Health data science is a multidisciplinary field that uses tools and methods from data science and artificial intelligence to extract health and non-health data about populations and communities from various sources. For example, large disparate data sources create effective data visualization and build statistical and predictive models to tailor preventive interventions for at-risk groups, promote health, reduce health inequalities, and enhance healthcare and health management decisions. One aspect interrelated with and enhanced by health data science is 'personalization and precision' in public health, usually referred to as Precision Public Health. This describes the application and combination of new and existing technologies, which more precisely define and analyze individuals and their environment over the life course, tailor preventive interventions for at-risk groups, and improve the population's overall health. Precision Public Health aims to provide 'the right intervention to the right population at the right time based on many related disciplines, such as public health genomics, life course epidemiology, social epidemiology, and Geographic Information Systems (GIS).
Some emerging health data science and precision public health applications involve the timely translation of genome-based knowledge and technologies into public health and health policies. Geospatial applications and predictive modeling techniques identify populations at high risk for a disease, link characterizations of the environment to health changes, explore small area socio-economic inequalities in health, identify and evaluate suspected cancer clusters, and map the risk of diseases. Precision Public Health and Heath Economics can play an essential role in improving health economics methods and cost-effectiveness procedures. Many Precision Public Health elements can be incorporated into Health Economics valuations and cost-effectiveness methods, using observational data and expert opinion methodologies. Using decision-making approaches with Precision Public Health's gained information, analysts can maximize Population Health without ignoring social preferences.
The aim of the current Research Topic is to cover promising, recent, and novel research trends in the Precision Public Health and related to Health Economics field. Areas to be covered in this Research Topic may include, but are not limited to:
• Decision Support Systems for Healthcare and Health Economics;
• Personalized Healthcare and Health Economics;
• Health Geographic Spatial Models and Artificial intelligence;
• Public health data quality and uncertainty;
• Precision Public Health and Environmental Health;
• Public health genomics.
Health data science is a multidisciplinary field that uses tools and methods from data science and artificial intelligence to extract health and non-health data about populations and communities from various sources. For example, large disparate data sources create effective data visualization and build statistical and predictive models to tailor preventive interventions for at-risk groups, promote health, reduce health inequalities, and enhance healthcare and health management decisions. One aspect interrelated with and enhanced by health data science is 'personalization and precision' in public health, usually referred to as Precision Public Health. This describes the application and combination of new and existing technologies, which more precisely define and analyze individuals and their environment over the life course, tailor preventive interventions for at-risk groups, and improve the population's overall health. Precision Public Health aims to provide 'the right intervention to the right population at the right time based on many related disciplines, such as public health genomics, life course epidemiology, social epidemiology, and Geographic Information Systems (GIS).
Some emerging health data science and precision public health applications involve the timely translation of genome-based knowledge and technologies into public health and health policies. Geospatial applications and predictive modeling techniques identify populations at high risk for a disease, link characterizations of the environment to health changes, explore small area socio-economic inequalities in health, identify and evaluate suspected cancer clusters, and map the risk of diseases. Precision Public Health and Heath Economics can play an essential role in improving health economics methods and cost-effectiveness procedures. Many Precision Public Health elements can be incorporated into Health Economics valuations and cost-effectiveness methods, using observational data and expert opinion methodologies. Using decision-making approaches with Precision Public Health's gained information, analysts can maximize Population Health without ignoring social preferences.
The aim of the current Research Topic is to cover promising, recent, and novel research trends in the Precision Public Health and related to Health Economics field. Areas to be covered in this Research Topic may include, but are not limited to:
• Decision Support Systems for Healthcare and Health Economics;
• Personalized Healthcare and Health Economics;
• Health Geographic Spatial Models and Artificial intelligence;
• Public health data quality and uncertainty;
• Precision Public Health and Environmental Health;
• Public health genomics.