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GitHub Repository

“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…

GitHub Repository

“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.

When the first lockdown orders were passed in India and stay-at-home orders in California, many in my family dispersed across nations felt containment for the first time. An old couple had arrived to the US in December last year and could not leave now. I had planned to spend summer in Delhi with my family but that is not going to happen. It is too risky to be mobile. At the same time, our lives under lockdown are dependent on people being productive, at home or beyond. When I think about theorizing place and COVID19, I must take containment seriously. The moment reveals the inadequacy of concepts as containers, making the discursive gaps apparent (Fortun 2012) but leaving us flailing about as we meet each other, fingers-crossed. 

The clearest inadequacy is methodological nationalism (Wimmer & Schiller 2002): even as lockdowns have visibly occured across national borders, the transmission of virus through arteries of transnational industrial capitalism (some of it late, some not) and the privilege of transnational mobility point that as long as these infrastructures remain in place, so will this virus and more such to come. We continue to order things online, and Amazon continues to maintain these infrastructures. Public spaces are gradually opening with questionable safety norms in place. India, like other countries, is rescuing its citizens and bringing them back home, even as it continues to let migrant workers starve. 

There is consensus that things will not be as before, even as transnational mobilities continue to function. With enough PPE, fingers-crossed, everyone will be fine. What does it mean to take containment seriously, at a time when we are opening up? As things will continue to be normalized to our collective surprise and fatigue, this moment should mobilize us to think about different ways of organizing and care. These do not have to be new ways of thinking and doing but those that have blossomed in our lands for some time. 

In my annotation, I offer brief summaries of articles that animate my thinking about theorizing from confinement and that offer ways of doing already present: 

• Epidemics in American Concentration Camps: From the “White Plague” to COVID-19: Japanese Americans have formed the group Tsuru for Solidarity, calling for decarceration from prisons, jails, and detention centers. As these violent confined places become hotspots of infection, residents and descendants of residents of World War II concentration camps located across the US (most famously in Manzanar, California) recall accounts of epidemic management. Not surprisingly, the burden to remain healthy and disease-free was on detainees, which meant aggregating community and family resources when detainees were already deprived of livelihoods. As staffing problems arose during tuberculosis epidemic in 1940s, the hospital management even considered family members to take hospital shifts. 

• By Desperate Measures Relieved?: Public Health, Prisons, and the Politics of Life: Jason Ludwing writes about how notions of accelerating vaccine development for COVID19 through human "challenge trials" reminds him of medical experiments on incarcerated people in the US. Challenge trials depend on a volunteering body to take on the infection, but for people in prisons, the line blurs between a consenting body that volunteers and a coerced body that is sacrificed. He points to the prison-university complex  in collaboration between University of Maryland and Maryland Corrections in typhoid experiments based at Prison Volunteer Research Unit (PVRU) which launched many publications and research careers. The researchers frame those as ethical experiments because the male inmates received better accomodation and pay. Even though incarcerated populations will not be experimented upon during COVID, prison factories have remained open for producing PPE. Ludwig reminds us that this is not because of the moment, but an inevitable consequence of a system that deprives people of their bodies. 

• COVID-19, Biopolitics and Abolitionist Care Beyond Security and Containment: Eva Boodman argues that we must see beyond individual protection against microbes (biodefense) especially when it comes to people confined by coercion. Building from Foucault's biopolitics (make live/let die), Boodman sees this as continuation, not departure from what many groups have known all along: that the state and university is not for them. They know that we will keep getting messages of management and security as care. Boodman has a vision for abolitionist care, arguing that abolitionists over the years have assidously foregrounded racialized and class-ed neglect that COVID exacerbates and called for its end rather than thinking with. Abolitionist vision would mean calling an end for prisons, jails and all forms of carceration and in line with neglect of public health, an end to all for-profit nursing homes and treatment centers. It means to center mutual aid groups that have been working on-ground for a long time, and those that are built anew. It would mean for both to learn from each other. But mutual aid groups will also be careful to not be co-opted (as Black Panther Party's free breakfast program was co-opted by USDA), or serve as justification for further state neglect. Abolitionist care acknowledges that it will have to work temporarily with security apparatuses even as it continues to resist from inside. The end goal is not to settle for a liberal future.

