#160 Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure
Authors: Jonathan F. Donges, Carl-Friedrich Schleussner, Denis A. Engemann and Anders Levermann
Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that observed features of societal marginalization and clustering during transitions emerge naturally in a co-evolutionary adaptive network model that resolves the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how declining societal support for smoking affects individual behaviour and the network structure. Our results corroborate empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalised clusters. Herein self-amplifying feedbacks between individual behaviour and dynamic network restructuring play a pivotal role in the transition. We suggest a generative mechanism for the co-evolution of individual behaviour and social network structure that may apply to a wide range of examples beyond smoking such as sustainability transitions in lifestyle choices. Preprint: arxiv.org:1512.05013 [physics.soc-ph] (2015).
#175 Biases in healthcare data of gastroenteritis
Authors: Elizabeth Buckingham-Jeffery and Thomas House
Gastroenteritis is one of the most common illnesses worldwide. It is characterised by the symptoms of diarrhoea and vomiting. Although most cases of gastroenteritis in high income countries are self-limiting, there is a significant impact on healthcare services and the economy. Determining the prevalence of gastroenteritis is challenging. We have been exploring the possibility of establishing a comprehensive near real-time picture of the levels of activity of gastroenteritis in the UK by investigating the biases in presentation data from healthcare services.
Our modelling assumption is that there is an unobserved complex, noisy, non-linear process representing the true prevalence of different gastroenteritis-causing pathogens. No single data source perfectly correlates with this true prevalence, due to sampling at different rates from different sections of the population. These sampling biases must be understood in order to obtain a clearer picture of the burden of gastroenteritis and to make accurate predictions of future prevalence.
We compared data from an online cohort symptom survey, laboratory confirmed cases, and routine syndromic surveillance data from family doctors. We observed stark differences in the age distribution and seasonality of gastroenteritis measured by the datasets. However the signal of a more major epidemic was captured by multiple sources.
This contributes towards an improved understanding of gastroenteritis burden, which can influence policy decisions regarding the management of this illness.This is in collaboration with the Real-time Syndromic Surveillance Team at Public Health England, and the Centre for Mathematical Modelling of Infectious Diseases at the London School of Hygiene and Tropical Medicine.
Those attending this talk should expect to be introduced to the complexities of gastrointestinal disease surveillance with the big, imperfect datasets that are currently available. Note that despite the large worldwide impact of gastroenteritis on health and the economy it has received less research effort than would be expected.
#178 Mathematical modeling of the Neuro-Endocrine-Immune (NEI) network in Rheumatoid Arthritis (RA)
Authors: Nima Amin, Sebastian Binder and Michael Meyer-Hermann
There is extensive intercommunication between the nervous, the endocrine, and the immune (NEI) systems which interact via a complex network of shared signaling molecules and nerve signaling thereby regulating their biological functions. This complexity is further increased by the circadian dynamics of these signaling molecules. Despite clinical investigations, it is not yet completely clear how the circadian dynamics influence symptoms and treatment of NEI-related diseases, such as RA.
In our study, we employ the descriptive and predictive power of mathematical models to tackle this question. Using ordinary differential equations we describe the kinetics and nonlinear interactions between Tumor Necrosis Factor (TNF), Noradrenaline and Cortisol, which play a key role in development and maintenance of RA. In this study we address three main topics: (I) The pathophysiology of this complex system to understand the behavior of the system in healthy and RA conditions (ii) Optimization of current treatments of patients (iii) Determination of essential factors inducing the transition from healthy to RA phase.
Notably our model fitted to the clinical data succeeds in capturing the existing circadian dynamics of the NEI system. The model reproduces the clinical data in the RA individuals with respect to desensitization of the Hypothalamus-Pituitary-Adrenal axis to serum TNF. This assumption is supported by experimental observations that the ratio of TNF secretion to Cortisol secretion in RA patient is inadequate compared to healthy individuals. The model is further used to optimize the scheduling and dosing of the glucocorticoid therapy. According to the RA model and supported by recent studies, taking the drug at midnight leads to the strongest inhibition of TNF. This study provides an interdisciplinary framework for studying the complexity of the NEI system, for a deeper understanding of how it is functioning and for determination of causative agents of NEI dysfunction.
