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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…

“ After extensive hurricane damage fueled in part by a warmer atmosphere and warmer, higher seas, communities in Texas are considering ways to rebuild more resilient infra- structure. In the U.S. Caribbean, govern- ments are developing new frameworks for storm recovery based on lessons learned from the 2017 hurricane season.” (34)

“​​However, Harvey’s total rainfall was likely compounded by warmer surface water temperatures feeding the direct deep tropical trajectories historically associated with extreme precipitation in Texas, and these warmer temperatures are partly attributable to human-induced climate change. Initial analyses suggest that the human- influenced contribution to Harvey’s rainfall that occurred in the most affected areas was significantly greater than the 5% to 7% increase expected from the simple thermodynamic argument that warmer air can hold more water vapor. One study estimated total rainfall amount to be increased as a result of human-induced climate change by at least 19% with a best estimate of 38%, and another study found the three-day rainfall to be approximately 15% more intense and the event itself three times more likely.” (95)

“​​For example, in the Nebraska part of the northern High Plains, small water-table rises occurred in parts of this area, and the net depletion was negligible. In contrast, in the Texas part of the southern High Plains, development of groundwater resources was more extensive, and the depletion rate averaged 1.6 km3/year.” (160)

“In the Southeast (Atlantic and Gulf Coasts), power plants and oil refineries are especially vulnerable to flooding…Nationally, a sea level rise of 3.3 feet (1 m; at the high end of the very likely range under a lower scenario [RCP4.5] for 2100) (for more on RCPs, see the Scenario Products section in App. 3)47 could expose dozens of power plants that are currently out of reach to the risks of a 100-year flood (a flood having a 1% chance of occurring in a given year). This would put an additional cumulative total of 25 gigawatts (GW) of oper- ating or proposed power capacities at risk.48 In Florida and Delaware, sea level rise of 3.3 feet (1 m) would double the number of vulnerable plants (putting an additional 11 GW and 0.8 GW at risk in the two states, respectively); in Texas, vulnerable capacity would more than triple (with an additional 2.8 GW at risk).” (180)

“The Southern Great Plains, composed of Kansas, Oklahoma, and Texas, experiences weather that is dramatic and consequential. Hurricanes, flooding, severe storms with large hail and tornadoes, blizzards, ice storms, relentless winds, heat waves, and drought—its people and economies are often at the mercy of some of the most diverse and extreme weather hazards on the planet. These events cause significant stress to existing infrastructure and socioeconomic systems and can result in significant loss of life and the loss of billions of dollars in property.” (991)

“With the Gulf of Mexico to its southeast, the coastal Southern Great Plains is vulnerable to hurricanes and sea level rise. Relative sea level rise along the Texas Gulf Coast is twice as large as the global average, and an extreme storm surge in Galveston Bay would threaten much of the U.S. petroleum and natural gas refining capacity.” (992)

“The Southern Great Plains ranks near the top of states with structurally deficient or functionally obsolete bridges, while other bridges are nearing the end of their design life.16,17,18 Road surface degradation in Texas urban centers is linked to an extra $5.7 billion in vehicle operating costs annually (dollar year not reported).15 The region has tens of thousands of dams and levees; however, many are not subject to regular inspection and maintenance and have an average age exceeding 40 years.” (995)

“Along the Texas coastline, sea levels have risen 5–17 inches over the last 100 years, depending on local topography and subsidence (sinking of land).25 Sea level rise along the western Gulf of Mexico during the remainder of the 21st century is likely to be greater than the projected global average of 1–4 feet or more.26 Such a change, along with the related retreat of the Gulf coastline,27 will exacerbate risks and impacts from storm surges.” (996)

“Superimposed on the existing complexities at the intersection of food, energy, and water is the specter of climate change. During 2010–2015, the multiyear regional drought severely affected both agricultural and aquatic ecosystems. One prominent impact was a reduction of irrigation water released for the Texas Rice Belt farmers on the Texas coastal plains, as well as a reduction in the amount of water available to meet instream flow needs in the Colorado River and freshwater inflow needs to Matagorda Bay.” (997)

“The 2017 Texas State Water Plan52 indicates that the growing Texas population will result in a 17% increase in water demand in the state over the next 50 years. This increase is project- ed to be primarily associated with municipal use, manufacturing, and power generation, owing to the projections of population increase in the region.”  (1001)

[See Edwards Aquifer case study on pg. 1002.]

