Decision makers\u27 experience of participatory dynamic simulation modelling: Methods for public health policy

Background: Systems science methods such as dynamic simulation modelling are well suited to address questions about public health policy as they consider the complexity, context and dynamic nature of system-wide behaviours. Advances in technology have led to increased accessibility and interest in systems methods to address complex health policy issues. However, the involvement of policy decision makers in health-related simulation model development has been lacking. Where end-users have been included, there has been limited examination of their experience of the participatory modelling process and their views about the utility of the findings. This paper reports the experience of end-user decision makers, including senior public health policy makers and health service providers, who participated in three participatory simulation modelling for health policy case studies (alcohol related harm, childhood obesity prevention, diabetes in pregnancy), and their perceptions of the value and efficacy of this method in an applied health sector context. Methods: Semi-structured interviews were conducted with end-user participants from three participatory simulation modelling case studies in Australian real-world policy settings. Interviewees were employees of government agencies with jurisdiction over policy and program decisions and were purposively selected to include perspectives at different stages of model development. Results: The ‘co-production’ aspect of the participatory approach was highly valued. It was reported as an essential component of building understanding of the modelling process, and thus trust in the model and its outputs as a decision-support tool. The unique benefits of simulation modelling included its capacity to explore interactions of risk factors and combined interventions, and the impact of scaling up interventions. Participants also valued simulating new interventions prior to implementation in the real world, and the comprehensive mapping of evidence and its gaps to prioritise future research. The participatory aspect of simulation modelling was time and resource intensive and therefore most suited to high priority complex topics with contested options for intervening. Conclusion: These findings highlight the value of a participatory approach to dynamic simulation modelling to support its utility in applied health policy settings

Knowledge mobilisation for policy development: Implementing systems approaches through participatory dynamic simulation modelling

Background: Evidence-based decision-making is an important foundation for health policy and service planning decisions, yet there remain challenges in ensuring that the many forms of available evidence are considered when decisions are being made. Mobilising knowledge for policy and practice is an emergent process, and one that is highly relational, often messy and profoundly context dependent. Systems approaches, such as dynamic simulation modelling can be used to examine both complex health issues and the context in which they are embedded, and to develop decision support tools. Objective: This paper reports on the novel use of participatory simulation modelling as a knowledge mobilization tool in Australian real-world policy settings. We describe how this approach combined systems science methodology and some of the core elements of knowledge mobilisation best practice. We describe the strategies adopted in three case studies to address both technical and socio-political issues, and compile the experiential lessons derived. Finally, we consider the implications of these knowledge mobilisation case studies and provide evidence for the feasibility of this approach in policy development settings. Conclusion: Participatory dynamic simulation modelling builds on contemporary knowledge mobilisation approaches for health stakeholders to collaborate and explore policy and health service scenarios for priority public health topics. The participatory methods place the decision-maker at the centre of the process and embed deliberative methods and co-production of knowledge. The simulation models function as health policy and programme dynamic decision support tools that integrate diverse forms of evidence, including research evidence, expert knowledge and localized contextual information. Further research is underway to determine the impact of these methods on health service decision-making

Real-time FPGA-based co-simulation of large scale power systems

With the rapid increase of size and complexity of modem electrical power systems, 1) the simulation accuracy and 2) the capability of simulating large power systems have become two conflicting objectives. This thesis proposes a novel FPGA-RTDS co-simulator to meet these two objectives. As the basis of the co-simulator, a library of power system components is developed in FPGA, including the most commonly used power system elements and control systems. The proposed cosimulator combines the advantages of 1) the paralleled architecture, high logic density and high clock speed from FPGA and 2) better modelling flexibility and user-friendly GUI from RTDS together. Multi-FPGA structure is introduced to further improve the simulation capability for large power systems. The use of detailed EMT models in the whole system guarantees the accuracy of simulation and eliminates the potential interface error. Deeply pipelined and massively paralleled algorithms have been designed to maximize time and hardware efficiency. The modular design significantly improves the system expandability. Case studies including large scale power system with more than 4000 nodes are presented to demonstrate the simulation capability. Comparisons are made with SIMULINK and RTDS to verify the accuracy of the proposed co-simulator

“Turning mirrors into windows”: A study of participatory dynamic simulation modelling to inform health policy decisions

Introduction: Achieving evidence-based public health policy is challenging. There is increasing recognition that more sophisticated, system-science, analytic methods, such as dynamic simulation modelling (DSM), are needed to better understand the dynamic, interacting and interrelated elements within complex public health systems. This thesis explored the implementation, feasibility and value of a novel participatory DSM approach as a tool for knowledge mobilisation and decision support in Australian health policy settings. An indepth case study of participatory modelling of Diabetes in Pregnancy (DIP) in the Australian Capital Territory (2016-2018) was conducted. Two additional modelling case studies focusing on prevention of childhood overweight and obesity and alcohol-related harms in New South Wales provided supplementary data across different settings. Methods: A multidisciplinary stakeholder group, including researchers, clinicians, public health practitioners, policy makers, and simulation modelling experts, was convened to coproduce a pioneering, multi-method DSM to inform DIP health service policy and planning. Using participatory action research methods, interviews with participants, recordings from model development workshops and meetings, participatory research field notes and other documents were analysed to determine the feasibiliy and value of the participatory model development process. The analysis explored the deliberations, challenges, opportunities and decisions involved. Interviews with end-user participants for the primary and additional case studies explored their perceptions of the utility and value of this approach in applied settings. Results: Participatory DSM builds on elements of best practice in knowledge mobilisation, including embedding deliberative methods to build shared understanding. The methods enabled a collaborative, co-production approach to evidence-informed practice that moved beyond evidence synthesis to provide dynamic decision support. The participatory process was iterative, with key decisions re-visited and refined throughout the process. It facilitated a significant, interdisciplinary knowledge base, built understanding of the modelling process, and established trust in the model to inform policy decisions. Key insights relating to the prevention and management of DIP were gained. The importance of implementing and maintaining population interventions promoting healthy weight for children and young adults was demonstrated. The unique benefits of simulation modelling most valued by health sector decision makers were its capacity to explore risk factor interactions, compare the outcomes of alternative intervention combinations, and consider the impacts of scaling-up. Participants also valued simulating new interventions prior to implementation, and mapping evidence gaps to prioritise future research. Discussion: Using a participatory approach to DSM for health policy is feasible and enhances the value of models as knowledge mobilisation and health policy decision support tools. The detailed analysis in this thesis revealed the socio-technical opportunities and challenges of implementing these interdisciplinary methods at the intersection of systems science, knowledge mobilisation and public health policy, and the key elements required for successful implementation in applied health policy settings

