key: cord-0504104-3dk9sgru
authors: Tiwari, Anuj; Dadhania, Arya V.; Ragunathrao, Vijay Avin Balaji; Oliveira, Edson R. A.
title: Using Machine Learning to Develop a Novel COVID-19 Vulnerability Index (C19VI)
date: 2020-09-22
journal: nan
DOI: nan
sha: b619c67e2f1a471957bcd071f12895f0548c926e
doc_id: 504104
cord_uid: 3dk9sgru

COVID19 is now one of the most leading causes of death in the United States. Systemic health, social and economic disparities have put the minorities and economically poor communities at a higher risk than others. There is an immediate requirement to develop a reliable measure of county-level vulnerabilities that can capture the heterogeneity of both vulnerable communities and the COVID19 pandemic. This study reports a COVID19 Vulnerability Index (C19VI) for identification and mapping of vulnerable counties in the United States. We proposed a Random Forest machine learning based COVID19 vulnerability model using CDC sociodemographic and COVID19-specific themes. An innovative COVID19 Impact Assessment algorithm was also developed using homogeneity and trend assessment technique for evaluating severity of the pandemic in all counties and train RF model. Developed C19VI was statistically validated and compared with the CDC COVID19 Community Vulnerability Index (CCVI). Finally, using C19VI along with census data, we explored racial inequalities and economic disparities in COVID19 health outcomes amongst different regions in the United States. Our C19VI index indicates that 18.30% of the counties falls into very high vulnerability class, 24.34% in high, 23.32% in moderate, 22.34% in low, and 11.68% in very low. Furthermore, C19VI reveals that 75.57% of racial minorities and 82.84% of economically poor communities are very high or high COVID19 vulnerable regions. The proposed approach of vulnerability modeling takes advantage of both the well-established field of statistical analysis and the fast-evolving domain of machine learning. C19VI provides an accurate and more reliable way to measure county level vulnerability in the United States. This index aims at helping emergency planners to develop more effective mitigation strategies especially for the disproportionately impacted communities.

The novel coronavirus disease (COVID -19) has been recognized as the newest and biggest global public health crisis 1 . In the first half of 2020, the COVID-19 has nearly killed half a million people worldwide of which more a quarter of them have died in the United States of America 2 . Within three months after the first reported case in the United States in January 2020, coronavirus cases had been confirmed in all fifty states, including the District of Columbia and other inhabited U.S. territories 2 . Shortly after, many states-imposed policies to curb the spread 3, 4 . Despite these diffused efforts, a massive surge in incidence and mortality began to recur in June 2020 that placed the U.S. as the most affected country setting apart from other counties by a huge margin 2, 5 . This has clearly indicated a lapse in effective COVID risk assessment and response at different levels. With the added concern of the second wave and disproportionate impact of the pandemic on minorities and economically poor, reliable country-wide assessment of COVID-19 vulnerability is a matter of necessity and urgency 6 .

Identification of vulnerable areas is critical for public health departments to take the appropriate measures to increase preparedness against COVID- 19 . In order to identify these areas, the Centers for Disease Control and Prevention (CDC) initially used the social vulnerability index (SVI) 7 , which is calculated based on census variables distributed in four distinct themes: i) socio-economic factors, ii) household composition, iii) minority status and language, and iv) access to housing and transportation. SVI failed to sufficiently determine vulnerability during this unprecedented situation, largely due to its primary objective aimed at addressing natural disaster crises during hurricanes, earthquakes, and forest fires 8, 9 . Thus, the CDC and Surgo Foundation developed COVID-19 Community Vulnerability Index (CCVI) 10 by introducing two new variables, v) epidemiological risk factors and vi) public health system capacity, in the hope to rectify the shortcomings in the previous vulnerability assessment technique. Despite this optimization, CCVI and SVI are based on a statistical linear algorithm 7,10 that is unable to sufficiently account for the multiplicative, non-linear nature of vulnerability 11 . The lack of a comprehensive and accurate COVID-19 vulnerability index impairs the preparedness of critical areas against the pandemic as they are blurred to public health measures. This scenario highlights the urgent need for improvements of the nationwide approaches to identify vulnerable areas amid COVID -19 pandemic.

