Globally local: Hyper-local modeling for accurate forecast of COVID-19.

Importance: Assumption of a well-mixed population during modeling is often erroneously made without due analysis of its validity. Ignoring the importance of the geo-spatial granularity at which the data is collected could have significant implications on the quality of forecasts and the actionable clinical recommendations that are based on it.

Objective: This paper's primary objective is to test the hypothesis that the characteristic dynamics defining the trajectory of the pandemic in a region is lost when the data is aggregated and modeled at higher geo-spatial levels.

Use of Stratified Cascade Learning to predict hospitalization risk with only socioeconomic factors.

Background And Objective: Published models predicting health related outcomes rely on clinical, claims and social determinants of health (SDH) data. Addressing the challenge of predicting with only SDH we developed a novel framework termed Stratified Cascade Learning (SCL) and used it for predicting the risk of hospitalization (ROH).

Materials And Methods: The variable set includes 27 SDH and "age" and "sex" for a cohort of diabetic patients.