• Beyond Inside/Outside: Imagining Safety During Covid-19: Author mobilizes her experience of leaving domestic abuse to think about living and working in confined domestic spaces. Feminized labor blurs inside/outside boundaries, revealed starkly by COVID. It is fatigued and exhausted but carries on. She says: "My experience of abuse was organized around waiting. Waiting for something bad to happen and then waiting for the bad thing to be over”. She says that the years of abuse live in her body. She was afraid to call for mediation because the police and state have worked to either criminalize or pass judgement on people like her. The work of transformative justice and prison abolition made her ask the question: why must we endure? Even though staying can be strategic, a way of survival, community can be elusive too. She offers the notion of "pod-building": does away with romantic ideas of community predicated upon shared identities and political analysis and pushes us to rely on relationship-building and trust with people we already know: that are reliable, have good boundaries and skills, which do not necessarily mirror our politics. This reconfiguration of care comes as she recognizes the link between intimate partner violence, gender-based violence, and prison-industrial complex that disrupted her healing and now animate her activism. 

• Working During COVID-19: Occupational Hazards and Workers’ Right to a Safe Workplace: A brief history of labor organizing around occupational safety and hazards and the role of ILO. To be recognized as occupational hazard, a worker in the American context must demonstrate that disease was contracted in place of work. For mining industry, the struggle to include silicosis and lung-based infections went on for decades and was successful but still requires heavy bureaucratic lifting. For petrochemical industries, this is even difficult as communities live in contamination, blurring home and work places. Workers in informal economy are even more precarious and face either starvation or contagion. As the ILO called for COVID to be recognized as a workplace hazard, could workers demand better conditions and from whom and how? The authors offer two examples from "occupied" factories, or those controlled by workers' assemblies: Rimaflow from Milan (Italy) and Traful Newen in Neuquen (Argentina). These workplaces implemented safety protocols much earlier than ordered by the state, and allowed older people, people with co-morbidity, and those who have domestic emergencies to stay at home with pay. Rather than decreasing production, these workplaces have seen an increase and created more jobs in a more ethical way.  

More reading: Care not Cages! #COVID19DecarcerateSyllabus

I've found myself returning to thinking about/around/within interstitial spaces of care, particularly within hospital settings, interested in how viral activity unsettles the ideas we have around space and boundaries, both biological and infrastructural. In COVID-19 pathology and response, the inbetween, the interstitial, become sites challenge and possibility. With COVID-19, we see an acknowledgment of once forgotten spaces quite obviously, with hospital atria and hallways being reconfigured into patient care spaces, makeshift morgues established in refrigerated trucks, and hospitals spilling out into neighboring streets and parks. More than ever, we see how hospitals are simultaneously bounded and unbounded--the most stable and unstable sites for care. Along this line of thought, what might thinking through hospitals as heterotopia of crisis and deviation afford?

Foucault outlines six principles for heterotopic spaces

The heterotopia is capable of juxtaposing in a single real place several spaces, several sites that are in themselves incompatible

Heterotopias are most often linked to slices in time—which is to say that they open onto what might be termed, for the sake of symmetry, heterochronies. The heterotopia begins to function at full capacity when men arrive at a sort of absolute break with their traditional time. This situation shows us that the cemetery is indeed a highly heterotopic place since, for the individual, the cemetery begins with this strange heterochrony, the loss of life, and with this quasi-eternity in which her permanent lot is dissolution and disappearance.

Heterotopias always presuppose a system of opening and closing that both isolates them and makes them penetrable. In general, the heterotopic site is not freely accessible like a public place. Either the entry is compulsory, as in the case of entering a barracks or a prison, or else the individual has to submit to rites and purifications.