In the current study, we developed a more reliable assessment, the COVID-19 Vulnerability Index (C19VI) which quantifies the pandemic vulnerability of each United States county. This relative index processed the same six input variables as CCVI, however, instead of using a statistical linear algorithm, we utilized machine learning technique. We implemented Random Forest (RF) machine learning technique to calculate C19VI. An innovative 'COVID-19 Impact Assessment' algorithm was also developed using homogeneity analysis and temporal trend assessment techniques for training the RF model. Next, our C19VI index was compared with CDC's CCVI using advanced statistical measures and a machine learning model. We then tested the accuracy and checked the internal consistency of the C19VI.

Our vulnerability assessment methodology has allowed us to analyze the impact of COVID-19 that has been unequal yet widespread across the nation [12] [13] [14] [15] . Besides, there are systemic socioeconomic inequalities that increase the susceptibility and exposure of the marginalized groups 13, 15, 16 . Thus, in addition to conducting a nationwide analysis of COVID-19 vulnerability, C19VI has allowed us to explore the existing healthcare disparities in the realm of COVID-19 pandemic in great detail. This study may enhance the current techniques in vulnerability modeling, leveraging the preparedness of vulnerable counties to reduce the COVID-19 burden within the United States.

We used publicly available datasets from Johns Hopkins University 2 , Centers for Disease Control and Prevention (CDC) 10 , United States Census Bureau 17 , and United States Department of Homeland Security 18 for impact assessment, vulnerability modeling, population-specific vulnerability analysis, and data visualization and mapping, respectively. The data for COVID-19 confirmed cases and mortality in the United States from 22 nd January 2020 -31 st July 2020 were obtained from Johns Hopkins University. Figure 1 (A) and 1(B) presents the normalized (per 100,000) total confirmed cases and deaths dataset maps for all United States counties, respectively.

The four socio-demographic SVI indicators and two CCVI thematic indicators, referred to as input themes were obtained from CDC. This results in six input themes that determine the vulnerability of a region to COVID-19 pandemic (Table 1 ). Figure 2 States. County wise total population, racial population and poverty breakdown were obtained from the United States Census Bureau. Regional boundary data of the United States was collected from Homeland infrastructure foundation-level data 18 in Geographic Information System (GIS) ready file format (ESRI shapefile 19 ). This study did not require a review by the Institutional Review Board since publicly available, de-identified data was used.

In order to understand the impact of COVID-19 pandemic in all 3142 counties in the United States, we have proposed a 'COVID-19 Impact Assessment' algorithm. This algorithm 'Scores' and 'Ranks' the impact of COVID-19 pandemic by evaluating the change with respect to time and severity in confirmed cases, deaths, and infection fatality rate (IFR) 20 using trend analysis (Mann Kendall 21, 22 & Theil and Sen Slope 23, 24 ) and homogeneity assessment (Pettitt's test 25 ) . Trend analysis characterizes the overall pattern in daily time series dataset and homogeneity assessment identifies abrupt changes in temporal trends [21] [22] [23] [24] [25] . Thus, the algorithm classifies each county in one of the six impact groups, 'very high' (Rank = 1), 'high' (Rank = 2), 'moderate' (Rank = 3), 'low' (Rank = 4), 'very low' (Rank = 5) and 'non-significant' (Rank = -999). See supplementary material for the COVID-19 Impact Assessment Algorithm pseudocode.

The algorithm functions in four steps:

1. Data import and pre-processing: County-wise, daily time-series data of the confirmed cases and deaths were obtained from the John Hopkins University as mentioned above 2 .

Then, daily time-series data for IFR is calculated using the imported datasets.

Pettitt's test 25 was applied county-wise to check for the homogeneity in the time-series dataset of all three epidemiological parameters obtained after step 1. If the data was found to be non-homogeneous, pre and post changepoint time series were computed and kept alongside the 'overall' dataset, which was the only populated data column in the cases of homogenous datasets. This expanded the time-series dataset into three aspects, i.e. pre changepoint, post changepoint, and overall, for each of the three epidemiological parameters, i.e. confirmed cases, deaths, and IFR for each county.

We applied Mann Kendall's test 21, 22 to assess the trend and its nature, i.e. increasing, decreasing, or no trend, in a given time-series. Next, the trend magnitude was quantified using the Theil and Sen slope estimator test 23, 24 . Mann Kendall's, and Theil and Sen Slope estimator test was performed on all three time-series computed at the end of step 2 for all three epidemiological parameters in each county.

