scholarly journals Agent-based evolving network modeling: a new simulation method for modeling low prevalence infectious diseases

2021 ◽  
Author(s):  
Matthew Eden ◽  
Rebecca Castonguay ◽  
Buyannemekh Munkhbat ◽  
Hari Balasubramanian ◽  
Chaitra Gopalappa
Keyword(s):  
Infectious Diseases ◽  
Network Modeling ◽  
Compartmental Modeling ◽  
Agent Based ◽  
Network Generation ◽  
Scale Free ◽  
Individual Level ◽  
Scale Free Networks ◽  
The Individual ◽  
Low Prevalence

AbstractAgent-based network modeling (ABNM) simulates each person at the individual-level as agents of the simulation, and uses network generation algorithms to generate the network of contacts between individuals. ABNM are suitable for simulating individual-level dynamics of infectious diseases, especially for diseases such as HIV that spread through close contacts within intricate contact networks. However, as ABNM simulates a scaled-version of the full population, consisting of all infected and susceptible persons, they are computationally infeasible for studying certain questions in low prevalence diseases such as HIV. We present a new simulation technique, agent-based evolving network modeling (ABENM), which includes a new network generation algorithm, Evolving Contact Network Algorithm (ECNA), for generating scale-free networks. ABENM simulates only infected persons and their immediate contacts at the individual-level as agents of the simulation, and uses the ECNA for generating the contact structures between these individuals. All other susceptible persons are modeled using a compartmental modeling structure. Thus, ABENM has a hybrid agent-based and compartmental modeling structure. The ECNA uses concepts from graph theory for generating scale-free networks. Multiple social networks, including sexual partnership networks and needle sharing networks among injecting drug-users, are known to follow a scale-free network structure. Numerical results comparing ABENM with ABNM estimations for disease trajectories of hypothetical diseases transmitted on scale-free contact networks are promising for application to low prevalence diseases.

scholarly journals A HYBRID MODEL FOR STUDYING SPATIAL ASPECTS OF INFECTIOUS DISEASES

2012 ◽  
Vol 54 (1-2) ◽  
pp. 37-49 ◽  
Author(s):  
BENJAMIN J. BINDER ◽  
JOSHUA V. ROSS ◽  
MATTHEW J. SIMPSON
Keyword(s):  
Infectious Disease ◽  
Spatial Analysis ◽  
Infectious Diseases ◽  
Hybrid Model ◽  
Population Level ◽  
Infectious Agents ◽  
Agent Based ◽  
Individual Level ◽  
The Individual ◽  
Spread Of Infection

AbstractWe consider a hybrid model, created by coupling a continuum and an agent-based model of infectious disease. The framework of the hybrid model provides a mechanism to study the spread of infection at both the individual and population levels. This approach captures the stochastic spatial heterogeneity at the individual level, which is directly related to deterministic population level properties. This facilitates the study of spatial aspects of the epidemic process. A spatial analysis, involving counting the number of infectious agents in equally sized bins, reveals when the spatial domain is nonhomogeneous.

Simulating an Incentive Framework for Scientific Production by Means of Adaptive Agents

2014 ◽  
pp. 221-238
Author(s):  
Gabriel Franklin ◽  
Tibérius O. Bonates
Keyword(s):  
Incentive Mechanism ◽  
Scientific Production ◽  
Agent Based Simulation ◽  
Adaptive Agents ◽  
Scientific Publications ◽  
Agent Based ◽  
Individual Level ◽  
Original Target ◽  
Negative Effect ◽  
The Individual

This chapter describes an agent-based simulation of an incentive mechanism for scientific production. In the proposed framework, a central agency is responsible for devising and enforcing a policy consisting of performance-based incentives in an attempt to induce a global positive behavior of a group of researchers, in terms of number and type of scientific publications. The macro-level incentive mechanism triggers micro-level actions that, once intensified by social interactions, lead to certain patterns of behavior from individual agents (researchers). Positive reinforcement from receiving incentives (as well as negative reinforcement from not receiving them) shape the behavior of agents in the course of the simulation. The authors show, by means of computational experiments, that a policy devised to act at the individual level might induce a single global behavior that can, depending on the values of certain parameters, be distinct from the original target and have an overall negative effect. The agent-based simulation provides an objective way of assessing the quantitative effect that different policies might induce on the behavior of individual researchers when it comes to their preferences regarding scientific publications.

