Analytics and Modeling Track Competencies
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- Public Health Science and Practice
- Organizational Entities in Public Health Science and Practice
- Epidemiology Methods, Studies, and Investigations
- Interpersonal and Professional Communication
- Leadership for Emerging Scientists
- Data Management for Modeling
- Model Construction and Analysis
- Model Interpretation and Presentation
The PE Fellowship Analytics and Modeling Track is a competency-based program. Competency is defined as, “a complex combination of knowledge, skills, and abilities demonstrated by organization members that are critical to the effective and efficient function of the organization”. The Analytics and Modeling Track competencies provide the framework for the training, assignments, and activities of the fellows while in the program, and they describe what the fellow should be able to do following completion of the fellowship. The competencies are also at the core of the planning and evaluation process for the fellowship. They will continue to evolve to meet the expanding mission of the agency and public health.
Public Health Science and Practice
- History and scientific foundation of public health in the United States
- Population health tools, core public health functions, and the ten essential services
- Types of public health protection, promotion, and prevention programs
- Factors that contribute to the success or failure of population health programs
- Evidence base for population health
- Assessment of, and factors contributing to, population health problems
Organizational Entities in Public Health Science and Practice
- United States government entities’ roles, functions, and statutory restrictions in public health protection, promotion, and prevention
- CDC history, vision, mission, role, organizational structure, priorities, and CDC’s unique organizational culture
- Roles and functions of key CDC public health partners
Epidemiology Methods, Studies, and Investigations
- Epidemiology terms, concepts, and prominent historical events
- Epidemiological aspects of a specific public health problem
- Design of epidemiology studies and investigations
- Analytic methods, outcome measures, and measures of disease frequency & association used in epidemiology
- Methods to control confounding, bias, effect modification, and other threats to validity in epidemiology
Interpersonal and Professional Communication
- Leverage a range of effective listening techniques when interacting with colleagues
- Advocacy and inquiry in dialogue with individuals and groups
- Conflict resolution skills to address concerns, disagreement, and conflict
- Management of sensitive issues in a diplomatic and non-threatening way
- Express ideas, technical information, and research results using clear, concise, and accurate oral and written communication, specific to the audience
- Clear and concise scientific written communication and scientific presentations
Leadership for Emerging Scientists
- Identify personal strengths, capabilities, limitations, and implications of actions taken
- Demonstrate resilience to changing situations or overcoming obstacles inhibiting work activities
- Balance perfection and practicality to work within a fast-paced environment
- Participate in multidisciplinary project teams to contribute to common goals and solutions and manage conflict among team members
- Develop positive, productive relationships with colleagues and shareholders
- Network with CDC and non-CDC colleagues and collaborate with colleagues with divergent perspectives and disciplines
- Influence project partners and shareholders and defend the use of prevention effectiveness research methods, processes, viewpoints, and tools
- Perform multiple tasks and professional duties within a multidisciplinary, federal public health agency and complete projects on time
- Apply cultural sensitivity strategies within a diverse workforce
- Initiate, promote, and expand individual, team, and organizational learning opportunities
Data Management for Modeling
- Identify various data sources for modeling
- Link and integrate data for modeling
- Manage and maintain data for modeling
Model Construction and Analysis
- List several goals of modeling
- Understand systems as a foundation for modeling
- Understand and employ various modeling techniques (eg., agent-based simulation, compartmental models, state-space transitions, disease transmission models, network models, discrete event simulation, Monte Carlo simulation, decision trees, econometrics, etc.)
- Estimate parameters of models
- Identify and define model assumptions
- Optimize models (debugging, routine-checking, efficiency)
- Calibrate models using various methods (eg., manual, random, Nelder-Mead, etc.)
- Apply variance reduction techniques to models
- Employ specialized software for modeling (eg., R, Netlogo, MatLab, TreeAge, XL, Python, Anylogic, etc.)
- Document model specifications for others to follow
- Employ validation methods, perform sensitivity analysis, and apply statistical methods appropriately to models
- Employ statistical software for model analysis (eg., SAS, STATA, R, MatLab, etc.)
Model Interpretation and Presentation
- Critically evaluate a modelling study and communicate its strengths and weaknesses to a scientifically literate audience
- Interpret and explain models for non-modelers and policymakers in both written and oral presentation
- Identify the relationship between models and real-world epidemiological and health data
- Employ data visualization software to models (eg., Tableau, VB, R, Python, etc.)