Impact 'Score' and 'Rank' determination: Impact Score was determined using the trend magnitude data obtained from the previous step. We used IFR as the most important parameter for assessing the impact of the COVID-19 pandemic in our algorithm 20, 26 . In the instances where IFR did not show a significant trend in a given county, we first used the deaths 26 . If the deaths did not show a significant trend either, confirmed cases were used to evaluate the impact of the pandemic 26 . Thus, rank classification occurred in three stages, each further divided according to the homogeneity results:

a. On the basis of the IFR:

i. In a homogeneous IFR time-series with an increasing 'overall' trend, the county was assigned Rank 1 and its impact Score was equal to the 'overall' trend magnitude.

ii. In a non-homogeneous IFR time-series with an increasing pre-changepoint trend, the scoring and ranking were specified based on the post-changepoint trend. Counties with increasing post-changepoint trends were classified as i. In a homogeneous death time-series with an increasing 'overall' trend, the county was assigned Rank 2 and its impact Score was equal to the 'overall' trend magnitude.

ii. In a non-homogeneous death time-series with an increasing prechangepoint trend, the scoring and ranking were specified based on the post- c. On the basis of the confirmed cases:

i. In a homogeneous time-series of confirmed cases with an increasing 'overall' trend, the county was assigned Rank 4 and its impact Score was equal to the 'overall' trend magnitude.

ii. In a non-homogeneous time-series of confirmed cases with an increasing pre-changepoint trend, the scoring and ranking were specified based on the Every other county was classified as Rank -999 and Score -999. Finally, out of the three ranks, assigned to each county, based on the three epidemiological variables, the highest impact group (lowest rank) and its corresponding trend magnitude were decided as the final COVID-19 Impact Score and Rank for a given county.

Our study methodology was built and tested in six steps ( Figure 3 ). First, the training-testing data was prepared using the "most affected" and the "non-significantly" affected counties using the proposed COVID-19 impact assessment algorithm. Second, COVID-19 vulnerability map was generated using the RF machine learning technique 27, 28 . Third, vulnerability modeling was validated using Receiver Operating Characteristic (ROC)-Area Under the ROC Curve (AUC) technique 29-31 and Cronbach's α 32 . Fourth, our C19VI modeling was comparatively assessed against the CDC's CCVI using Friedman 33 and two-tailed Wilcoxon signed rank 34 test and later, the input themes contribution to the respective vulnerability index, the output, were ranked using, and Boruta technique 35 . Fifth, C19VI was analyzed with racial minority population and poverty dataset to determine the disproportionate impact of COVID-19 pandemic on county level. Lastly, an interactive version of the C19VI map with other results was released to the public using the ESRI Web GIS customization toolkit 36 . Each step is further detailed below:

Step 1: Preparation of the training-testing dataset: Proposed COVID-19 Impact assessment algorithm was used to map the impact of COVID-19 pandemic on all 3142 counties in the US using confirmed cases and deaths. Out of total 3142 counties, 200 very highly affected and 200 non-significantly affected counties were selected to prepare the COVID-19 vulnerability modeling training and testing dataset. 70% of the total counties (280) were randomly selected and implemented as a training dataset while rest 30% (120) were used for testing.

Step 2: COVID-19 vulnerability modeling: COVID-19 vulnerability modeling was implemented using the RF machine learning technique 27, 28 . This model predicts vulnerability of a given county on a continuous scale of 0 (least vulnerable) to 1 (most vulnerable). The map was graded according to the COVID-19 vulnerability index into five vulnerability classes 10 : very high (>80%), high (80%-60%), moderate (60%-40%), low (40%-20%) and very low (<20%) 10 .

Step 3: Validation of vulnerability modeling: The effectiveness of RF machine learning technique was specified by evaluating uncertainties in the resulting vulnerability map. The ROC -AUC 29-31 was implemented to analyze the conformity between the validation fold of the trainingtesting dataset and the products of the applied technique. We computed Cronbach's α 32 for the developed C19VI index to measure reliability by assessing the C19VI values, the output, with CDC's six theme variables, the input.

Step 4: Comparison of the CCVI and C19VI: As, both the CCVI and the C19VI models are developed using the same six thematic indicators, Friedman 33 and two-tailed Wilcoxon signed rank 34 statistical tests were implemented to comparatively assess model vulnerability prediction ability. Next, Boruta feature importance assessment technique 35 was used to evaluate the relative importance of input indicators in CCVI and C19VI.

