighted average number of cases for these counts. This smoothed time-weighted average is related to a Euler approximation of a differential equation; it provides modeling flexibility while approximating potential mechanistic models of disease spread. The smoothed case estimates are used in a similar spatiotemporal model predicting future log-death counts based on a geometric mean estimate of the estimated number of observed cases for the previous seven days as well as the other data streams. The resulting county-level predictions and corresponding confidence intervals are shown (Fig. 1)."

Source: https://www.researchgate.net/publication/343642027_The_COVID-19_Pandemi…

Skylar W. Marvel1, John S. House2, Matthew Wheeler2, Kuncheng Song1, Yihui Zhou1, Fred A. Wright1,3, Weihsueh A. Chiu4, Ivan Rusyn4, Alison Motsinger-Reif2*, David M. Reif1*

Affiliations:

1 Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA.

2 Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA.

3 Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA

4 Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77845, USA.

“Data sources in the current model (version 11.2.1) include the Social Vulnerability Index (SVI) of the Centers for Disease Control and Prevention (CDC) for emergency response and hazard mitigation planning (Horney et al. 2017), testing rates from the COVID Tracking Project (Atlantic Monthly Group 2020), social distancing metrics from mobile device data ( https://www.unacast.com/covid19/social-distancing-scoreboard), and dynamic measures of disease spread and case numbers ( https://usafacts.org/issues/coronavirus/). Methodological details concerning the integration of data streams—plus the complete, daily time series of all source data since February 2020 and resultant PVI scores—are maintained on the public Github project page (COVID19PVI 2020). Over this period, the PVI has been strongly associated with key vulnerability-related outcome metrics (by rank-correlation), with updates of its performance assessment posted with model updates alongside data at the Github project page (COVID19PVI 2020).”

Source: https://ehp.niehs.nih.gov/doi/10.1289/EHP8690

Empowering local actoors

“We present the PVI Dashboard as a dynamic container for contextualizing these disparities. It is a modular tool that will evolve to incorporate new data sources and analytics as they emerge (e.g., concurrent flu infections, school and business reopening statistics, heterogeneous public health practices). This flexibility positions it well as a resource for integrated prioritization of eventual vaccine distribution and monitoring its local impact. The PVI Dashboard can empower local and state officials to take informed action to combat the pandemic by communicating interactive, visual profiles of vulnerability atop an underlying statistical framework that enables the comparison of counties and the evaluation of the PVI’s component data.”

New Pandemic Vulnerability Index dashboard maps global spread of COVID-19 (Texas A&M, The Battalion, April 26, 2021

Centre County Commissioner's Proud of Community's Pandemic Response (WJAC TV, March 23rd, 2021)

Covid-19: how to prioritize worse-off populations in allocating safe and effective vaccines (October 5, 2020)

The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning (Jan 5, 2021)

The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: monitoring county level vulnerability (Aug 13, 2020)

Built with toxicology knowledge

“The software used to generate PVI scores and profiles from these data is freely available at https://toxpi.org”

General visualization capabilities

“The interactive visualization within the PVI Dashboard is intended to communicate factors underlying vulnerability and empower community action [...] The visualization and quantification of county-level vulnerability indicators are displayed by a radar chart, where each of the 12 indicators comprises a “slice” of the overall PVI profile. On loading, the Dashboard displays the top 250 PVI profiles (by rank) for the current day. The data, PVI scores, and predictions are updated daily, and users can scroll through historical PVI and county outcome data. Individual profiles are an interactive map layer with numerous display options/filters that include sorting by overall score, filtering by combinations of slice scores, clustering by profile similarity (i.e. vulnerability “shape”), and searching for counties by name or state (Additional functionality is detailed in the Supplement). User selection of any county overlays the summary Scorecard and populates surrounding panels with county- specific information (Figure 1). The scrollable panels at left include plots of vulnerability drivers relative to the nation-wide distribution across all U.S. counties, with the location of the selected county delineated. The panels across the bottom of the Dashboard report cumulative county numbers of cases and deaths; timelines of cumulative cases, deaths, PVI score, and PVI rank; daily changes in cases and deaths for the most recent 14-day period (commonly used in reopening guidelines[6]; and predicted cases and deaths for a 7-day forecast horizon.”

Visualizing comparison and "peer counties"

“the multi-criteria filtering capabilities in the Dashboard were used to find a “peer county” for comparison. “

Source: https://ehp.niehs.nih.gov/doi/10.1289/EHP8690 and https://www.researchgate.net/publication/343642027_The_COVID-19_Pandemi…

“It is difficult to imagine any of these studies exerting as much of an impact on public discourse and policy as they did if they had not been closely connected to litigation, advocacy, and regulatory interest in addressing the emerging issue of environmental justice.” (6)

“EJ scholarship has uncovered environmental and health disparities based not only on race, class, and gender, but also on ethnicity, nationality, indigenous status, immigration and citizenship status, sexual orientation, age, and the intersections among these categories (Nyseth-Brehm & Pellow, 2014; Chakraborty, Collins, & Grineski, 2016; Gaard, 2018). Activists are increasingly appealing to these diverse axes of identity to mobilize broad-based organizing on environmental, healthcare, and immigration policies (Hestres & Nisbet, 2018).” (9)

