Using integrated modeling to support the global eradication of vaccine‐preventable diseases

Abstract

The long‐term management of global disease eradication initiatives involves numerous inherently dynamic processes, health and economic trade‐offs, significant uncertainty and variability, rare events with big consequences, complex and interrelated decisions, and a requirement for cooperation among a large number of stakeholders. Over the course of more than 16 years of collaborative modeling efforts to support the Global Polio Eradication Initiative, we developed increasingly complex integrated system dynamics models that combined numerous analytical approaches, including differential equation‐based modeling, risk and decision analysis, discrete‐event and individual‐based simulation, probabilistic uncertainty and sensitivity analysis, health economics and optimization. We discuss the central role of systems thinking and system dynamics in the overall effort and the value of integrating different modeling approaches to appropriately address the trade‐offs involved in some of the policy questions. We discuss practical challenges of integrating different analytical tools and we provide our perspective on the future of integrated modeling. Copyright © 2018 System Dynamics Society

Review of poliovirus modeling performed from 2000 to 2019 to support global polio eradication

“…Following the search process shown in Figure 1 , the systematic literature review and addition of other studies by the three GPEI-supported modeling groups led to the extraction of information from 176 included studies [ 1 - 4 , 8 - 179 ]. As noted, during review of the full text of the studies identified by the search, we excluded papers that presented statistical analyses that did not include a mechanistic poliovirus transmission model [ 180 – 187 ].…”

“…Table 2 provides an overview of some of the attributes of the model structures and assumptions for the 83 papers that included a poliovirus transmission model [ 4 , 9 , 10 , 14 , 18 – 20 , 22 , 24 – 27 , 33 – 36 , 38 – 41 , 43 , 46 , 47 , 49 – 62 , 64 , 65 , 68 – 71 , 73 – 77 , 81 , 97 – 100 , 129 , 132 – 135 , 147 – 171 ] organized by modeling group. Mathematical models for poliovirus transmission vary considerably in their complexity.…”

“…Given the complexity of poliovirus immunity and multiple vaccine options, transmission and health economic modeling can provide insights that can support decision-makers as they evaluate different decisions and policy options. The GPEI partners implicitly value this type of modeling by engaging multiple modeling groups, and some prior studies documented the important role of modeling with respect to supporting some GPEI decisions [ 1 – 4 ]. Notably, however, we could not identify a comprehensive list of GPEI decisions, which makes it difficult to systematically document the decision-support provided by modeling.…”

Applying Sterman's proposed principles of modeling rigor to hybrid models combining multiple simulation methods

“…Unsurprisingly, optimization models are excellent for quickly uncovering optimal policies. A hybrid approach can take advantage of the above complementary methods’ strengths (Duintjer Tebbens and Thompson, ).…”

Fuzzy rule‐based inference in system dynamics formulations

“…In the same issue, for example, Ghaffarzadegan and Larson () show how traditional operations research (OR) models can benefit by including feedback processes. Additionally, Duintjer Tebbens and Thompson () argue for more integrated modeling for healthcare studies and its value to appropriately address complex policy decisions. The authors present results of more than a decade of their efforts in producing complex integrated SD models that combine numerous analytical methods and discuss practical challenges of integrating different analytical tools.…”