Step 5: Community specific vulnerability analysis: Long-standing systemic, social and economic inequities across the counties have put many people from racial minority groups and living below the poverty line at increased risk of getting sick and dying from COVID-19 15, 16, 37 . By overlaying the C19VI map on racial minority population percentage data, COVID-19 vulnerability specific to racial minority groups were identified. As recommended by CDC, a 13% of the racial minority threshold was used for computing the COVID-19 vulnerability for racial minority groups 38 . Similarly, by overlaying the C19VI map on poverty percentage data, COVID-19 vulnerability specific to economically poor communities were identified. As defined by the Economic Research Service (ERS), USDA a 20% of the poverty threshold was used to estimate the vulnerability for economically poor communities 39, 40 . ESRI ArcGIS overlay analysis tool 41 was used to conduct the community-specific vulnerability analysis. 

Our COVID-19 Impact Assessment algorithm performed a county-wise assessment of the pandemic using the confirmed cases, deaths and IFRs data from 22nd January 2020 to 31st July 2020. We generated a map of our assessment that groups the impact of the pandemic on all United

States counties in one of the six categories (Figure 4(A) ). We found 88 counties with 'very high', 30 with 'high', 73 with 'moderate', 344 with 'low' and 214 with 'very low,' and 2393 with 'non-significant' impact due to the COVID-19 pandemic (Figure 4(B) ). Top 200 counties with the most impact and the bottom 200 with non-significant impact were used as training and testing datasets for our COVID-19 vulnerability model.

Using the impact assessment data of the selected United States counties, input themes and the RF technique, we developed COVID-19 Vulnerability Index (C19VI). 

We used the AUC-ROC technique to validate the prediction accuracy of our C19VI model. As shown in Figure 7 (A) and 7(B), we found 90% accuracy (AUC = 0.90) during the training phase and 84% accuracy (AUC = 0.84) during the testing phase, respectively. High internal consistency (Cronbach's α = 0.709) of C19VI model was revealed using Cronbach's α test 42, 43 . Overall, validation and reliability results indicate that the random forest machine learning modeling provides a high quality COVID-19 vulnerability map (C19VI) for the United States 31, 42, 43 .

Since we introduced a new COVID-19 vulnerability assessment index, we quantitatively evaluated its performance against the existing vulnerability model, CDC's CCVI, to assess C19VI's predictive power and applicability. We used Friedman test 33 , two-tailed Wilcoxon signed rank test 34 , and Boruta parameter importance assessment technique 35 to comparatively evaluate C19VI and CCVI.

• Friedman and Wilcoxon tests: The Friedman and the two-tailed Wilcoxon signed rank tests detected significant differences (p<0.0001) between the indices given by the two models, C19VI and CCVI. The mean rank of C19VI is 1.614 while mean rank of CCVI is 1.386. The full results of the Friedman and Wilcoxon tests can be found in Table 2 and   Table 3 , respectively.

• Boruta Test: Individual importance of each CDC input themes in determining C19VI and CCVI were quantified using Boruta, a wrapper algorithm. The most important parameter in C19VI was theme 5 (135.17), while in CCVI, it was theme 6 (131.36) (Table 4 ).

in the C19VI model as compared to CCVI, where it is ranked fourth ( Table 4 ). The full results of the Boruta test on CCVI and C19VI are presented in Figure 8 (A) and 8(B), respectively.

The racial minority populations of the United States reside more densely in the southern states and in urban areas 17, 44, 45 . Our community-specific analysis reveals that the racial minorities disproportionately live in counties that are more vulnerable to COVID-19 (Figure 9(A) ). We found that 75.57% counties with racial minority populations > 13%, have very high or high (CCVI > 0.60) COVID19 vulnerability. Similar to racial minorities, economically poor communities are more likely to be affected by the virus and have higher mortality rates 45 . The C19VI derived COVID-19 vulnerability with reference to poverty is presented in Figure 9 (B). We find that 82.84% of economically poor counties, where poverty > 20%, have very high or high (CCVI > 0.60) COVID-19 vulnerability.

