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Policy & Preparedness

Spring boarding from the previous accomplishments and findings from the MIDAS-funded Policy & Preparedness project, the Policy Methods and Policy Studies Component both aim to improve policy decision-making through the development of new methods for uncertainty analysis and for model-linked value of information analysis, and promote and accelerate the use of computational modeling by public health practitioners. 

Specifically, the Policy Methods team will construct a "proof-of-concept" analysis of influenza epidemic mitigation strategies that integrates geospatially accurate cost data and estimates impact of operational constraints on mitigation strategy effectiveness.

The Policy Studies Component will evaluate strategies for translating modeling results and tools into public health policy.

To access State Pandemic Influenza Plans, please click on the tab above. 

Engaging Computational Methods for Public Health Law & PolicyPlanned and hosted a workshop on "Engaging Computational Methods for Public Health Law and Policy," including representatives from machine learning, natural language processing, and network analytics. For more information, visit the MIDAS news archives.

Developed the Legal Network Analyzer (LENA), permitting practitioners and policymakers to visualize legally directed relationships between public health system agents as mandated by federal or state statutes and regulations (Sweeney, J Public Health Manag Pract, 2013).

Conducted cost impact of employee vaccination program in PA. Identified optimal PODs sites and throughput targets, allowing health departments to adjust resources accordingly (Everett, J Public Health Manag Pract 2013).

Proposed a conceptual model for tracking progress toward the goal of preparedness for public health emergencies using legal, economic, and operational indicators (Potter, J Public Health Manag Pract 2013).

Applied a sequential experimental design method with FRED to analyze multiple policy alternatives to provide a set of promising "optimal" combinations of policies to control an H1N1-type epidemic (Luangkesorn, J Public Health Manag Pract 2013).

Articulated themes that we think lay a foundation for sound research on modeling for preparedness (Burke, J Public Health Manag Pract, 2013).

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