System dynamics simulation for evaluating implementation strategies of genomic sequencing: tutorial and conceptual model

INTRODUCTION

Precision Medicine (PM), especially in oncology, involve diagnostic and complex treatment pathways that are based on genomic features. To conduct evaluation and decision analysis for PM, advanced modeling techniques are needed due to its complexity. Although System Dynamics (SD) has strong modeling power, it has not been widely used in PM and individualized treatment.

AREAS COVERED

We explained SD tools using examples in cancer context and the rationale behind using SD for genomic testing and personalized oncology. We compared SD with other Dynamic Simulation Modelling (DSM) methods and listed SD's advantages. We developed a conceptual model using Causal Loop Diagram (CLD) for strategic decision-making in Whole Genome Sequencing (WGS) implementation.

EXPERT OPINION

The paper demonstrates that SD is well-suited for health policy evaluation challenges and has useful tools for modeling precision oncology and genomic testing. SD's system-oriented modeling captures dynamic and complex interactions within systems using feedback loops. SD models are simple to implement, utilize less data and computational resources, and conduct both exploratory and explanatory analyses over time. If the targeted system has complex interactions and many components, deals with lack of data, and requires interpretability and clinicians' input, SD offers attractive advantages for modeling and evaluating scenarios.

Corrigendum: "A new hope" for positive psychology: a dynamic systems reconceptualization of hope theory

[This corrects the article DOI: 10.3389/fpsyg.2022.809053.].

Implementing a stakeholder-driven community diffusion-informed intervention to create healthier, more equitable systems: a community case study in Greenville County, South Carolina

This case study describes the application of a theory-informed, stakeholder-driven intervention with a group of 19 multi-sector stakeholders from an existing coalition to promote whole-of-community change that supports childhood obesity prevention. The intervention applied community-based system dynamics to design and implement activities that promoted insights into the systems driving childhood obesity prevalence and helped participants prioritize actions to influence those systems. This led to three new priority areas for the coalition: addressing food insecurity; building power among historically marginalized voices within the community; and supporting advocacy efforts to promote community-wide change beyond the coalition's previous focus on organizational-level policy, systems and environment change. The intervention spurred the application of community-based system dynamics to other health issues and in partner organizations, which demonstrates paradigm shifts about how to address complex public health issues in the community.

"A New Hope" for Positive Psychology: A Dynamic Systems Reconceptualization of Hope Theory

In this review of the central tenets of hope theory, we examine the meta-theoretical, theoretical, and methodological foundations of the literature base. Our analysis moves from a broad examination of the research landscape in hope theory across disciplines, to a deeper investigation of the empirical literature in university students. This review highlights the significant impact of this body of research in advancing our understanding of aspects of thriving characterized by hope. However, we also evidence several limitations that may impede the advancement of the next wave of growth in this field. To address these limitations, we argue for an interdisciplinary approach to expanding the meta-theoretical, theoretical, and methodological horizons, enabling a more dynamic systems approach to the study of hope. Drawing on the intersection of positive psychology with systems thinking, we describe a methodological approach that enables a deeper examination of the processes and interactions through which hope emerges, using an analysis of the lived experience of young people. It is proposed that this research agenda will bring to life an alternate story about the resourcefulness of our youth through their own voice, enabling us to leverage this in the design of more effective strategies to facilitate hope in the future. This research agenda provides a roadmap that will provide alternative methodologies that address the current limitations in the field of hope research and, importantly, can provide fuel to spur on the acceleration of the next wave of research and practice in the field of positive psychology more broadly.

Understanding obesity-related behaviors in youth from a systems dynamics perspective: The use of causal loop diagrams

This paper reports how we applied systems dynamics methods to gain insight into the complexity of obesity-related behaviors in youth, including diet, physical activity, sedentary behavior, and sleep, by integrating a literature review into causal loop diagrams (CLDs). Results showed that the CLDs consisted of multiple subsystems and three types of dynamics appeared, including (1) feedback loops, (2) connections between feedback loops and subsystems, and (3) mechanisms. We observed clear similarities in the dynamics for the four behaviors in that they relate to "traditional" subsystems, such as home and school environments, as well as to newly added subsystems, including macroeconomics, social welfare, and urban systems. The CLDs provided insights that can support the development of intervention strategies, including (1) the confirmation that a range of mechanisms cover and connect multiple levels and settings, meaning that there is no silver bullet to address obesity; (2) understanding of how interventions in one particular setting, such as school, might be influenced by the interactions with other settings, such as urban systems; and (3) a comprehensive view of (un)intended consequences. This way of framing the problem will assist moving towards public health interventions that respond to and operate in the complexity of the real world.