The Urban Data Visualization Lab (UDVL) at the University of Illinois at Chicago (UIC) have featured an interactive version of the C19VI map which can be accessed at the following URL https://udv.lab.uic.edu/national-covid-19-vulnerability-index-c19vi. This map portal is easily accessible on personal computers and mobile devices. Figure 10 

Ever since the United States declared a national emergency due to the COVID-9 pandemic in March 2020, the country is grappling against a huge continuous surge in the incidence and mortality rates 2,46 . In the last month alone, June 30th, 2020 to July 31st, 2020, the total number of confirmed cases has risen from 27,29,764 to 47,13,014 while the total death count has increased from 1,30,313 to 1,56,826 2 . It is expected that the United States' COVID-19 death toll will double, potentially reaching more than 0.4 million by the beginning of next year 6 . Thus, keeping in mind the uncontrollable spread, ineffective strategies to check the transmission, disproportionate impact based on systemic inequalities, and heterogeneous impact on different regions, we have developed a county-level COVID-19 vulnerability index (C19VI) that assess the vulnerability of a region using an innovative methodology. This methodology takes into account the limitations of the existing vulnerability modeling techniques to assess COVID-19 vulnerability nationwide and performs a disproportionate analysis that points out the existing health disparities in the country.

In the following sections, we discuss the unique characteristics of our C19VI model and the utility of C19VI in the nationwide and community-specific vulnerability assessment.

Recently, many researchers 47-54 have internationally conducted COVID-19 risk and vulnerability assessment using either mathematical modeling 48 Thus, we optimized these limiting factors by introducing RF machine learning, a non-linear, non-parametric predictive modeling technique which can efficiently compute large datasets and account for complex interactions between variables 27, 28 . Additionally, in comparison to other machine learning techniques, RF is easier to tune and does not overfit the data making it ideal for pandemic vulnerability modeling 28 .

Furthermore, we optimized dynamic characteristics of a pandemic by developing novel 'COVID-19 Impact Assessment' algorithm which assesses the regional pandemic impact by performing trend and homogeneity analysis on daily datasets rather than static values for a defined period. Trend and homogeneity assessments help characterize the course of the pandemic and point out the COVID-19 response through changes in healthcare infrastructure or policies in a given region by identifying subtle changes in daily datasets [21] [22] [23] [24] [25] . Moreover, besides optimization, our impact assessment algorithm also serves to enhance vulnerability modeling to be driven by the chronic disease burden, healthcare infrastructure, and policy impact such as lockdown phases.

In conjunction with the optimized impact assessment algorithm, high training (90%) and testing (84%) accuracy with favorable internal reliability score (Cronbach's α = 0.709) of the RF machine learning-derived predictive modeling technique makes C19VI an accurate and reliable index. Besides, despite using the same input, the machine learningderived C19VI produced significantly different and consistent results than the CDC's CCVI as elucidated through the Friedman and Wilcoxon signed-rank tests. Moreover, Boruta algorithm-based importance assessment of the variables for both methods shows that both methods handled the variables with major notable differences. The divergence between the two methods indicates that the C19VI was able to capture non-linear relationships in the variables which were not captured with the linear 'equal weight assignment approach' used in the CDC's CCVI model. Therefore, capturing non-linearity in the input variables, promising validation results, and unique characteristics of the C19VI methodology makes it an optimal candidate to be incorporated into more comprehensive, quantitative vulnerability assessment methodologies.

Our nationwide vulnerability analysis reveals interesting patterns of vulnerability distributions around the country. We found that the majority of most vulnerable counties are concentrated in the southern states. As shown in the Figure 5 This index can also be used alongside other epidemiological data, such as disease transmission, infection fatality rate, the proportion of cases needing hospitalization, intensive care unit admissions, or ventilator support to heighten the preparedness of a district or state, as well as planning and executing the response. We also recommend the use of our C19VI index alongside the CDC's Social Vulnerability Index (SVI) for developing disaster risk assessment and preparedness plans in COVID-19 affected regions.

For example, in the times of COVID-19 pandemic, the C19VI should be used alongside the SVI for the disaster management in counties with frequent forest fires, tornadoes or hurricanes.

