scholarly journals Principled simulation of agent-based models in epidemiology

2020 ◽  
Author(s):  
Sean L. Wu ◽  
Andrew J. Dolgert ◽  
Joseph A. Lewnard ◽  
John M. Marshall ◽  
David L. Smith
Keyword(s):  
Stochastic Simulation ◽  
Disease Transmission ◽  
Dynamic Models ◽  
Discrete Event ◽  
Model Complexity ◽  
Agent Based Models ◽  
Time Step ◽  
Hazard Functions ◽  
Population Mixing ◽  
Agent Based

AbstractAfter more than a century of sustained work by mathematicians, biologists, epidemiologists, probabilists, and other experts, dynamic models have become a vital tool for understanding and describing epidemics and disease transmission systems. Such models fulfill a variety of crucial roles including data integration, estimation of disease burden, forecasting trends, counterfactual evaluation, and parameter estimation. These models often incorporate myriad details, from age and social structure to inform population mixing patterns, commuting and migration, and immunological dynamics, among others. This complexity can be daunting, so many researchers have turned to stochastic simulation using agent-based models. Developing agent-based models, however, can present formidable technical challenges. In particular, depending on how the model updates state, unwanted or even unnoticed approximations can be introduced into a simulation model. In this article, we present computational methods for approximating continuous time discrete event stochastic processes based on a discrete time step to speed up complicated simulations which also converges to the true process as the time step goes to zero. Our stochastic models is constructed via hazard functions, and only those hazards which are dependent on the state of other agents (such as infection) are approximated, whereas hazards governing dynamics internal to an agent (such as immune response) are simulated exactly. By partitioning hazards as being either dependent or internal, a generic algorithm can be presented which is applicable to many models of contagion processes, with natural areas of extension and optimization.Author summaryStochastic simulation of epidemics is crucial to a variety of tasks in public health, encompassing intervention evaluation, trend forecasting, and estimation of epidemic parameters, among others. In many situations, due to model complexity, time constraints, unavailability or unfamiliarity with existing software, or other reasons, agent-based models are used to simulate epidemic processes. However, many simulation algorithms are ad hoc, which may introduce unwanted or unnoticed approximations. We present a method to build approximate, agent-based models from mathematical descriptions of stochastic epidemic processes which will improve simulation speed and converge to exact simulation techniques in limiting cases. The simplicity and generality of our method should be widely applicable to various problems in mathematical epidemiology and its connection to other methods developed in chemical physics should inspire future work and elaboration.

BIASED RANDOM WALKS, PARTIAL DIFFERENTIAL EQUATIONS AND UPDATE SCHEMES

2013 ◽  
Vol 55 (2) ◽  
pp. 93-108 ◽  
Author(s):  
JACK D. HYWOOD ◽  
KERRY A. LANDMAN
Keyword(s):  
Statistical Physics ◽  
Planck Equation ◽  
Step Length ◽  
Physical Object ◽  
Cell Diameter ◽  
Agent Based Models ◽  
Time Step ◽  
Partial Differential ◽  
Agent Based ◽  
Random Walkers

AbstractThere is much interest within the mathematical biology and statistical physics community in converting stochastic agent-based models for random walkers into a partial differential equation description for the average agent density. Here a collection of noninteracting biased random walkers on a one-dimensional lattice is considered. The usual master equation approach requires that two continuum limits, involving three parameters, namely step length, time step and the random walk bias, approach zero in a specific way. We are interested in the case where the two limits are not consistent. New results are obtained using a Fokker–Planck equation and the results are highly dependent on the simulation update schemes. The theoretical results are confirmed with examples. These findings provide insight into the importance of updating schemes to an accurate macroscopic description of stochastic local movement rules in agent-based models when the lattice spacing represents a physical object such as cell diameter.

scholarly journals Modelling COVID - 19 Transmission in a Hemodialysis Centre Using Simulation Generated Contacts Matrices

