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Applying innovative data science principles to enhance immunization policy decisions
Immunizations and their corresponding vaccines have saved millions of lives over the past few decades. For instance, the vaccine for measles was introduced in 1962 and resulted in such an incredible drop in cases that by 1993 measles had almost disappeared in the United States. While the protection provided by immunizations has been historically proven, recent trends towards reductions in vaccination rates present a risk to public health.
Objective:
Understanding and quantifying this risk enables better informed policy decisions and can be achieved through advances in health information technologies. This presentation covers work done to apply these technologies towards determining immunization-related risks and understanding the effects of policy decisions upon them.
Methods:
Expanding capabilities in data management are utilized for the broad collection of immunization data and disease tracking information. These disparate data sets are integrated using innovative processes that have been recently developed. Additionally, advances in data analytics enable a deep understanding of the impacts immunization have upon a specific population. Using this understanding as a backbone, advanced machine learning techniques, such as agent-based models, are used to explore theoretic scenarios that serve as surrogates of policy decisions.
Results:
The outputs from the methods include simulated information on the spread, or lack thereof, of disease in a population. Simulations are based on policy decisions relating to vaccination and result in an understanding of the susceptibility of a population to disease.
Conclusions:
The analysis in this presentation results in a strong, quantified understanding of the impact immunizations have upon a population and how policy decisions in this area are of critical importance for the public health. By the end of the session, the participants will be able to understand the following; the advantages of data aggregation, advances in data analytics, and the effects of immunization policy on population health.
Learning Areas:
Biostatistics, economicsPublic health or related public policy
Learning Objectives:
Describe the process for applying advancements in data science on immunization, disease, and population data. Evaluate the impact of immunization policy decisions on population health.
Keyword(s): Immunizations, Policy/Policy Development
Qualified on the content I am responsible for because: I have performed data science roles across a variety of fields for over six years with particular emphasis in exploring and understanding disparate data sets. In the field of public health, I have created innovative solutions for analyzing non-traditional data sources and determining actionable intelligence that impact public health. Among my scientific interests has been the advancement of data science, in particular data integration and analysis.
Any relevant financial relationships? Yes
| Name of Organization | Clinical/Research Area | Type of relationship |
|---|---|---|
| Northrop Grumman Corporation | Public Health | Employment (includes retainer) |
I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.