COVID-19 has brought previously unaddressed health disparities of racially marginalized and economically poor communities to the forefront of both disaster management officials and government concern. By overlaying the C19VI with the race and poverty data, we find that racial minorities and economically poor Americans disproportionately live in communities that are more vulnerable to COVID-19. Our finding is consistent with other evidence highlighting the disproportionate incidence of COVID-19 among minority groups and poor communities 13, 15, 37, [57] [58] [59] . In an ongoing pandemic, when the available county-level cases and deaths dataset segregated by minority population and economic status are not sufficient to generate reliable COVID-19 risk estimates, our analysis provides an excellent way to identify precise solutions, from neighborhood-level investments in cities to targeted testing strategies in rural areas, to help the communities that disproportionately bear the burden of this crisis.

Thus, the C19VI is intended to help policymakers at all levels, non-profit entities, private companies, local organizations, and the general public to help in effective COVID-19 contingency planning, drive the equitable distribution of resources, address pandemic-associated healthcare disparities, provide businesses with opportunities to grow where support is needed the most, raise public awareness on COVID-19 pandemic for informed decision making. Besides, we hope that this methodology will also prove to be useful in driving more advanced predictive modeling techniques by professionals in academia and innovation. Lastly, we believe that C19VI will be a tool for public health officials to help formulate effective and inclusive COVID response plans that will alter the course of this pandemic.

Ideally, it would be possible to calculate the index at a census tract level. However, several important variables used to define vulnerability were not available at a census tract level. Hence, this analysis is restricted to the county-level. Secondly, being based on the ranking of counties for CDC six themes, our C19VI is a relative index of each county rather than being an absolute score.

Thirdly, we were unable to test the external validity of C19VI since no accurate and stable measure of vulnerability was available. Finally, confirmed cases and deaths used in this study are up to 31st

July 2020 and might not have captured the impact of the pandemic as well as the strength of the counties in which rapid changes have occurred up to the present day.

In this work, we proposed an innovative approach to conduct the vulnerability assessment of COVID-19 within the United States at the county level. This approach integrates the reliable and high-functioning domains of machine learning and predictive modeling. RF machine learning technique in conjunction with novel 'COVID-19 Impact Assessment' algorithm successfully yields a vulnerability model, which non-linearly processes the input variables with high trainingtesting accuracy and favorable internal validity, that generates a COVID-19 vulnerability index of the United States. Besides promising validation results, comparative assessment of our technique confirms that the C19VI predictive model is a reliable and pragmatic alternative to the CDC's CCVI. More importantly, our innovative approach to develop a vulnerability index has enhanced the nationwide capacity to predict the potential harms accurately which may help curtail the distressing course and consequences of the pandemic. Additionally, when combined with the racial minority and poverty dataset, C19VI demonstrated its efficiency in disproportionate vulnerability analysis and helped validate the existing disparities. Thus, as a COVID-19 risk assessment tool, this index will help the public health officials of the federal, state, and local agencies and other academic and autonomous institutions in formulating and implementing policies, plans, and recommendations. Lastly, further exploration of our methodology, especially in vulnerability modeling, has the potential to progress the digital public health technology for pandemic management in general.

AT conceived the study, designed methodology and programmed the model. AT and VA-BR performed statistical analysis and prepared the figures. AVD worked on the data acquisition and data interpretation. AT, AVD and ERAO performed data analysis. AT and AVD wrote the manuscript. AVD and ERAO revised the manuscript critically for important intellectual content.

All authors interpreted the results, and approved the final version for submission.

We declare no competing interests. vulnerability of the racial minorities. The map shows counties with high vulnerability (C19VI > 0.6) and higher than 13% racial minorities in cobalt, low vulnerability (C19VI < 0.6) and higher than 13% racial minorities in tropical blue, high vulnerability (C19VI > 0.6) and lower than 13% racial minorities in red, and low vulnerability (C19VI < 0.6) and lower than 13% racial minorities in chardonnay. (B) Map of the U.S. counties showing COVID-19 vulnerability of the economically poor communities. The map shows counties with high vulnerability (C19VI > 0.6) and higher than 20% poverty in red, low vulnerability (C19VI < 0.6) and higher than 20% poverty in pink, high vulnerability (C19VI > 0.6) and lower than 20% racial minorities in orange, and low vulnerability (C19VI < 0.6) and lower than 20% poverty in chardonnay. 