#184 Controlling complex policy problems: a multimethodological approach using system dynamics and network controllability
Authors: Lukas Schoenenberger, Radu Tanase and Andrea Schenker-Wicki
System dynamics (SD), an approach to modeling and simulating complex systems, has repeatedly demonstrated its value in contributing to the understanding and solution of complex policy problems. Typical areas of system dynamics application include modeling of policy problems related to public health, energy and the environment, social welfare, sustainable development, and security. One of the main challenges in system dynamics is that, due to a high degree of interdependent model variables and nonlinear relationships, the detection of model levers, i.e. variables capable of effectively and efficiently controlling complex policy problems, is exceedingly demanding. So, notwithstanding the usefulness of system dynamics in the analysis of these problems, the solution identification process is far from straightforward and in most cases trial-and-error driven. To address this challenge, we propose to combine system dynamics with network controllability to facilitate the detection of model levers. In essence, a system dynamics model can be thought of a web of interrelated causal factors that are assumed giving rise to the complex policy problem under study. Due to its web similarity, the structure of a system dynamics model can be accurately described as a directed weighted network, making it accessible to algorithmic exploration using concepts from the fields of graph theory and network science. Referring to recent research on control principles of complex networks, model levers are found first by calculating the size of the minimum driver set of the system dynamics model (network), second by computing all existing minimum driver sets, and third by ranking minimum driver set variables according to their control centrality. Variables with a high control centrality should be of primary interest to policy-makers when designing new solutions to complex policy problems. We demonstrate the proposed multimethodological approach on the basis of the World Dynamics model, a classic example from the system dynamics literature.
#227 Information Processing and Storage at Network Criticality
Authors: Jake Hanson and Sara Walker
The explicit connection between phase transitions and information theory has been examined only in limited detail. Here we use Ising Models and apply two information measures, Transfer Entropy and Active Information, to a broad range of network topologies including scale-free, small world, lattice, and random. Our results show that the total amount of information processed by the network, as measured by the Transfer Entropy, peaks near the critical temperature of the network. Similarly, the total information stored in the network, as measured by Active Information, undergoes a drastic transition at the critical temperature of the network, but the exact interpretation of this is sensitive to the update rule for the evolution of the model.
These results imply that networks optimized for information processing are poised for phase transitions. In the biological case, major leaps in evolutionary complexity are hypothesized to be the result of changes in the informational architecture of biological networks. However, to make this connection, the informational architecture of models more biologically relevant than the Ising Model must be studied. Therefore, we study an additional model known as the Sandpile Model to generalize the idea of phase transitions outside the Ising Model notion of “temperature”. We find that, like the Ising Model, the information processed by the Sandpile Model peaks when the system is poised at criticality.
Authors: Jonathan F. Donges, Carl-Friedrich Schleussner, Denis A. Engemann and Anders Levermann
Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that observed features of societal marginalization and clustering during transitions emerge naturally in a co-evolutionary adaptive network model that resolves the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how declining societal support for smoking affects individual behaviour and the network structure. Our results corroborate empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalised clusters. Herein self-amplifying feedbacks between individual behaviour and dynamic network restructuring play a pivotal role in the transition. We suggest a generative mechanism for the co-evolution of individual behaviour and social network structure that may apply to a wide range of examples beyond smoking such as sustainability transitions in lifestyle choices. Preprint: arxiv.org:1512.05013 [physics.soc-ph] (2015).
#175 Biases in healthcare data of gastroenteritis
Authors: Elizabeth Buckingham-Jeffery and Thomas House
Gastroenteritis is one of the most common illnesses worldwide. It is characterised by the symptoms of diarrhoea and vomiting. Although most cases of gastroenteritis in high income countries are self-limiting, there is a significant impact on healthcare services and the economy. Determining the prevalence of gastroenteritis is challenging. We have been exploring the possibility of establishing a comprehensive near real-time picture of the levels of activity of gastroenteritis in the UK by investigating the biases in presentation data from healthcare services.
Our modelling assumption is that there is an unobserved complex, noisy, non-linear process representing the true prevalence of different gastroenteritis-causing pathogens. No single data source perfectly correlates with this true prevalence, due to sampling at different rates from different sections of the population. These sampling biases must be understood in order to obtain a clearer picture of the burden of gastroenteritis and to make accurate predictions of future prevalence.
We compared data from an online cohort symptom survey, laboratory confirmed cases, and routine syndromic surveillance data from family doctors. We observed stark differences in the age distribution and seasonality of gastroenteritis measured by the datasets. However the signal of a more major epidemic was captured by multiple sources.
This contributes towards an improved understanding of gastroenteritis burden, which can influence policy decisions regarding the management of this illness.This is in collaboration with the Real-time Syndromic Surveillance Team at Public Health England, and the Centre for Mathematical Modelling of Infectious Diseases at the London School of Hygiene and Tropical Medicine.
Those attending this talk should expect to be introduced to the complexities of gastrointestinal disease surveillance with the big, imperfect datasets that are currently available. Note that despite the large worldwide impact of gastroenteritis on health and the economy it has received less research effort than would be expected.