2021 ◽  
Author(s):  
Mohammadali Tofighi ◽  
Ali Asgary ◽  
Asad A. Merchant ◽  
Mohammad Ali Shafiee ◽  
Mahdi M. Najafabadi ◽  
...  
Keyword(s):  
Discrete Event ◽  
Intermittent Hemodialysis ◽  
Physical Interaction ◽  
Generation Process ◽  
Care Providers ◽  
Time Step ◽  
Dialysis Unit ◽  
Agent Based ◽  
Micro Scale ◽  
Contact Matrix

AbstractThe COVID-19 pandemic has been particularly threatening to the patients with end-stage kidney disease (ESKD) on intermittent hemodialysis and their care providers. Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. The results show that micro-simulation can be used to estimate contact matrices, which can be used effectively for disease modelling in dialysis and similar settings.

Dynamic Data Mining Based on the Stability of Dynamic Models

2021 ◽  
pp. 99-117
Author(s):  
Hocine Chebi
Keyword(s):  
Data Mining ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Fixed Points ◽  
Dynamic Models ◽  
Agent Based Models ◽  
Dynamic Data ◽  
Agent Based ◽  
The Monte Carlo Method ◽  
Evacuation System

This work presents a new approach based on the use of stable dynamic models for dynamic data mining. Data mining is an essential technique in the process of extracting knowledge from data. This allows us to model the extracted knowledge using a formalism or a modeling technique. However, the data needed for knowledge extraction is collected in advance, and it can take a long time to collect. The objective is therefore to move towards a solution based on the modeling of systems using dynamic models and to study their stability. Stable dynamic models provide us with a basis for dynamic data mining. In order to achieve this objective, the authors propose an approach based on agent-based models, the concept of fixed points, and the Monte-Carlo method. Agent-based models can represent dynamic models that mirror or simulate a dynamic system, where such a model can be viewed as a source of data (data generators). In this work, the concept of fixed points was used in order to represent the stable states of the agent-based model. Finally, the Monte-Carlo method, which is a probabilistic method, was used to estimate certain values, using a very large number of experiments or runs. As a case study, the authors chose the evacuation system of a supermarket (or building) in case of danger, such as a fire. This complex system mainly comprises the various constituent elements of the building, such as rows of shelves, entry and exit doors, fire extinguishers, etc. In addition, these buildings are often filled with people of different categories (age, health, etc.). The use of the Monte-Carlo method allowed the authors to experiment with several scenarios, which allowed them to have more data to study this system and extract some knowledge. This knowledge allows us to predict the future situation regarding the building's evacuation system and anticipate improvements to its structure in order to make these buildings safer and prevent the greatest number of victims.

scholarly journals Emulating Target Trials to Improve Causal Inference from Agent-Based Models

2021 ◽  
Author(s):  
Eleanor J Murray ◽  
Brandon D L Marshall ◽  
Ashley L Buchanan
Keyword(s):  
Causal Inference ◽  
Disease Transmission ◽  
Causal Effect ◽  
Potential Outcomes ◽  
Emergent Properties ◽  
Target Trial ◽  
Agent Based Models ◽  
Agent Based ◽  
Effect Estimation ◽  
Potential Outcomes Framework

Abstract Agent-based models are a key tool for investigating the emergent properties of population health settings, such as infectious disease transmission, where the exposure often violates the key ‘no interference’ assumption of traditional causal inference under the potential outcomes framework. Agent-based models and other simulation-based modeling approaches have generally been viewed as a separate knowledge-generating paradigm from the potential outcomes framework, but this can lead to confusion about how to interpret the results of these models in real-world settings. By explicitly incorporating the target trial framework into the development of an agent-based or other simulation model, we can clarify the causal parameters of interest, as well as make explicit the assumptions required for valid causal effect estimation within or between populations. In this paper, we describe the use of the target trial framework for designing agent-based models when the goal is estimation of causal effects in the presence of interference, or spillover.