Interaction Of myxomatosis and Rabbit Haemorrhagic Disease in wild rabbit

Increasing resistance of rabbits to myxomatosis in Australia has led to the exploration of Rabbit Haemorrhagic Disease, also called Rabbit Calicivirus Disease (RCD) as a possible control agent. While the initial spread of RCD in Australia resulted in widespread rabbit mortality in affected areas, the possible population dynamic effects of RCD and myxomatosis operating within the same system have not been properly explored. Here we present early mathematical modelling examining the interaction between the two diseases. In this study we use a deterministic compartment model, based on the classical SIR model in infectious disease modelling. We consider, here, only a single strain of myxomatosis and RCD and neglect latent periods. We also include logistic population growth, with the inclusion of seasonal birth rates. We assume there is no cross-immunity due to either disease. The mathematical model allows for the possibility of both diseases to be simultaneously present in an individual, although results are also presented for the case where co-infection is not possible, since co-infection is thought to be rare and questions exist as to whether it can occur. The simulation results of this investigation show that it is a crucial issue and should be part of future field studies. A single simultaneous outbreak of RCD and myxomatosis was simulated, while ignoring natural births and deaths, appropriate for a short timescale of 20 days. Simultaneous outbreaks may be more common in Queensland. For the case where co-infection is not possible we find that the simultaneous presence of myxomatosis in the population suppresses the prevalence of RCD, compared to an outbreak of RCD with no outbreak of myxomatosis, and thus leads to a less effective control of the population. The reason for this is that infection with myxomatosis removes potentially susceptible rabbits from the possibility of infection with RCD (like a vaccination effect). We found that the reduction in the maximum prevalence of RCD was approximately 30% for an initial prevalence of 20% of myxomatosis, for the case where there was no simultaneous outbreak of myxomatosis, but the peak prevalence was only 15% when there was a simultaneous outbreak of myxomatosis. However, this maximum reduction will depend on other param- eter values chosen. When co-infection is allowed then this suppression effect does occur but to a lesser degree. This is because the rabbits infected with both diseases reduces the prevalence of myxomatosis. We also simulated multiple outbreaks over a longer timescale of 10 years, including natural population growth rates, with seasonal birth rates and density dependent (logistic) death rates. This shows how both diseases interact with each other and with population growth. Here we obtain sustained outbreaks occurring approximately every two years for the case of a simultaneous outbreak of both diseases but without simultaneous co-infection, with the prevalence varying from 0.1 to 0.5. Without myxomatosis present then the simulation predicts RCD dies out quickly without further introduction from elsewhere. With the possibility of simultaneous co-infection of rabbits, sustained outbreaks are possible but then the outbreaks are less severe and more frequent (approximately yearly). While further model development is needed, our work to date suggests that: 1) the diseases are likely to interact via their impacts on rabbit abundance levels, and 2) introduction of RCD can suppress myx- omatosis prevalence. We recommend that further modelling in conjunction with field studies be carried out to further investigate how these two diseases interact in the population

Co-Simulation Methods for Holistic Vehicle Design: A Comparison

Vehicle development involves the design and integration of subsystems of different domains to meet performance, efficiency, and emissions targets set during the initial developmental stages. Before a physical prototype of a vehicle or vehicle powertrain is tested, engineers build and test virtual prototypes of the design(s) on multiple stages throughout the development cycle. In addition, controllers and physical prototypes of subsystems are tested under simulated signals before a physical prototype of the vehicle is available. Different departments within an automotive company tend to use different modelling and simulation tools specific to the needs of their specific engineering discipline. While this makes sense considering the development of the said system, subsystem, or component, modern holistic vehicle engineering requires the constituent parts to operate in synergy with one-another in order to ensure vehicle-level optimal performance. Due to the above, integrated simulation of the models developed in different environments is necessary. While a large volume of existing co-simulation related publications aimed towards engineering software developers, user-oriented publications on the characteristics of integration methods are very limited. This paper reviews the current trends in model integration methods applied within the automotive industry. The reviewed model integration methods are evaluated and compared with respect to an array of criteria such as required workflow, software requirements, numerical results, and simulation speed by means of setting up and carrying out simulations on a set of different model integration case studies. The results of this evaluation constitute a comparative analysis of the suitability of each integration method for different automotive design applications. This comparison is aimed towards the end-users of simulation tools, who in the process of setting up a holistic high-level vehicle model, may have to select the most suitable among an array of available model integration techniques, given the application and the set of selection criteria