#178 Mathematical modeling of the Neuro-Endocrine-Immune (NEI) network in Rheumatoid Arthritis (RA)
Authors: Nima Amin, Sebastian Binder and Michael Meyer-Hermann
There is extensive intercommunication between the nervous, the endocrine, and the immune (NEI) systems which interact via a complex network of shared signaling molecules and nerve signaling thereby regulating their biological functions. This complexity is further increased by the circadian dynamics of these signaling molecules. Despite clinical investigations, it is not yet completely clear how the circadian dynamics influence symptoms and treatment of NEI-related diseases, such as RA.
In our study, we employ the descriptive and predictive power of mathematical models to tackle this question. Using ordinary differential equations we describe the kinetics and nonlinear interactions between Tumor Necrosis Factor (TNF), Noradrenaline and Cortisol, which play a key role in development and maintenance of RA. In this study we address three main topics: (I) The pathophysiology of this complex system to understand the behavior of the system in healthy and RA conditions (ii) Optimization of current treatments of patients (iii) Determination of essential factors inducing the transition from healthy to RA phase.
Notably our model fitted to the clinical data succeeds in capturing the existing circadian dynamics of the NEI system. The model reproduces the clinical data in the RA individuals with respect to desensitization of the Hypothalamus-Pituitary-Adrenal axis to serum TNF. This assumption is supported by experimental observations that the ratio of TNF secretion to Cortisol secretion in RA patient is inadequate compared to healthy individuals. The model is further used to optimize the scheduling and dosing of the glucocorticoid therapy. According to the RA model and supported by recent studies, taking the drug at midnight leads to the strongest inhibition of TNF. This study provides an interdisciplinary framework for studying the complexity of the NEI system, for a deeper understanding of how it is functioning and for determination of causative agents of NEI dysfunction.
#184 Controlling complex policy problems: a multimethodological approach using system dynamics and network controllability
Authors: Lukas Schoenenberger, Radu Tanase and Andrea Schenker-Wicki
System dynamics (SD), an approach to modeling and simulating complex systems, has repeatedly demonstrated its value in contributing to the understanding and solution of complex policy problems. Typical areas of system dynamics application include modeling of policy problems related to public health, energy and the environment, social welfare, sustainable development, and security. One of the main challenges in system dynamics is that, due to a high degree of interdependent model variables and nonlinear relationships, the detection of model levers, i.e. variables capable of effectively and efficiently controlling complex policy problems, is exceedingly demanding. So, notwithstanding the usefulness of system dynamics in the analysis of these problems, the solution identification process is far from straightforward and in most cases trial-and-error driven. To address this challenge, we propose to combine system dynamics with network controllability to facilitate the detection of model levers. In essence, a system dynamics model can be thought of a web of interrelated causal factors that are assumed giving rise to the complex policy problem under study. Due to its web similarity, the structure of a system dynamics model can be accurately described as a directed weighted network, making it accessible to algorithmic exploration using concepts from the fields of graph theory and network science. Referring to recent research on control principles of complex networks, model levers are found first by calculating the size of the minimum driver set of the system dynamics model (network), second by computing all existing minimum driver sets, and third by ranking minimum driver set variables according to their control centrality. Variables with a high control centrality should be of primary interest to policy-makers when designing new solutions to complex policy problems. We demonstrate the proposed multimethodological approach on the basis of the World Dynamics model, a classic example from the system dynamics literature.
#227 Information Processing and Storage at Network Criticality
Authors: Jake Hanson and Sara Walker
The explicit connection between phase transitions and information theory has been examined only in limited detail. Here we use Ising Models and apply two information measures, Transfer Entropy and Active Information, to a broad range of network topologies including scale-free, small world, lattice, and random. Our results show that the total amount of information processed by the network, as measured by the Transfer Entropy, peaks near the critical temperature of the network. Similarly, the total information stored in the network, as measured by Active Information, undergoes a drastic transition at the critical temperature of the network, but the exact interpretation of this is sensitive to the update rule for the evolution of the model.
These results imply that networks optimized for information processing are poised for phase transitions. In the biological case, major leaps in evolutionary complexity are hypothesized to be the result of changes in the informational architecture of biological networks. However, to make this connection, the informational architecture of models more biologically relevant than the Ising Model must be studied. Therefore, we study an additional model known as the Sandpile Model to generalize the idea of phase transitions outside the Ising Model notion of “temperature”. We find that, like the Ising Model, the information processed by the Sandpile Model peaks when the system is poised at criticality.
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