The early outbreak of coronavirus disease-2019 (COVID)-19 became associated with various 'hot spots' in the USA, particularly in large cities. However, despite the widespread nature of the outbreak, much of what is known about the virus' impact and clusters is understood either for individuals, or at the state level. This paper assesses the predictors of outbreaks at the neighborhood level. Using data from the Louisiana Department of Health, we use spatial regression models to analyze the case count through 3 May 2020 and its relationship to individual and geographic neighborhood characteristics at the census tract level. We find a particularly strong and large correlation between race and COVID-19 cases, robust to model specification and spatial autocorrelation. In addition, neighborhoods with lower rates of poverty and those with fewer residents over 70 have fewer cases. Policy makers should adjust testing strategies to better service the hardest hit populations, particularly minorities and the elderly. In addition, the results are greater evidence of the impact of systemic issues on health, which require a long-term strategy for redress.