Macroscopic Modeling of Information Flow in an Agent-Based Electronic Health Record System

2011 ◽  
pp. 305-334
Author(s):  
Ben Tse
Keyword(s):  
Electronic Health Record ◽  
Electronic Health Record System ◽  
Health Record ◽  
Record System ◽  
Macroscopic Modeling ◽  
Agent Based ◽  
Individual Level ◽  
Patient Health ◽  
Electronic Health ◽  
The Individual

This chapter presents an architecture, or general framework, for an agent-based electronic health record system (ABEHRS) to provide health information access and retrieval among different medical services facilities. The agent system’s behaviors are analyzed using the simulation approach and the mathematical modeling approach. The key concept promoted by ABEHRS is to allow patient health records to autonomously move through the computer network uniting scattered and distributed data into one consistent and complete data set or patient health record. ABEHRS is an example of multi-agent swarm system, which is composed of many simple agents and a system that is able to self-organize. The ultimate goal is that the reader should appreciate the benefits of using mobile agents and the importance of studying agent behaviors at the system level and at the individual level.

ANALYZING THE AGENT-BASED MODEL AND ITS IMPLICATIONS

2003 ◽  
Vol 06 (03) ◽  
pp. 331-347 ◽  
Author(s):  
YUTAKA I. LEON SUEMATSU ◽  
KEIKI TAKADAMA ◽  
NORBERTO E. NAWA ◽  
KATSUNORI SHIMOHARA ◽  
OSAMU KATAI
Keyword(s):  
Social Sciences ◽  
Internal Structure ◽  
Design Methodology ◽  
Social Systems ◽  
Agent Based Model ◽  
Social Phenomena ◽  
Agent Based ◽  
Individual Level ◽  
The Social ◽  
The Individual

Agent-based models (ABMs) have been attracting the attention of researchers in the social sciences, becoming a prominent paradigm in the study of complex social systems. Although a great number of models have been proposed for studying a variety of social phenomena, no general agent design methodology is available. Moreover, it is difficult to validate the accuracy of these models. For this reason, we believe that some guidelines for ABMs design must be devised; therefore, this paper is a first attempt to analyze the levels of ABMs, identify and classify several aspects that should be considered when designing ABMs. Through our analysis, the following implications have been found: (1) there are two levels in designing ABMs: the individual level, related to the design of the agents' internal structure, and the collective level, which concerns the design of the agent society or macro-dynamics of the model; and (2) the mechanisms of these levels strongly affect the outcomes of the models.

A CONNECTIONIST ABM OF SOCIAL CATEGORIZATION PROCESSES

2012 ◽  
Vol 15 (06) ◽  
pp. 1250077 ◽  
Author(s):  
DIRK VAN ROOY
Keyword(s):  
Social Influence ◽  
Network Structure ◽  
Social Adaptation ◽  
Multiple Sources ◽  
Agent Based ◽  
Individual Level ◽  
Extensive Research Program ◽  
Micro Level ◽  
The Individual ◽  
Over Time

This paper introduces a connectionist Agent-Based Model (cABM) that incorporates detailed, micro-level understanding of social influence processes derived from laboratory studies and that aims to contextualize these processes in such a way that it becomes possible to model multidirectional, dynamic influences in extended social networks. At the micro-level, agent processes are simulated by recurrent auto-associative networks, an architecture that has a proven ability to simulate a variety of individual psychological and memory processes [D. Van Rooy, F. Van Overwalle, T. Vanhoomissen, C. Labiouse and R. French, Psychol. Rev. 110, 536 (2003)]. At the macro-level, these individual networks are combined into a "community of networks" so that they can exchange their individual information with each other by transmitting information on the same concepts from one net to another. This essentially creates a network structure that reflects a social system in which (a collection of) nodes represent individual agents and the links between agents the mutual social influences that connect them [B. Hazlehurst, and E. Hutchins, Lang. Cogn. Process. 13, 373 (1998)]. The network structure itself is dynamic and shaped by the interactions between the individual agents through simple processes of social adaptation. Through simulations, the cABM generates a number of novel predictions that broadly address three main issues: (1) the consequences of the interaction between multiple sources and targets of social influence (2) the dynamic development of social influence over time and (3) collective and individual opinion trajectories over time. Some of the predictions regarding individual level processes have been tested and confirmed in laboratory experiments. In a extensive research program, data is currently being collected from real groups that will allow validating the predictions of cABM regarding aggregate outcomes.

scholarly journals Coupling effects on turning points of infectious diseases epidemics in scale-free networks