Building Social-Ecological System Resilience to Tackle Antimicrobial Resistance Across the One Health Spectrum: Protocol for a Mixed Methods Study

BACKGROUND

Antimicrobial resistance (AMR) is an escalating global crisis with serious health, social, and economic consequences. Building social-ecological system resilience to reduce AMR and mitigate its impacts is critical.

OBJECTIVE

The aim of this study is to compare and assess interventions that address AMR across the One Health spectrum and determine what actions will help to build social and ecological capacity and readiness to sustainably tackle AMR.

METHODS

We will apply social-ecological resilience theory to AMR in an explicit One Health context using mixed methods and identify interventions that address AMR and its key pressure antimicrobial use (AMU) identified in the scientific literature and in the gray literature using a web-based survey. Intervention impacts and the factors that challenge or contribute to the success of interventions will be determined, triangulated against expert opinions in participatory workshops and complemented using quantitative time series analyses. We will then identify indicators using regression modeling, which can predict national and regional AMU or AMR dynamics across animal and human health. Together, these analyses will help to quantify the causal loop diagrams (CLDs) of AMR in the European and Southeast Asian food system contexts that are developed by diverse stakeholders in participatory workshops. Then, using these CLDs, the long-term impacts of selected interventions on AMR will be explored under alternate future scenarios via simulation modeling and participatory workshops. A publicly available learning platform housing information about interventions on AMR from a One Health perspective will be developed to help decision makers identify promising interventions for application in their jurisdictions.

RESULTS

To date, 669 interventions have been identified in the scientific literature, 891 participants received a survey invitation, and 4 expert feedback and 4 model-building workshops have been conducted. Time series analysis, regression modeling of national and regional indicators of AMR dynamics, and scenario modeling activities are anticipated to be completed by spring 2022. Ethical approval has been obtained from the University of Waterloo's Office of Research Ethics (ethics numbers 40519 and 41781).

CONCLUSIONS

This paper provides an example of how to study complex problems such as AMR, which require the integration of knowledge across sectors and disciplines to find sustainable solutions. We anticipate that our study will contribute to a better understanding of what actions to take and in what contexts to ensure long-term success in mitigating AMR and its impact and provide useful tools (eg, CLDs, simulation models, and public databases of compiled interventions) to guide management and policy decisions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/24378.

Electrofuel Synthesis from Variable Renewable Electricity: An Optimization-Based Techno-Economic Analysis

Sectors such as aviation may require low-carbon liquid fuels to dramatically reduce emissions. This analysis characterizes the economic viability of electrofuels, synthesized from CO₂ from direct air capture (DAC) and hydrogen from electrolysis of water, powered primarily by solar or wind electricity. This optimization-based techno-economic analysis suggests that using today's technology, hydrocarbon electrofuels would cost upward of $4/liter of gasoline equivalent (lge), potentially falling to $1.7-1.8/lge in the next decade and <$1/lge by 2050. Only in the latter case are electrofuels potentially less costly than using petroleum fuels offset with DAC with sequestration. Achieving low-end electrofuel costs is contingent on substantial reductions in the capital cost of DAC, electrolyzers, and renewable electricity generation. However, the system also requires sufficient operational flexibility to efficiently power this capital-intensive equipment on variable electricity. Such forms of flexibility include various types of storage, supplementary natural gas and grid electricity interconnections (penalized with a steep carbon price), curtailment, and the ability to modestly adjust fuel synthesis and DAC operating levels over time scales of several hours to days.

Systems dynamics approach for modelling South Africa's response to COVID-19: A "what if" scenario

BACKGROUND

Many countries in the world are still struggling to control COVID-19 pandemic. As of April 28, 2020, South Africa reported the highest number of COVID-19 cases in Sub-Sahara Africa. The country took aggressive steps to control the spread of the virus including setting a national command team for COVID-19 and putting the country on a complete lockdown for more than 100 days. Evidence across most countries has shown that, it is vital to monitor the progression of pandemics and assess the effects of various public health measures, such as lockdowns. Countries need to have scientific tools to assist in monitoring and assessing the effectiveness of mitigation interventions. The objective of this study was thus to assess the extent to which a systems dynamics model can forecast COVID-19 infections in South Africa and be a useful tool in evaluating government interventions to manage the epidemic through 'what if' simulations.

DESIGN AND METHODS

This study presents a systems dynamics model (SD) of the COVID-19 infection in South Africa, as one of such tools. The development of the SD model in this study is grounded in design science research which fundamentally builds on prior research of modelling complex systems.

