Abstract: The technology disclosed relates to a system and method for predicting chronic disease outcome for census tract-level communities at risk for COVID-19 related complications. The system can access satellite image data of built environment for census tract-level communities and merge the image data with respective chronic disease prevalence data per census tract-level community. This merging of image data with chronic disease prevalence data results in a high-dimensional image space. The system includes logic to identify principal components forming a basis of the high-dimensional image space. A subset of the principal components is selected that cumulatively explain at least fifty percent of the explained variance. The system includes logic to calculate COVID-19 risk score as a weighted combination of the selected principal components. The COVID-19 risk score is provided to public health policy decision makers for use in public health policy decisions.

Abstract: The technology disclosed relates to systems and methods for predicting digital twins. The system includes logic to use a machine learning model predict correlation between pairs of persons and save the results in an environmental and phenotypic correlation matrix. The inputs to the machine learning model can include data from individual-level and group-level datasets. The individual-level datasets include administration dataset including clinical data and person dataset including personal data. The group-level datasets include exposome dataset including environmental exposure and subpopulation dataset. The system includes logic to use the environmental and phenotypic correlation matrix as a random effect when determining associations between exposures and outcomes. The system includes a second machine learning model that can take a pair of exposure and outcome and the environmental and phenotypic correlation matrix as input to predict causal association between exposure and outcome.

Abstract: The technology disclosed relates to a system and method for predicting comorbidity trajectories of disease categories on a census tract-basis. The system include logic to process satellite images for a particular census tract and generate respective latent feature vectors for respective satellite images. The system include logic to determine respective weighted average latent feature vectors for the respective latent feature vectors. The respective weighted average latent feature vectors are regressed against a plurality of disease categories and a plurality of risk factors. The regressor generates prevalence scores for disease categories in the plurality of disease categories and for risk factors in the plurality of risk factors. The system can correlate the disease categories with each other and with risk factors to determine comorbidity trajectories of the disease categories in the particular census tract.