Rolling Updates on Coronavirus Disease (COVID-19)

Bringing resources to state, local, tribal & territorial governments

Quarantine Fatigue: first-ever decrease in social distancing measures after the COVID-19 pandemic outbreak before reopening United States

Trends in Number and Distribution of COVID-19 Hotspot Counties-United States

Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months

A social vulnerability index for disaster management

The Impact of Social Vulnerability on COVID-19 in the US: An Analysis of Spatially Varying Relationships

Development of a vulnerability index for diagnosis with the novel coronavirus, COVID-19

Foundation S. The COVID-19 Community Vulnerability Index (CCVI)

A New Approach to the Social Vulnerability Indices: Decision Tree-Based Vulnerability Classification Model

The disproportionate impact of COVID-19 on racial and ethnic minorities in the United States

Disparities in Incidence of COVID-19 Among Underrepresented Racial/Ethnic Groups in Counties Identified as Hotspots During

Does the Covid-19 pandemic disproportionately affect the poor? Evidence from a six-country survey

Poverty and Covid-19: rates of incidence and deaths in the United States during the first 10 weeks of the pandemic

Why inequality could spread COVID-19

Homeland infrastructure foundation-level data

Shapefile technical description. An ESRI white paper

How Large Was the Mortality Increase Directly and Indirectly Caused by the COVID-19 Epidemic? An Analysis on All-Causes Mortality Data in Italy

Nonparametric tests against trend

Rank correlation methods

A rank-invariant method of linear and polynominal regression analysis (Parts 1-3)

Estimates of the regression coefficient based on Kendall's tau

A non-parametric approach to the change-point problem

Modelling insights into the COVID-19 pandemic

Random forests

Classification and regression by randomForest

Diagnostic tests 3: receiver operating characteristic plots

Understanding receiver operating characteristic (ROC) curves

Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach

Coefficient alpha and the internal structure of tests

The use of ranks to avoid the assumption of normality implicit in the analysis of variance

Individual comparisons by ranking methods

Feature selection with the Boruta package

COVID-19 exacerbating inequalities in the US

Improving Health Equity for Black Communities in the Face of Coronavirus Disease-2019

Rural Health Disparities: The Economic Argument. Application of the Political Economy to Rural Health Disparities

Rural, low-income families and their well-being: Findings from 20 years of research

Making sense of Cronbach's alpha

Cronbach's Alpha: Simple definition, use and interpretation

The Coronavirus's unique threat to the south

Spatial Variation in Socio-ecological Vulnerability to COVID-19 in the Contiguous United States

COVID-19 Progression Timeline and Effectiveness of Response-to-Spread Interventions across the United States

Risk assessment of novel coronavirus COVID-19 outbreaks outside

Data-Driven Development of a Small-Area COVID-19 Vulnerability Index for the United States

A vulnerability index for the management of and response to the COVID-19 epidemic in India: an ecological study. The Lancet Global Health

Social Vulnerability and Racial Inequality in COVID-19 Deaths in Chicago

COVID-19: District level vulnerability assessment in India

COVID-19 and urban vulnerability in India

Visualizing and Assessing US County-Level COVID19 Vulnerability

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

What do we know about the risk of dying from COVID-19. Our World in Data

Mathematical models for COVID-19: Applications, limitations, and potentials

Inequity and the disproportionate impact of COVID-19 on communities of color in the United States: The need for a trauma-informed social justice response

Social Vulnerability and Equity: The Disproportionate Impact of COVID-19

The Disproportionate Impact of Covid-19 on Communities of Color. NEJM Catalyst Innovations in Care Delivery

COVID-19 Impact Assessment Algorithm -Pseudocode Algorithm 1: COVID-19 Impact Assessment

Daily reported COVID-19 deaths by county // n = 192; Total days from 22nd

// first non Zero Index of array or the instance when first confirmed case, death and infection fatality rate reported homoC, cpC = HomogeneityAnalysis

// Homogeneity analysis and change point detection overallCT, overallCG = TrendAnalysis

// Trend analysis overall, pre and post change point -cases overallDT, overallDG = TrendAnalysis

// Trend analysis overall, pre and post change point -deaths overallIFRT

D', homoD, overallDT, overallDG, preDT, preDG, postDT, postDG) Rank-C, Score-C = COVIDImpact

// Priority wise COVID Impact assessment (IFR > deaths > cases)

// Best Rank Switch(Rank): case('Rank-IFR'): Score = Score-IFR; Break

Rank-D'): Score = Score-D; Break

Rank-C'): Score = Score-C; Break

We would like to thank Johns Hopkins University, Centers for Disease Control and Prevention 

We used publicly available data and have referenced the sources in the paper.