RESULTS

The SD model satisfactorily replicates the general trend of COVID-19 infections and recovery for South Africa within the first 100 days of the pandemic. The model further confirms that the decision to lockdown the country was a right one, otherwise the country's health capacity would have been overwhelmed. Going forward, the model predicts that the level of infection in the country will peak towards the last quarter of 2020, and thereafter start to decline.  Conclusions: Ultimately, the model structure and simulations suggest that a systems dynamics model can be a useful tool in monitoring, predicting and testing interventions to manage COVID-19 with an acceptable margin of error. Moreover, the model can be developed further to include more variables as more facts on the COVID-19 emerge.

Systems dynamics approach for modelling South Africa's response to COVID-19: A "what if" scenario

Background: Many countries in the world are still struggling to control COVID-19 pandemic. As of April 28, 2020, South Africa reported the highest number of COVID-19 cases in Sub- Sahara Africa. The country took aggressive steps to control the spread of the virus including setting a national command team for COVID-19 and putting the country on a complete lockdown for more than 100 days. Evidence across most countries has shown that, it is vital to monitor the progression of pandemics and assess the effects of various public health measures, such as lockdowns. Countries need to have scientific tools to assist in monitoring and assessing the effectiveness of mitigation interventions. The objective of this study was thus to assess the extent to which a systems dynamics model can forecast COVID-19 infections in South Africa and be a useful tool in evaluating government interventions to manage the epidemic through 'what if' simulations. Design and Methods: This study presents a systems dynamics model (SD) of the COVID-19 infection in South Africa, as one of such tools. The development of the SD model in this study is grounded in design science research which fundamentally builds on prior research of modelling complex systems. Results: The SD model satisfactorily replicates the general trend of COVID-19 infections and recovery for South Africa within the first 100 days of the pandemic. The model further confirms that the decision to lockdown the country was a right one, otherwise the country's health capacity would have been overwhelmed. Going forward, the model predicts that the level of infection in the country will peak towards the last quarter of 2020, and thereafter start to decline. Conclusions: Ultimately, the model structure and simulations suggest that a systems dynamics model can be a useful tool in monitoring, predicting and testing interventions to manage COVID-19 with an acceptable margin of error. Moreover, the model can be developed further to include more variables as more facts on the COVID-19 emerge.

Health system resilience in the face of crisis: analysing the challenges, strategies and capacities for UNRWA in Syria

Health system resilience reflects the ability to continue service delivery in the face of extraordinary shocks. We examined the case of the United Nations Relief and Works Agency (UNRWA) and its delivery of services to Palestine refugees in Syria during the ongoing crisis to identify factors enabling system resilience. The study is a retrospective qualitative case study utilizing diverse methods. We conducted 35 semi-structured interviews with UNRWA clinical and administrative professionals engaged in health service delivery over the period of the Syria conflict. Through a group model building session with a sub-group of eight of these participants, we then elicited a causal loop diagram of health system functioning over the course of the war, identifying pathways of threat and mitigating resilience strategies. We triangulated analysis with data from UNRWA annual reports and routine health management information. The UNRWA health system generally sustained service provision despite individual, community and system challenges that arose during the conflict. We distinguish absorptive, adaptive and transformative capacities of the system facilitating this resilience. Absorptive capacities enabled immediate crisis response, drawing on available human and organizational resources. Adaptive capacities sustained service delivery through revised logistical arrangements, enhanced collaborative mechanisms and organizational flexibility. Transformative capacity was evidenced by the creation of new services in response to changing community needs. Analysis suggests factors such as staff commitment, organizational flexibility and availability of collaboration mechanisms were important assets in maintaining service continuity and quality. This evidence regarding alternative strategies adopted to sustain service delivery in Syria is of clear relevance to other actors seeking organizational resilience in crisis contexts.

A System Dynamics and Participatory Action Research Approach to Promote Healthy Living and a Healthy Weight among 10-14-Year-Old Adolescents in Amsterdam: The LIKE Programme

This paper describes the design of the LIKE programme, which aims to tackle the complex problem of childhood overweight and obesity in 10–14-year-old adolescents using a systems dynamics and participatory approach. The LIKE programme focuses on the transition period from 10-years-old to teenager and was implemented in collaboration with the Amsterdam Healthy Weight Programme (AHWP) in Amsterdam-East, the Netherlands. The aim is to develop, implement and evaluate an integrated action programme at the levels of family, school, neighbourhood, health care and city. Following the principles of Participatory Action Research (PAR), we worked with our population and societal stakeholders as co-creators. Applying a system lens, we first obtained a dynamic picture of the pre-existing systems that shape adolescents’ behaviour relating to diet, physical activity, sleep and screen use. The subsequent action programme development was dynamic and adaptive, including quick actions focusing on system elements (quick evaluating, adapting and possibly catalysing further action) and more long-term actions focusing on system goals and/or paradigm change. The programme is supported by a developmental systems evaluation and the Intervention Level Framework, supplemented with routinely collected data on weight status and health behaviour change over a period of five years. In the coming years, we will report how this approach has worked to provide a robust understanding of the programme’s effectiveness within a complex dynamic system. In the meantime, we hope our study design serves as a source of inspiration for other public health intervention studies in complex systems.