2017 ◽  
Vol 18 (S7) ◽  
Author(s):  
Kiseong Kim ◽  
Sangyeon Lee ◽  
Doheon Lee ◽  
Kwang Hyung Lee
Keyword(s):  
Infectious Diseases ◽  
Turning Points ◽  
Coupling Effects ◽  
Scale Free ◽  
Scale Free Networks

Macroscopic Modeling of Information Flow in an Agent-Based Electronic Health Record System

2011 ◽  
pp. 602-630
Author(s):  
Ben Tse
Keyword(s):  
Electronic Health Record ◽  
Electronic Health Record System ◽  
Health Record ◽  
Record System ◽  
Macroscopic Modeling ◽  
Agent Based ◽  
Individual Level ◽  
Patient Health ◽  
Electronic Health ◽  
The Individual

This chapter presents an architecture, or general framework, for an agent-based electronic health record system (ABEHRS) to provide health information access and retrieval among different medical services facilities. The agent system’s behaviors are analyzed using the simulation approach and the mathematical modeling approach. The key concept promoted by ABEHRS is to allow patient health records to autonomously move through the computer network uniting scattered and distributed data into one consistent and complete data set or patient health record. ABEHRS is an example of multi-agent swarm system, which is composed of many simple agents and a system that is able to self-organize. The ultimate goal is that the reader should appreciate the benefits of using mobile agents and the importance of studying agent behaviors at the system level and at the individual level.

scholarly journals Effect of human behavior on the evolution of viral strains during an epidemic

2021 ◽  
Author(s):  
Asma Azizi ◽  
Natalia L. Komarova ◽  
Dominik Wodarz
Keyword(s):  
New Orleans ◽  
Human Behavior ◽  
Behavioral Changes ◽  
Disease Avoidance ◽  
Agent Based ◽  
Scale Free ◽  
Behavioral Traits ◽  
The Social ◽  
Scale Free Networks ◽  
Cross Immunity

AbstractIt is well known in the literature that human behavior can change as a reaction to disease observed in others, and that such behavioral changes can be an important factor in the spread of an epidemic. It has been noted that human behavioral traits in disease avoidance are under selection in the presence of infectious diseases. Here we explore a complimentary trend: the pathogen itself might experience a force of selection to become less “visible”, or less “symptomatic”, in the presence of such human behavioral trends. Using a stochastic SIR agent-based model, we investigated the co-evolution of two viral strains with cross-immunity, where the resident strain is symptomatic while the mutant strain is asymptomatic. We assumed that individuals exercised self-regulated social distancing (SD) behavior if one of their neighbors was infected with a symptomatic strain. We observed that the proportion of asymptomatic carriers increased over time with a stronger effect corresponding to higher levels of self-regulated SD. Adding mandated SD made the effect more significant, while the existence of a time-delay between the onset of infection and the change of behavior reduced the advantage of the asymptomatic strain. These results were consistent under random geometric networks, scale-free networks, and a synthetic network that represented the social behavior of the residents of New Orleans.

scholarly journals Learning from COVID-19: Infectious Disease Vulnerability Promotes Pro-Environmental Behaviors

2021 ◽  
Vol 18 (16) ◽  
pp. 8687
Author(s):  
Da Jiang ◽  
Mingxuan Li ◽  
Hanyang Wu ◽  
Shuang Liu
Keyword(s):  
Climate Change ◽  
Infectious Disease ◽  
Infectious Diseases ◽  
Environmental Concern ◽  
Environmental Behaviors ◽  
Individual Level ◽  
Societal Level ◽  
Using Data ◽  
The Individual ◽  
The Relationship

Environmental problems, such as climate change, pollution, and environmental degradation, are important contributors to the spread of infectious diseases, such as COVID-19 and SARS. For instance, a greater concentration of ambient NO2 was associated with faster transmission of the SARS-CoV-2 virus, which causes COVID-19. However, it remains unclear whether outbreaks of infectious diseases arouse individuals’ concern on the need to protect the environment and therefore promote more pro-environmental behaviors. To this end, we examined the relationship between infectious disease vulnerability and pro-environmental behaviors using data from a cross-societal survey (N = 53 societies) and an experiment (N = 214 individuals). At both the societal and the individual levels, infectious disease vulnerability increased pro-environmental behaviors. At the societal level, this relationship was mediated by citizens’ level of environmental concern. At the individual level, the relationship was mediated by empathy. The findings show that infectious disease vulnerability is conducive to pro-environmental behaviors.

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