The focus of the policy section is on developing a set of methods that focus both modelers and policymakers on models in a way that makes decision alternatives clearer and improves the power of data and of models to inform policy. The overall strategy is called Inference Robustness Assessment (IRA). It encapsulates basic traditions in science into two iterative processes. The first assesses the validity of inferences made from a particular model form by realistically relaxing simplifying assumptions in that model form and assessing whether greater realism has the potential to change the inferences made when analyzing the model and analyzing data with the model. This iterative loop is pursued when there is enough data to make a definitive inference about policy. When there is not enough data, a second identifiability analysis loop is pursued to determine what additional data will best inform a decision. Identifiability means that data can determine model characteristics uniquely. For example only one policy choice is consistent with the data. The IRA approach does not focus on parameter identifiability as traditional identifiability analysis does. It focuses on making inferences identifiable which may occur even when no parameters are identifiable. A key to making science serve policy using IRA is to begin with simple policy choices of high relevance to the policymakers and with simple models that have face validity for potentially informing that policy choice. We will examine aspects of communication between modelers and policymakers to establish good beginnings leading to successful policymaking. Quite often policy choices entail doing things with limited resources. A key to making models policy relevant is, therefore, incorporating parameters and structures to enable cost effectiveness and cost benefit analyses using the model. Policy choices can be influenced by the chances of different outcomes given different policy decisions. Therefore IRA incorporates a full range of deterministic to stochastic models and employs Bayesian inference to establish the joint ranges of model parameters that are consistent with data. This approach has the advantage that while pursuing the best scientifically supported inference, it is able to inform a policy choice at any level of depth into the IRA process.

Public Health Relevance

This project develops methods that focus models, modelers, and policymakers in a coordinated manner that best informs policy. Those methods help policymakers see more clearly how different processes involved in infection transmission affect their decisions. They also help modelers see what they have to do to best relate to the decisions of policymakers. Finally they establish the validity of model based policy inferences.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54GM111274-01
Application #
8796480
Study Section
Special Emphasis Panel (ZGM1-BBCB-5 (MI))
Project Start
Project End
Budget Start
2014-09-12
Budget End
2015-06-30
Support Year
1
Fiscal Year
2014
Total Cost
$217,214
Indirect Cost
$32,067
Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
078200995
City
Seattle
State
WA
Country
United States
Zip Code
98109
Feldstein, Leora R; Matrajt, Laura; Elizabeth Halloran, M et al. (2016) Extrapolating theoretical efficacy of inactivated influenza A/H5N1 virus vaccine from human immunogenicity studies. Vaccine 34:3796-802
Bento, Ana I; Rohani, Pejman (2016) Forecasting Epidemiological Consequences of Maternal Immunization. Clin Infect Dis 63:S205-S212
Xue, Katherine S; Hooper, Kathryn A; Ollodart, Anja R et al. (2016) Cooperation between distinct viral variants promotes growth of H3N2 influenza in cell culture. Elife 5:e13974
Koepke, Amanda A; Longini Jr, Ira M; Halloran, M Elizabeth et al. (2016) PREDICTIVE MODELING OF CHOLERA OUTBREAKS IN BANGLADESH. Ann Appl Stat 10:575-595
Ngwa, Moise C; Liang, Song; Kracalik, Ian T et al. (2016) Cholera in Cameroon, 2000-2012: Spatial and Temporal Analysis at the Operational (Health District) and Sub Climate Levels. PLoS Negl Trop Dis 10:e0005105
Chao, Dennis L; Dimitrov, Dobromir T (2016) Seasonality and the effectiveness of mass vaccination. Math Biosci Eng 13:249-59
Neher, Richard A; Bedford, Trevor; Daniels, Rodney S et al. (2016) Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses. Proc Natl Acad Sci U S A 113:E1701-9
Chen, Wan-Jun; Lai, Sheng-Jie; Yang, Yang et al. (2016) Mapping the Distribution of Anthrax in Mainland China, 2005-2013. PLoS Negl Trop Dis 10:e0004637
Fang, Li-Qun; Yang, Yang; Jiang, Jia-Fu et al. (2016) Transmission dynamics of Ebola virus disease and intervention effectiveness in Sierra Leone. Proc Natl Acad Sci U S A 113:4488-93
Magpantay, F M G; Domenech DE Cellès, M; Rohani, P et al. (2016) Pertussis immunity and epidemiology: mode and duration of vaccine-induced immunity. Parasitology 143:835-49

Showing the most recent 10 out of 63 publications