Systemic Barriers and Equitable Interventions to Improve Vegetable and Fruit Intake in Children: Interviews with National Food System Actors

Fruit and vegetable (FV) intake is declining in New Zealand, and over half of New Zealand's children do not meet the recommendation of two serves of fruit and three serves of vegetables daily (with even lower adherence among children in high-deprivation neighbourhoods). The aim of this study was to map the potential causal pathways explaining this decline and possible actions to reverse it. Semi-structured interviews were held in April-May 2018 with 22 national actors from the produce industry, food distribution and retail sector, government, and NGO health organisations. The qualitative systems dynamics method of cognitive mapping was used to explore causal relationships within the food system that result in low FV intake among children. Barriers and solutions identified by participants were analysed using thematic analysis and according to a public health intervention framework. Participants were in agreement with the goal of improving FV intake for health and economic outcomes, and that health promotion strategies had been ineffectual to date due to multiple systemic barriers. Common barriers discussed were poverty, high food prices, low skills/knowledge, unhealthy food environments, climate change, and urbanization. Solutions with the strongest evidence of efficacy identified by the participants were subsidizing FVs and early childhood interventions to improve FV exposure.

Flexibility of Boolean Network Reservoir Computers in Approximating Arbitrary Recursive and Non-Recursive Binary Filters

Reservoir computers (RCs) are biology-inspired computational frameworks for signal processing that are typically implemented using recurrent neural networks. Recent work has shown that Boolean networks (BN) can also be used as reservoirs. We analyze the performance of BN RCs, measuring their flexibility and identifying the factors that determine the effective approximation of Boolean functions applied in a sliding-window fashion over a binary signal, both non-recursively and recursively. We train and test BN RCs of different sizes, signal connectivity, and in-degree to approximate three-bit, five-bit, and three-bit recursive binary functions, respectively. We analyze how BN RC parameters and function average sensitivity, which is a measure of function smoothness, affect approximation accuracy as well as the spread of accuracies for a single reservoir. We found that approximation accuracy and reservoir flexibility are highly dependent on RC parameters. Overall, our results indicate that not all reservoirs are equally flexible, and RC instantiation and training can be more efficient if this is taken into account. The optimum range of RC parameters opens up an angle of exploration for understanding how biological systems might be tuned to balance system restraints with processing capacity.

Effective implicit finite-difference method for sensitivity analysis of stiff stochastic discrete biochemical systems

Simulation of cellular processes is achieved through a range of mathematical modelling approaches. Deterministic differential equation models are a commonly used first strategy. However, because many biochemical processes are inherently probabilistic, stochastic models are often called for to capture the random fluctuations observed in these systems. In that context, the Chemical Master Equation (CME) is a widely used stochastic model of biochemical kinetics. Use of these models relies on estimates of kinetic parameters, which are often poorly constrained by experimental observations. Consequently, sensitivity analysis, which quantifies the dependence of systems dynamics on model parameters, is a valuable tool for model analysis and assessment. A number of approaches to sensitivity analysis of biochemical models have been developed. In this study, the authors present a novel method for estimation of sensitivity coefficients for CME models of biochemical reaction systems that span a wide range of time-scales. They make use of finite-difference approximations and adaptive implicit tau-leaping strategies to estimate sensitivities for these stiff models, resulting in significant computational efficiencies in comparison with previously published approaches of similar accuracy, as evidenced by illustrative applications.

Using group model building to develop a culturally grounded model of breastfeeding for low-income African American women in the USA

AIMS AND OBJECTIVES

To identify barriers and supporting factors for breastfeeding, and the dynamic interactions between them, as identified by low-income African American women and lactation peer helpers.

BACKGROUND

Stark breastfeeding disparities exist between African American mothers and their White counterparts in the USA. This pattern is often replicated across the globe, with marginalised populations demonstrating decreased breastfeeding rates. While breastfeeding research focused on sociocultural factors for different populations has been conducted, a more dynamic model of the factors impacting breastfeeding may help identify effective leverage points for change.