scholarly journals PROBLEMS AND PROSPECTS FOR APPLICATION OF AGENT-BASED MODELLING IN THE PSYCHOLOGY OF INFLUENCE

2021 ◽  
pp. 116-139
Author(s):  
В. В. Латынов
Keyword(s):  
Optimal Level ◽  
Psychological Research ◽  
Agent Based Modeling ◽  
Model Complexity ◽  
Agent Based Model ◽  
Agent Based Models ◽  
Social Phenomena ◽  
Agent Based ◽  
Complex Models ◽  
The Impact

В статье обсуждаются вопросы применения агент-ориентированного моделирования в психологических исследованиях. Данный вид моделирования используется для изучения систем, состоящих из большого количества взаимодействующих друг с другом агентов. Рассматривается текущее состояние и перспективы использования агентных моделей. Выделяются основные направления применения агент-ориентированного моделирования в психологии: генерирование новых и совершенствование уже существующих теорий; проверка исследовательских гипотез; построение сложных моделей социальных явлений и процессов, включающих психологические закономерности разного типа. Формулируются задачи, требующие решения при создании агентной модели: задание оптимального уровня сложности модели; достижение ее психологического реализма; выбор качеств, которыми будут обладать агенты; определение правил их взаимодействия с другими агентами и средой взаимодействия. Обсуждается проблема калибрования агентной модели, т. е. основанного на данных экспериментальных исследований обоснования необходимости введения конкретных качеств и правил взаимодействия агентов. Рассматриваются возможности агент-ориентированного моделирования при изучении процессов психологического воздействия. Выделяются теории и эмпирические закономерности, требующие учета при создании агентных моделей в области психологии воздействия. Эти теории и закономерности относятся главным образом к двум областям психологического исследования, ориентированным, соответственно, на анализ закономерностей восприятия, изменения и выражения мнений и аттитюдов на уровне отдельного индивида («двухпроцессный» подход, модель знаний о воздействии М. Фристэда и П. Райта); изучение закономерностей, связанных с влиянием на мнения, аттитюды и поведение человека его членства в группе и позиции его окружения (теория «лидеров мнения», теории групповой идентичности). The article discusses the application of agent-based modeling in psychological research. This type of modeling is used to study systems consisting of a large number of agents interacting with each other. The current state and prospects of using agent-based models are considered. The main directions of application of agent-based modeling in psychology are highlighted: generating new and improving existing theories; testing research hypotheses; construction of complex models of social phenomena and processes, including psychological patterns of various types. The tasks that need to be solved when creating an agent-based model are formulated: setting the optimal level of model complexity; achieving her psychological realism; choice of qualities that agents will possess; defining the rules for their interaction with other agents and the interaction environment. The problem of calibrating the agent-based model is discussed, that is, substantiating the need to introduce specific qualities and rules for the interaction of agents based on experimental research data. The possibilities of agent-based modeling in the study of the processes of psychological influence are considered. Theories and empirical patterns are highlighted that require consideration when creating agent-based models in the field psychology of influence. These theories and patterns relate mainly to two areas of psychological research, focused, respectively, on the analysis of patterns of perception, change and expression of opinions and attitudes at the level of an individual ("two-process" approach, the model of knowledge about the impact of M. Freestad and P. Wright); study of the patterns associated with the influence on the opinions, attitudes and behavior of a person by his membership in a group and the position of his environment (theory of "opinion leaders", theories of group identity).

scholarly journals Modelling COVID-19 transmission in a hemodialysis centre using simulation generated contacts matrices

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259970
Author(s):  
Mohammadali Tofighi ◽  
Ali Asgary ◽  
Asad A. Merchant ◽  
Mohammad Ali Shafiee ◽  
Mahdi M. Najafabadi ◽  
...  
Keyword(s):  
Discrete Event ◽  
Intermittent Hemodialysis ◽  
Physical Interaction ◽  
Generation Process ◽  
Care Providers ◽  
Time Step ◽  
Dialysis Unit ◽  
Agent Based ◽  
Micro Scale ◽  
Contact Matrix

The COVID-19 pandemic has been particularly threatening to patients with end-stage kidney disease (ESKD) on intermittent hemodialysis and their care providers. Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. The results show that micro-simulation can be used to estimate contact matrices, which can be used effectively for disease modelling in dialysis and similar settings.

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