Sensitivity analysis of infectious disease models: methods, advances and their application

Wu, Jianyong; Dhingra, Radhika; Gambhir, Manoj; Remais, Justin V.

doi:10.1098/rsif.2012.1018

Cited by 255 publications

(213 citation statements)

References 45 publications

(131 reference statements)

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“…Given these uncertainties it is imperative that thorough model sensitivity analyses are to be carried out [26], not only to gain a better understanding of the full range in qualitative and quantitative model behaviour under parameter changes, but also to highlight the most important knowledge gaps. In that respect, sensitivity analyses need to go beyond finding and reporting on the most influential parameters but crucially have to incorporate uncertainties in parameter values as well as in the underlying model structure and assumptions [27].…”

Section: Model and Other Uncertaintiesmentioning

confidence: 99%

Assessing dengue vaccination impact: Model challenges and future directions

Recker

Vannice

Hombach

et al. 2016

Vaccine

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a b s t r a c tIn response to the sharp rise in the global burden caused by dengue virus (DENV) over the last few decades, the WHO has set out three specific key objectives in its disease control strategy: (i) to estimate the true burden of dengue by 2015; (ii) a reduction in dengue mortality by at least 50% by 2020 (used as a baseline); and (iii) a reduction in dengue morbidity by at least 25% by 2020. Although various elements will all play crucial parts in achieving this goal, from diagnosis and case management to integrated surveillance and outbreak response, sustainable vector control, vaccine implementation and finally operational and implementation research, it seems clear that new tools (e.g. a safe and effective vaccine and/ or effective vector control) are key to success. The first dengue vaccine was licensed in December 2015, Dengvaxia Ò (CYD-TDV) developed by Sanofi Pasteur. The WHO has provided guidance on the use of CYD-TDV in endemic countries, for which there are a variety of considerations beyond the risk-benefit evaluation done by regulatory authorities, including public health impact and cost-effectiveness. Populationlevel vaccine impact and economic and financial aspects are two issues that can potentially be considered by means of mathematical modelling, especially for new products for which empirical data are still lacking. In December 2014 a meeting was convened by the WHO in order to revisit the current status of dengue transmission models and their utility for public health decision-making. Here, we report on the main points of discussion and the conclusions of this meeting, as well as next steps for maximising the use of mathematical models for vaccine decision-making.

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Section: Model and Other Uncertaintiesmentioning

confidence: 99%

Assessing dengue vaccination impact: Model challenges and future directions

Recker

Vannice

Hombach

et al. 2016

Vaccine

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“…The observation-based range of 3-8 TgN yr −1 in the magnitude of these emissions could result in a 10 % difference in predicted tropospheric ozone burden. OAT sensitivity analysis is used in a variety of research fields including environmental science (Bailis et al, 2005;Campbell et al, 2008;de Gee et al, 2008;Saltelli and Annoni, 2010), medicine (Coggan et al, 2005;Stites et al, 2007;Wu et al, 2013), economics (Ahtikoski et al, 2008) and physics (Hill et al, 2012). While the ease of implementing OAT sensitivity analysis is appealing, a major drawback of this approach is that it assumes that the model response to different inputs is independent, which in most cases is unjustified (Saltelli and Annoni, 2010) and can result in biased results .…”

Section: Different Approaches For Sensitivity Analysismentioning

confidence: 99%

Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output

et al. 2018

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Abstract. Global sensitivity analysis (GSA) is a powerful approach in identifying which inputs or parameters most affect a model's output. This determines which inputs to include when performing model calibration or uncertainty analysis. GSA allows quantification of the sensitivity index (SI) of a particular input -the percentage of the total variability in the output attributed to the changes in that input -by averaging over the other inputs rather than fixing them at specific values. Traditional methods of computing the SIs using the Sobol and extended Fourier Amplitude Sensitivity Test (eFAST) methods involve running a model thousands of times, but this may not be feasible for computationally expensive Earth system models. GSA methods that use a statistical emulator in place of the expensive model are popular, as they require far fewer model runs. We performed an eight-input GSA, using the Sobol and eFAST methods, on two computationally expensive atmospheric chemical transport models using emulators that were trained with 80 runs of the models. We considered two methods to further reduce the computational cost of GSA: (1) a dimension reduction approach and (2) an emulator-free approach. When the output of a model is multi-dimensional, it is common practice to build a separate emulator for each dimension of the output space. Here, we used principal component analysis (PCA) to reduce the output dimension, built an emulator for each of the transformed outputs, and then computed SIs of the reconstructed output using the Sobol method. We considered the global distribution of the annual column mean lifetime of atmospheric methane, which requires ∼ 2000 emulators without PCA but only 5-40 emulators with PCA. We also applied an emulator-free method using a generalised additive model (GAM) to estimate the SIs using only the training runs. Compared to the emulator-only methods, the emulator-PCA and GAM methods accurately estimated the SIs of the ∼ 2000 methane lifetime outputs but were on average 24 and 37 times faster, respectively.

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“…Many reviews of SA methods have been conducted in different fields. In particular, Hamby (1994) reviewed the literature on parameter SA for environmental models; Frey and Patil (2002) and Mokhtari and Frey (2005) reviewed the SA methods used for food safety; Coyle et al (2003) discussed the SA measures employed in the field of economics; Saltelli et al (2005Saltelli et al ( , 2012 focused on SA in chemical models; Borgonovo (2007) investigated sensitivity and uncertainty measures; Mishra et al (2009) reviewed the global SA methods used in groundwater models; Peter and Dwight (2010) discussed numerical SA approaches for aerodynamic optimization; Perz et al (2013) reviewed the global SA and UA methods applied to ecological resilience; Tian (2013) summarized the application of SA methods to building energy analysis; and Wu et al (2013) review recent advances in SA for infectious disease models. Some of these reviews explicitly highlighted the advantages and disadvantages of various methods and they provided very good summaries of these topics.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

Song

Zhang

Zhan

et al. 2015

Journal of Hydrology

470

273

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s u m m a r y Sensitivity analysis (SA) aims to identify the key parameters that affect model performance and it plays important roles in model parameterization, calibration, optimization, and uncertainty quantification. However, the increasing complexity of hydrological models means that a large number of parameters need to be estimated. To better understand how these complex models work, efficient SA methods should be applied before the application of hydrological modeling. This study provides a comprehensive review of global SA methods in the field of hydrological modeling. The common definitions of SA and the typical categories of SA methods are described. A wide variety of global SA methods have been introduced to provide a more efficient evaluation framework for hydrological modeling. We review, analyze, and categorize research into global SA methods and their applications, with an emphasis on the research accomplished in the hydrological modeling field. The advantages and disadvantages are also discussed and summarized. An application framework and the typical practical steps involved in SA for hydrological modeling are outlined. Further discussions cover several important and often overlooked topics, including the relationship between parameter identification, uncertainty analysis, and optimization in hydrological modeling, how to deal with correlated parameters, and time-varying SA. Finally, some conclusions and guidance recommendations on SA in hydrological modeling are provided, as well as a list of important future research directions that may facilitate more robust analyses when assessing hydrological modeling performance.

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Sensitivity analysis of infectious disease models: methods, advances and their application

Cited by 255 publications

References 45 publications

Assessing dengue vaccination impact: Model challenges and future directions

Assessing dengue vaccination impact: Model challenges and future directions

Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

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