DESIGN

Group model building was used as a grounded theoretical approach, to build and validate a model representing factors impacting breastfeeding and the relationships between them.

METHODS

Low-income African American women (n = 21) and lactation peer helpers (n = 3) were engaged in model building sessions to identify factors impacting breastfeeding. A two-cycle process was used for analysis, in vivo and axial coding. The final factors and model were validated with a subgroup of participants.

RESULTS

The participants generated 82 factors that make breastfeeding easier, and 86 factors that make breastfeeding more challenging. These were grouped into 10 and 14 themes, respectively. A final model was constructed identifying three domains impacting breastfeeding: a mother's return to work or school, her knowledge, support and persistence, and the social acceptance of breastfeeding.

CONCLUSIONS

This study documented the sociocultural context within which low-income African American women are situated by identifying factors impacting breastfeeding, and the dynamic interactions between them. The model also provided various leverage points from which breastfeeding women can be supported.

RELEVANCE TO CLINICAL PRACTICE

Postpartum nurses are critical in supporting breastfeeding practices. To be most effective, they must be aware of the factors impacting breastfeeding, some of which may be unique to women based on their culture.

Scientific Collaborations: How Do We Measure the Return on Relationships?

Emerging infectious diseases (EIDs), the majority of which are zoonotic, represent a tremendous challenge for public health and biosurveillance infrastructure across the globe. Due to the complexity of zoonotic pathogens, it is essential that research and response to EIDs be a transdisciplinary effort. And while crisis and circumstance may be the initial catalyst for responding to an outbreak, we provide examples of how transdisciplinary scientific collectives, which are organized and solidified in advance of crises, can transform the way the world responds to outbreaks and in some cases could even prevent one from occurring (1). Current methods for assessing whether a cooperative engagement between countries is producing measurable and sustainable value is based on the ideas of return on investment and do not consider the inherent importance of relationships. In this article, we apply the idea of return on relationships (ROR) and propose a method for measuring ROR, using a system dynamics modeling framework commonly used in epidemiology. Tracking the numerous and diverse scientific collaborations that emerged from a training workshop for biosurveillance of bats held in Singapore in 2014, we apply a methodology for visualizing and measuring the relationship networks and outcomes that result. Additionally, the collaborative, multidisciplinary network that coalesced in response to the Hantavirus outbreak in New Mexico is 1993 is discussed as an example of the long-term benefits of ROR.

Innate immune programing by endotoxin and its pathological consequences

Monocytes and macrophages play pivotal roles in inflammation and homeostasis. Recent studies suggest that dynamic programing of macrophages and monocytes may give rise to distinct "memory" states. Lipopolysaccharide (LPS), a classical pattern recognition molecule, dynamically programs innate immune responses. Emerging studies have revealed complex dynamics of cellular responses to LPS, with high doses causing acute, resolving inflammation, while lower doses are associated with low-grade and chronic non-resolving inflammation. These phenomena hint at dynamic complexities of intra-cellular signaling circuits downstream of the Toll-like receptor 4 (TLR4). In this review, we examine pathological effects of varying LPS doses with respect to the dynamics of innate immune responses and key molecular regulatory circuits responsible for these effects.

Tool use as distributed cognition: how tools help, hinder and define manual skill

Our thesis in this paper is that, in order to appreciate the interplay between cognitive (goal-directed) and physical performance in tool use, it is necessary to determine the role that representations play in the use of tools. We argue that rather being solely a matter of internal (mental) representation, tool use makes use of the external representations that define the human–environment–tool–object system. This requires the notion of Distributed Cognition to encompass not simply the manner in which artifacts represent concepts but also how they represent praxis. Our argument is that this can be extended to include how artifacts-in-context afford use and how this response to affordances constitutes a particular form of skilled performance. By artifacts-in-context, we do not mean solely the affordances offered by the physical dimensions of a tool but also the interaction between the tool and the object that it is being used on. From this, “affordance” does not simply relate to the physical appearance of the tool but anticipates subsequent actions by the user directed towards the goal of changing the state of the object and this is best understood in terms of the “complimentarity” in the system. This assertion raises two challenges which are explored in this paper. The first is to distinguish “affordance” from the adaptation that one might expect to see in descriptions of motor control; when we speak of “affordance” as a form of anticipation, don’t we just mean the ability to adjust movements in response to physical demands? The second is to distinguish “affordance” from a schema of the tool; when we talk about anticipation, don’t we just mean the ability to call on a schema representing a “recipe” for using that tool for that task? This question of representation, specifically what knowledge needs to be represented in tool use, is central to this paper.