Parameterization and Sensitivity Analysis of a Complex Simulation Model for Mosquito Population Dynamics, Dengue Transmission, and Their Control

1Local and regional control is complicated by the facts that no vaccine has yet been licensed for broad-scale application and that dengue epidemiology is complex. Not only are there four different virus serotypes with potentially different ecologies, but transmission dynamics are also influenced by variation in the behavior and population dynamics of mosquito vectors and human hosts and the nature of their interactions with the environment and each other. 2 Mathematical and computer simulation models provide a systematic way to explore and analyze the complexity of dengue transmission and can be useful tools for devising more effective disease surveillance and control strategies. Models can integrate data from different sources and at different spatiotemporal scales, identify and prioritize gaps in data or knowledge, and address questions that are too complex, expensive, or dangerous to address in other ways. 3, 4 Models, therefore, provide a basic theoretical framework so that the intricate transmission processes of dengue can be represented in a simplified form that can be analyzed in ways that are not possible in the natural system itself. To explore the complexity of infectious disease systems, both simple 5 and complex 6– 8 models have been developed. More complex disease models tend to be more realistic and can be used to develop location-specific control strategies. However, the behavior of complex models can be difficult to explore analytically, 9 and as the number of model parameters increases, the likelihood also increases that parameters for which little empirical data exist are included. Evaluating the suitability, accuracy, and performance of complex disease models can be difficult. Sensitivity analysis provides a way to explore the behavior of complex disease models by identifying how variation in model parameters affects model output. 9 Model outputs obtained with different parameter values are evaluated to identify the parameters to which the model is most sensitive. In this way, sensitivity analysis can be useful not only for understanding model behavior, but also for identifying the biological processes that may be most important in determining pathogen transmission. Obtaining accurate empirical estimates of the parameters capturing the most important biological processes may then improve the accuracy of disease models and the ability of models to inform surveillance and control strategies. In this study, we conducted a sensitivity analysis of the complex dengue simulation models originally developed by Focks and others. 7, 8