“In Europe, EJ is often seen as an extension of protections for human rights, including rights of access to environmental information, participation in decision making, and access to the courts, which are enshrined in the United Nations Economic Convention for Europe’s1998 Aarhus Convention (Mason, 2010). In the global South, EJ issues are more often framed as matters of climate justice, participatory and sustainable development and conservation, indigenous and women’s rights, food and energy sovereignty, workplace safety and health, or the environmentalism of the poor (Carmin & Agyeman, 2011; Carruthers, 2008; Martinez-Alier, 2002; Reed & George, 2018; Walker, 2012).” (10)

“The goals of community-engaged scholarship are the generation, exchange and application of mutually beneficial and socially useful knowledge and practices developed through active partnerships between the academy and the community (Engagement Scholarship Consortium, 2018).” (11)

“A more inclusive scholarly process is crucial for strengthening marginalized groups’ rights to access and create knowledge that can help build their power to influence regulation, policy, and institutional practices. ES is scholarship “done with, rather than for or on, a community” (Furco, 2005, p. 10), and this is reason alone to prefer ES to other modes of inquiry into EJ.” (15)

“Ensuring that map making is a democratic process owned and controlled by community members requires that local people, not outside researchers, define the geographic or other boundaries over what counts as part of the “community.””(29)

“EJ research can also ground-truth existing regulatory data that is out-of-date or incomplete, especially emissions data that is reported by industry. In addition, ground-truthing can show how environmental standards for broad geographic areas can fail to protect EJ communities from pollution hot spots that exceed those standards.” (31)

“Data scientists can also use large data sets and algorithms to develop new measures of environmental and social inequities. For example, a team led by researchers at the University of Minnesota recently created a “pollution inequity” metric, which measures the difference between the environmental health damage caused and experienced by a group or individual...” (33)

“While real-time analysis of crowdsourced data can help track the immediate effects of environmental disasters, it may not be as useful for documenting long-term, cumulative toxic exposures typical of many EJ issues. … Much of that expertise is concentrated in corporate, government, and academic institutions, which may be unable or unwilling to collaborate with community-based EJ organizations. EJ researchers could play a valuable role in helping to foster big data literacy…” (33)

“EJ storytelling is a means of gathering testimonial evidence for research and organizing (Evans, 2002). Stories are a grassroots form of making meaning that is often more accessible and immediate in its impacts than academic research, building commitment to collective action (Newman, 2012). Storytelling lends itself to communicating complex causality in a form that can be more memorable than scientific data (Griffiths, 2007).” (34)

In the “Introduction” and “Foundations” sections, the author describes the utility of an “engaged scholarship” approach to academic environmental justice research and outlines several models for engaged scholarship. These models lie along the spectra of the apolitical to the political, and include different types of development, types of engagement, and types of expertise. The author argues in favor of an engaged scholarship approach to EJ as a way to root EJ research in actual EiJ problems and EJ needs. Note that the author defines EJ with the four dimensions of distributive justice, procedural justice, process justice, and restorative/corrective justice.

The sections II. METHODS and III. CHALLENGES AND RESPONSES detail methods and potential pitfalls in engaged scholarship with local communities. Methods can include: investment in easy-to-use and low cost technologies for citizen science uses (e.g., online mapping tools, low cost air quality monitoring devices), using storytelling methods for cultural research and to advance EJ goals, and adequately training and preparing researchers for community collaborations (see Hyde (2017) framework on pg. 38). Pitfalls can include: scholars assuming homogeneity in a community, tensions between community goals and academic goals (e.g., scholarly productivity vs. community education), and limitations imposed  by academic IRBs for collaboration. The author provides several examples of community collaboration focus, with an apparent focus on citizen science/crowdsourced data collection efforts.

• The examples of community and participatory-based work in action can be used to talk about the importance of qualitative data and how to frame people without formal scientific training as experts and engaged researchers. 

• I am curious if theater of the oppressed has been used in STEM and/or environmental education? Found the following

• Liberatory Science Education: Weaving Connections Between Feminist Theory and Science Education

• Challenging Social Exclusion in Computing via 'Theatre of the Oppressed' Pedagogy | Proceedings of the 51st ACM Technical Symposium on Computer Science Education

• http://www.labtecgc.udesc.br/tabd1/handle/123456789/18315 

• Review of Drama in Science Education

• Raphale’s call for future research along the following lines: systems thinking, investigation into why things work and fail, how aspirations of collaborative projects are met and unmet, how indeterminacy and complexity characterize participatory action and research–are questions that I am interrogating through my research as well. 

• The table on p. 26 on “Levels of Community Participation in Research” naturally raises the question for the reader: Where on this continuum are we? 

• The concise overview of engaged scholarship models: how do they overlap with similar approaches in pedagogy?

• What political developments have shaped engaged scholarship? For example, neoliberal restructuring has appropriated CBPR for market-oriented research and strengthened corporate-humanitarian networks rather than developing community capacities. 

• I want to think more about the idea of the timeline of community-university partnerships: are there benefits to short-term partnerships as well? Do all partnerships need to be sustainable to be mutually beneficial and meaningful?