There is growing concern that racial and ethnic minority communities around the United States are experiencing a disproportionate burden of infection rate and mortality from the coronavirus disease 2019 (Covid-19). While most research, media newspapers, websites, and television networks are providing statistical numbers of daily infection and death rate across US by state, these numbers fail to study the actual impact of COVID-19 to each race. Our approach has taken the top five races by population count in the US and has calculated the impact index by race for each state for COVID-19 infections and death rate. We also examine the rise in the utilization of hospitals as a result of the rise in cases of COVID-19 in the United states. We conclude that the African American race and Hispanic race is disproportionately impacted more than the white population for infection rate. 
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                            Data Analysis of Crime and Rates of Hospitalization due to COVID-19
                        
                    
    
            There has been an increasing concern that African American community has been disproportionately impacted during the coronavirus pandemic. This paper analyzes why the African American community is disproportionately impacted during the coronavirus pandemic and compares the COVID-19 data with hospitalizations, real estate, school closings, and crime data. Human behavior was impacted as a result of lockdown due to COVID pandemic and it lead to a shift in crime dynamics. We analyze shifts in crime types by comparing crimes before and after the COVID pandemic in Baltimore. There was a significant decline in total crimes during the time period immediately following stay at home orders. Findings show that the disproportionality among the African American community is significantly influenced by factors such as living in more crowded housing situations, working in consumer-facing serviced industries, having higher rates of pre-existing medical conditions, and lack of insurance or a consistent care source. 
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                            - PAR ID:
- 10333254
- Date Published:
- Journal Name:
- Proceeding of the IEEE International Conference on Computational Science and Computational Intelligence, (CSCI'21),Symposium of Big Data and Data Science (CSCI-ISBD)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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