“Between 1982 and 2012, 82 dams failed in Texas, and during 2015 the high-hazard Lew- isville Dam was of concern due to observed seepage.” (1005)

“Within Texas alone, 1,000 square miles of land is within 5 feet of the high tide line, including $9.6 billion in current assessed property value and homes to about 45,000 people. Sensitive assets include 1,600 miles of roadway, several hospitals and schools, 4 power plants, and 254 EPA-listed contamination sites (hazardous waste and sewage).100 Up to $20.9 billion in coastal prop- erty is projected to be flooded at high tide by 2030, and by 2050, property values below the high-water mark are projected to be in excess of $30 billion, assuming current trends of greenhouse gas emissions.” (1005)

“Saltwater intrusion of aquifers has been observed in the Gulf Coast Aquifer, the second most utilized aquifer in Texas, which supports 8 million people. Although this was in part associated with heavy pumping, the Gulf Coast Aquifer remains vulnerable to further saltwater intrusion resulting from SLR and storm surge exacerbated by climate change.” (1006)

“In August 2016, a historic flood resulting from 20 to 30 inches of rainfall over several days devastated a large area of southern Louisiana, causing over $10 billion in damages and 13 deaths. More than 30,000 people were rescued from floodwaters that damaged or destroyed more than 50,000 homes, 100,000 vehicles, and 20,000 businesses. In June 2016, torrential rainfall caused destructive flooding throughout many West Virginia towns, damaging thousands of homes and businesses and causing considerable loss of life. More than 1,500 roads and bridges were damaged or destroyed. The 2015–2016 El Niño poured 11 days of record-setting rainfall on Hawai‘i, causing severe urban flooding.” (67)

“Increases in baseline sea levels expose many more Gulf Coast refineries to flooding risk during extreme weather events. For example, given a Category 1 hurricane, a sea level rise of less than 1.6 feet (0.5 m)47 doubles the number of refineries in Texas and Louisiana vulnerable to flooding by 2100 under the lower scenario (RCP4.5).” (181)

“Many urban locations have experienced disruptive extreme events that have impacted the transportation network and led to societal and economic consequences. Louisiana experienced historic floods in 2016 that disrupted all modes of transportation and caused adverse impacts on major industries and businesses due to the halt of freight movement and employees’ inability to get to work. The 2016 floods that affected Texas from March to June resulted in major business disruption due to the loss of a major transportation corridor.147 In 2017, Hurricane Harvey affected population and freight mobility in Houston, Texas, when 23 ports were closed and over 700 roads were deemed impassable.” (498)

“​​Communities in Louisiana and New Jersey, for example, are already experiencing a host of negative environmental exposures coupled with extreme coastal and inland flooding.” (548)

“An example of the effects of rising sea levels can be found in Louisiana, which faces some of the highest land loss rates in the world. The ecosystems of the Mississippi River Delta provide at least $12–$47 billion (in 2017 dollars) in benefits to people each year.155 These benefits include hurricane storm protection, water supply, furs, habitat, climate stability, and waste treatment. However, between 1932 and 2016, Louisiana lost 2,006 square miles of land area (see Case Study “A Lesson Learned for Community Resettlement”),211 due in part to high rates of relative sea level rise” (775)

“The flood events in Baton Rouge, Louisiana, in 2016 and in South Carolina in 2015 provide real examples of how vulnerable inland and coastal communities are to extreme rainfall events.” (785)

“Hurricane Harvey was a Category 4 hurricane on the Saffir–Simpson scale when it made landfall on the central Texas coast near Rockport late in the evening of August 25, 2017. It then moved inland, stalled, and eventually moved back over the coastal Gulf of Mexico waters before making landfall a final time as a tropical storm several days later in southwestern Louisiana.” (992)

“The State of Louisiana’s Coastal Protection and Restoration Authority’s 2017 Coastal Master Plan has more than 100 struc- tural and coastal restoration projects designed to provide benefits over the next decade and up to 50 years into the future.” (1320)

“Louisiana’s Comprehensive Master Plan for a Sustainable Coast has five broad objectives: reduce economic losses from flooding, promote sustainable coastal ecosystems, provide coastal habitats that support commerce and recreation, sustain the region’s unique cultural heritage, and contribute to the regional and national economy by promoting a viable working coast. The plan contains actions  that advance all five objectives, reflecting a set of tradeoffs broadly acceptable to diverse communities in the face of hazards, including coastal subsidence (sinking land) and sea level rise.” (1323)

Ch. 23, Southern Great Plains (Texas): This chapter provides five (four listed below) key messages about the climate of and climate change in the southern great plains region:

1. Food, energy, water resources - Changes in water supply due to climate change are intersecting with changes in water demand due to food, water, and energy consumption. 

2. Infrastructure - the built environment is vulnerable to climate change. Along the gulf coast of Texas, sea level rise in the coming years is a major concern. 

3. Ecosystems and ecosystem services - aquatic ecosystems are impacted by extreme weather events. Not all aquatic species can adapt. 

4. Human health - Increased temperatures that cause disease transmission and an increase in extreme events that cause injury and displacement are projected in the coming years. 

Ch. 19, Southeast (Louisiana): This chapter provides four (two listed below) key messages about the climate of and climate change in the southeastern U.S.:

1. Urban infrastructure and health risks - Cities in the southeast are particularly vulnerable to heat, flooding, and disease risk due to climate change. 

2. Increasing flood risks in coastal and low-lying regions - Low lying regions are susceptible to flooding due to extreme rainfall and sea level rise.