Combining System Dynamics and Agent-Based Simulation to Study the Effects of Public Interventions on Poverty

Poverty is a multidimensional social phenomenon that occurs in every economy around the world. Counteracting poverty is one of the tasks of public administration units. Under many programs financed from public funds, various tools and instruments can be used to combat poverty, but their implementation should be preceded by an in-depth analysis of the effects generated by their use. This is not easy, because the phenomenon of poverty is very complex, results from the arrangement of many interrelated heterogeneous elements, and the effects of actions are visible only after a long time. Hence, research in this area requires the use of an approach that can cope with the complexity of this phenomenon in dynamic terms. The aim of the article is to present the concept of a hybrid simulation model for studying the impact of public intervention on the level of poverty at local, regional and national level. The model is a hybrid of two computer simulation methods: System Dynamics (SD) and Agent-Based Simulation (ABS). SD method is used to model macroelements of the examined system (e.g. GDP level, labor market) and microelements (e.g. households and their members) are modeled using ABS. The article also shows the results of verification and validation of the proposed solution performed using the model for a case study. The presented solution can be used both by public administration units at various levels as well as by scientists - to conduct socio-economic research

A unified inter-host and in-host model of antibiotic resistance and infection spread in a hospital ward

As the battle continues against hospital-acquired infections and the concurrent rise in antibiotic resistance among many of the major causative pathogens, there is a dire need to conduct controlled experiments, in order to compare proposed control strategies. However, cost, time, and ethical considerations make this evaluation strategy either impractical or impossible to implement with living patients. This paper presents a multi-scale model that offers promise as the basis for a tool to simulate these (and other) controlled experiments. This is a “unified” model in two important ways: (i) It combines inter-host and in-host dynamics into a single model, and (ii) it links two very different modeling approaches - agent-based modeling and differential equations - into a single model. The potential of this model as an instrument to combat antibiotic resistance in hospitals is demonstrated with numerical examples

Toward a Theory of Multi-Method Modeling and Simulation Approach

The representation via simulation models can easily lead to simulation models too simple for their intended purpose, or with too much detail, making them hard to understand. This problem is related to limitations of the modeling and simulation methods. A multi-method Modeling and Simulation (M&S) approach has the potential for improved representation by taking advantage of methods\u27 strengths and mitigating their weaknesses. Despite a high appeal for using multiple M&S methods, several related problems should be addressed first. The current level of theoretical, methodological, and pragmatic knowledge related to a multi-method M&S approach is limited. It is problematic that there is no clearly identified purpose and definition of the multi-method M&S approach. Theoretical and methodological advances are vital to enhancing the application of a multi-method M&S approach to address a broader range of scientific inquiries, improve quality of research, and enable finding common ground between scientific domains. This dissertation explored theoretical principles and research guidelines of a multi-method M&S approach. The analyzed literature offered perspectives related to the purpose, terms, and research guidelines of a multi-method M&S approach. A pragmatic philosophical stance was used to provide the basis for the choice of terms and definitions relevant to a multi-method M&S approach were proposed. The degrees of falsifiability are adapted to the M&S domain, which allowed for developing complementarity principles as the theoretical basis of a multi-method M&S approach. Next, a blueprint of a multi-method M&S approach called method formats was derived, because transitions toward formats must seek justifications in order to increase research objectivity and transparency. A sample set of methods was explored in the context of a proposed sample set of criteria. None of the methods were evaluated with the maximum score for every criterion, which implied that if all those characteristics were required within a research context, then, none of the methods could provide the highest possible score without combining methods. Finally, a case study that included a multi-method simulation model was developed, providing a data layer for evaluation of complementarity principles. The case study contributed to the credibility of complementarity principles as a reason to use a multi-method M&S approach and value of pseudo-triangulation as a mean of verification of a selected approach

Using Case-Based Reasoning for Simulation Modeling in Healthcare

The healthcare system is always defined as a complex system. At its core, it is a system composed of people and processes and requires performance of different tasks and duties. This complexity means that the healthcare system has many stakeholders with different interests, resulting in the emergence of many problems such as increasing healthcare costs, limited resources and low utilization, limited facilities and workforce, and poor quality of services. The use of simulation techniques to aid in solving healthcare problems is not new, but it has increased in recent years. This application faces many challenges, including a lack of real data, complicated healthcare decision making processes, low stakeholder involvement, and the working environment in the healthcare field. The objective of this research is to study the utilization of case-based reasoning in simulation modeling in the healthcare sector. This utilization would increase the involvement of stakeholders in the analysis process of the simulation modeling. This involvement would help in reducing the time needed to build the simulation model and facilitate the implementation of results and recommendations. The use of case-based reasoning will minimize the required efforts by automating the process of finding solutions. This automation uses the knowledge in the previously solved problems to develop new solutions. Thus, people could utilize the simulation modeling with little knowledge about simulation and the working environment in the healthcare field. In this study, a number of simulation cases from the healthcare field have been collected to develop the case-base. After that, an indexing system was created to store these cases in the case-base. This system defined a set of attributes for each simulation case. After that, two retrieval approaches were used as retrieval engines. These approaches are K nearest neighbors and induction tree. The validation procedure started by selecting a case study from the healthcare literature and implementing the proposed method in this study. Finally, healthcare experts were consulted to validate the results of this study

Operations research models for investigation and improvement of the hyperacute stroke care system

