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Abstract:
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Social epidemiology is that focuses on the effects of social-structural factors on states of health. Social epidemiology assumes that the distribution of inequality in a society reflects the distribution of disease and public health. With this assumption in place, we can implement data science to find causal inference. In this research, first, we suggest that alternative data sources as well as surveys, censuses, and observational studies are as a potential data for health statistics and policy analysis indicator because of lack of social epidemiology data. These novel data sources can have gains due to, the speed of data collection and providing a sensor information. Secondly, we focus on the use of causal inference and graphical models to make an interpretable and explainable model. This explainability and interpretability becomes important when the results of data driven decisions have a significant impact on individuals and the public. Finally, we explain how such analyzes could be conducted in real-data, enabling early detection of emerging patterns to allow law enforcement agencies and policy designers to react accordingly.
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