A STUDY OF DISPROPORTIONATELY AFFECTED POPULATIONS BY RACE/ETHNICITY DURING THE SARS-COV-2 PANDEMIC USING MULTI-POPULATION SEIR MODELING AND ENSEMBLE DATA ASSIMILATION

Keyphrases

• Modeling Data 100%
• Race-ethnicity 100%
• Contact Rate 100%
• Epidemiologist 100%
• Public Health Officials 100%
• Multi-population 100%
• Contact Matrix 100%
• SARS-CoV-2 Pandemic 100%
• Ensemble Data Assimilation 100%
• Affected Population 100%
• Viral 50%
• United States 50%
• Older Adults 50%
• Adverse Outcomes 50%
• Reproductive Age 50%
• Complete Data 50%
• Count Outcomes 50%
• Public Funding 50%
• Working Adults 50%
• Age-stratified 50%
• Minority Populations 50%
• Incomplete Data 50%
• Time-continuous 50%
• Death Data 50%
• US States 50%
• Disadvantaged Groups 50%
• Rate Matrix 50%
• Record Keeping 50%
• Death Cases 50%
• Aggregate Statistics 50%
• District of Columbia 50%
• Infrastructure Finance 50%
• Susceptible-infected-recovered Model 50%
• Reproductive number 50%
• Estimate Model Parameter 50%
• COVID-19 50%
• Continuous Updating 50%
• Daily Deaths 50%
• Multiple Data Assimilation 50%
• Time-varying Reproduction number 50%
• Data Assimilation Method 50%
• Recovered Model 50%

Mathematics

• Matrix (Mathematics) 100%
• Susceptible Population 100%
• Reproductive Number 100%
• Incomplete Data 50%
• Continuous Time 50%
• Rate Matrix 50%
• Time Series 50%
• Time Series Analysis 50%
• Complete Data 50%