Stroke is the third most common cause of death and the sixth major cause of disability around the world with ischemic stroke accounting for around 80% of all strokes. It has been clinically indicated in treating ischemic stroke patients that maximum benefits can be achieved with the speediest arterial recanalization by effective and fast application of existing acute therapies. These therapies comprise either (1) dissolving the blood clot using Intravenous Tissue Plasminogen Activator (IV tPA) treatment or (2) physically removing the clot from the artery using endovascular thrombectomy treatment. These treatments should be performed within the hyperacute time window of 6 hours from stroke onset. For nearly two decades until late 2014, the intravenous thrombolysis delivered to patients was the most effective treatment for stroke patients. This was administrated within a maximum of 4.5 hours from stroke onset. In early 2015, results of five clinical trials from different parts of the world demonstrated the effectiveness of the endovascular thrombectomy therapy. This was provided within 6 hours of stroke onset for the eligible stroke patients who already have received thrombolysis treatment. Research presented in this thesis is the first attempt to quantify the link between the earlier treatment and long-term benefits for the hyperacute stroke patients. Moreover, with the gradual emergence of new evidence about effectiveness of the endovascular thrombectomy treatment in the hyperacute stroke care systems, new questions were raised in the clinical literature since not all hospitals have the expertise and equipment required for delivering the endovascular thrombectomy treatment. Some of the most burning questions were formulated in an Editorial article published in the Journal of the American Medical Association (JAMA) by Warach and Johnson (2016). These questions mainly concern the issue of treatment pathway selection between two groups of hospitals with different facilities and expertise to support new investigations in the hyperacute stroke care system by comparing the long-term benefits for individual patients. This research demonstrates how Operations Research (OR) models can be used to answer these and other questions in the hyperacute stroke care system. It is specifically focused on OR models for investigation and improvement to provide better understanding of the complex decisions arising in the hyperacute stroke care system. The main aimof this thesis is to investigate the issue of design, development and validation of OR models used for investigation and improvement of the hyperacute stroke care system. Thus, this work addresses very recent and important questions in the field to support more effective and efficient provision of the services to stroke patients. Three OR models for investigation and improvement are designed and validated in this thesis: (1) ’IV tPA’ model, (2) ‘Endovascular Thrombectomy’ model, and (3) ‘Individual Patient’ model. The first two OR models are used to provide an understanding of the long-term population benefits of faster access to stroke treatment interventions. Based on the first two OR models, one minute earlier of IV tPA and endovascular thrombectomy interventions respectively on average provide 1.8 days and 3.2 extra days of healthy life for the stroke patients. The third OR model is used to provide assistance with maximizing the individual patient’s life-time benefits over two pathways of the hyperacute stroke care system. Finally, we present a novel validation framework that is used to validate all three OR models developed in this thesis. This research contributes to OR/MS literature by design, development and validation of OR models used to provide an improved understanding of the long-term population and individual patient’s benefits due to faster delivery of stroke treatment interventions in the hyperacute stroke care system. A discussion on the validation of OR models is also novel and further addresses the existing gaps in OR/MS literature

Using systems thinking to optimise health system interventions for improved maternal and child health in low-resource settings

Payment for performance (P4P) initiatives have been employed in low and middle-income (LMIC) countries as a means to improve the delivery and coverage of maternal and child health (MCH) services. Despite widespread implementation, there is still a lack of consensus on whether P4P is an effective initiative that leads to positive, sustained improvement in delivery of these services. There is a need to employ methods that can evaluate the pathways through which P4P alters health systems without diminishing the complex behaviour exhibited by health systems in the evaluation. Two methods for evaluation of complex systems were used to model the impact of a P4P programme on the delivery and uptake of MCH services in Tanzania: causal loop diagrams (CLDs) and system dynamics modelling (SDM). The CLD represents relationships between variables that are important when we consider how the health system responds and transforms under P4P. The CLD was developed using qualitative data from a process evaluation of a P4P programme in Tanzania and stakeholder consultation. The CLD was then used to build a quantitative SDM, using primary (stakeholder consultation) and secondary (impact evaluation of P4P programme, official statistics and reports) data sources. In the SDM, changes in design, implementation, and context (availability and supply of drugs, access to alternative sources of funding, staffing) were tested to explore the impact on key outcomes (percentage of women who received two doses of intermittent preventive treatment during antenatal care and percentage of women who had a facility-based delivery) and the effectiveness of the programme. The CLD pinpoints the key mechanisms underpinning provider achievement of P4P targets, reporting of health information by providers, and care seeking by the population, and identifies those mechanisms affected by P4P. For example, the availability of drugs and medical commodities was critical not only to provider achievement of P4P targets (supply of MCH services) but also to demand of services and was impacted by P4P through the availability of additional facility resources. In the SDM, severe delays in payment and change in allocation of payments (between staff and operations) impacted key outcomes, with changes in contextual factors (particularly provision of medicine) facilitating or hindering facility performance. Recommendations for programme design must consider the impact on the holistic system, to avoid suboptimal programme impact or unintended, negative consequences. Our study shows how secondary data from an impact and process evaluation can be used to model the health system and its response to P4P, to improve our understanding of programme mechanisms and inform the design of more effective future P4P programmes. This work will not only be relevant for P4P in Tanzania but also generate policy relevant recommendations for LMICs