Professor James Koopman (University of Michigan) is the Lead on the Policy Studies component.
Investigators are based at University of Michigan and University of Florida, though all of the Research Projects as well as the Software Development component will feed into and interact with the Policy Studies component.
The overall goal of this component is to improve communication and understanding between policy makers and modelers so they can work more effectively together. This process of using models for policymaking could enhance the joint understanding of policymakers and modelers if it more thoroughly examined how model parameters and structural uncertainty affects decisions.
- To formulate and to promote interactions between modelers, models and policymakers by improving how models capture processes and generate outcomes and to evaluate the effectiveness of such interactions that arise in MIDAS.
- To analyze models to account for costs and value health outcomes using widely used measures familiar to most policymakers that allow cost-benefit and cost-effectiveness comparisons not only between specific infectious disease policy alternatives, but also between spending on emerging and continuing infectious diseases versus other public health intervention.
- To address uncertainty in three ways:
- To establish a set of model analysis procedures that qualitatively evaluate how theoretical assumptions about the real world that are intrinsic to a model form might alter a policy decision.
- To formulate probabilistic aspects of of policy decisions within a Bayesian framework that helps policymakers use stochastic model outcomes to better inform their policy choices.
- To develop methods to calculate the economic value of efforts to gather further information so that decision makers can prioritize future information-gathering efforts.
Methods of inference robustness assessment, identifiability analysis, and economic analysis will be fused both to frame the discussions between policymakers and modelers (aim 1) and to improve the usability of the models for policy decision making (aims 2 and 3)
Policy for the Polio Endgame
The world is anxiously following the polio eradication endgame. The best strategy for that endgame depends strongly on how polio immunity affecting susceptibility wanes in relationship to immunity affecting contagiousness. It also depends on how reinfection with vaccine or wild viruses boosts immunity and slows subsequent waning of immunity. No standard prospective study designs can make the needed waning and boosting measurements. The time frames of waning are just too long for that and the conditions where immunity is tested by exposure to wild polio virus or vaccine virus are too irregular. The needed observations must be made from surveillance data. CIDID will be analyzing data from the silent circulation epidemic in Israel, from the end of paralytic polio in India, and from other sites in order to make the needed estimations.
But first we will determine exactly what aspects of waning immunity we must model in order to make solid decisions about how to handle the eradication endgame. For example, in the endgame, surveillance of infection must replace surveillance of paralytic disease. Such surveillance is very expensive. Where and how environmental surveillance for polio virus should be conducted can be changed by different patterns of waning and boosting. We must determine what aspects of waning make a difference to these decisions to insure we make the right estimations through our data analyses. Similarly, waning and boosting of immunity patterns could be crucial to whether a burst of oral polio vaccination administration to all age groups is needed right before finally stopping all use of oral polio vaccines. There is a chance in some areas that circulating vaccine derived poliovirus could evolve from the vaccine to cause polio if oral polio vaccine use is stopped when population immunity is too low. The particular waning parameters that must be in a model for this decision could be different than for the decision as to where environmental surveillance must be conducted.
The models needed will be assessed using an “inference robustness assessment” strategy. Then new iterative filtering estimation methods will be used to constrain the parameter spaces of the needed models, especially with regard to waning and boosting parameters. Creative use of genetic sequence data, sanitation data, environmental virus surveillance, serology patterns over time, and special survey data on virus excretion could be essential for making the needed constraints on parameter space. To evaluate that, new inference identifiability methods are being developed.
The CIDID policy unit is leading this project with the participation of other CIDID collaborators and with Joe Eisenberg’s MIDAS research project which focuses on multi-scale modeling of enteric infection transmission through the environment. This work is supported by a grant from the WHO Global Polio Eradication Initiative.
Click here to view the WHO global action plan to minimize poliovirus facility-associated risk (GAPIII).