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Background More than 360,000 people infected with COVID-19 in New York State (NYS) by the end of May 2020.The spatial variations of prevalence across the counties in NYS suggested that variations in county-level factors might contribute to the statewide COVID-19 outbreak. However, no study to date investigates such variations and the relevant predictors. We leveraged multiple public datasets and machine learning approaches to construct the county-level spatial-temporal prediction model of COVID-19 in NYS. Findings generating from this study help identify counties with high prevalence, county-level predictors, and promising next steps for policy efforts to control the second wave of statewide COVID-19 transmission. Methods Cumulative confirmed case rates (CCCR) of COVID-19 by county in NYS were extracted from the US Health Data system at four critical time points including March 17th (state of emergency, 4.40 per 100,000 people), April 18th (coronavirus peak, 310.10 per 100,000 people), April 25th (expand testing, 393.90 per 100,000 people), and May 11th (daily increased rate back to the level in March, 505.30 per 100,000 people. A total of 28 county-level predictors were used to construct the prediction model, and the generalized linear mixed effect least absolute shrinkage and selection operator (LASSO) regression was employed to select the predictors of COVID-19 outbreak across the counties in NYS with adjusting for time effect. Results The CCCR by the final timepoint was 1,850.3 per 100,000 people. Rockland County had the highest CCCR than any other counties, with a rate of 3,856.82 per 100,000 people, while Chautauqua and Franklin counties had the lowest CCCR (0.03 per 100,000 people). LASSO regression revealed counties with a larger proportion of non-citizen (β=9537.97, p=0.02) had a higher CCCR of COVID-19 across the time. In contrast, counties with a lower proportion of people with at least high school education (β=-6157.89, p=0.025) and a larger proportion of houses with less than 3 people (β=-5995.79471, p=0.01) had lower CCCR. Conclusion We identified immigrant status, education level and household type influenced the spatial variations of COVID-19 outbreak in NYS. Future interventions shall target on areas with greater density of non-citizens to prevent transmission. Disclosures All Authors: No reported disclosures

431. Geospatial prediction model of COVID-19 outbreak in New York State