A Survey of the Individual-Based Model applied in Biomedical and Epidemiology

Individual-based model (IBM) has been used to simulate and to design control strategies for dynamic systems that are subject to stochasticity and heterogeneity, such as infectious diseases. In the IBM, an individual is represented by a set of specific characteristics that may change dynamically over time. This feature allows a more realistic analysis of the spread of an epidemic. This paper presents a literature survey of IBM applied to biomedical and epidemiology research. The main goal is to present existing techniques, advantages and future perspectives in the development of the model. We evaluated 89 articles, which mostly analyze interventions aimed at endemic infections. In addition to the review, an overview of IBM is presented as an alternative to complement or replace compartmental models, such as the SIR (Susceptible-Infected-Recovered) model. Numerical simulations also illustrate the capabilities of IBM, as well as some limitations regarding the effects of discretization. We show that similar side-effects of discretization scheme for compartmental models may also occur in IBM, which requires careful attention

A scalable discrete event stochastic agent-based model of infectious disease propagation

The article of record as published may be found at http://dx.doi.org/10.1109/WSC.2015.7408160Proceedings of the 2015 Winter Simulation Conference, L. Yilmaz, W. K V. Chan, I. Moon, T. M. K. Roeder, C. Macal, and M. D. Rossetti, eds.We propose a newstochastic model of infectious disease propagation. This model tracks individual outcomes, but does so without needing to create connectivity graphs for all members of the population. This makes the model scalable to much larger populations than traditional agent-based models have been able to cope with, while preserving the impact of variability during the critical early stages of an outbreak. This contrasts favorably with aggregate deterministic models, which ignore variability, and negates the requirement to assume “convenient” but potentially unrealistic distribution choices which aggregate stochastic models need in order to be analytically tractable. Initial explorations with our new model show behaviors similar to the observed course of Ebola outbreaks over the past 30+ years-while many outbreaks will fizzle out relatively quickly, some appear to reach a critical mass threshold and can turn into widespread epidemics

Input Uncertainty and Data Collection Problems in Stochastic Simulation

Stochastic simulation is an important tool within the field of operational research. It allows for the behaviour of random real-world systems to be analysed, evaluated, and optimised. It is critical to understand the uncertainty and error in outcomes from simulation experiments, to ensure that decisions are made with appropriate levels of confidence. Frequently, input models that actuate stochastic simulations are estimated using samples of real-world data. This introduces a source of uncertainty into the simulation model which propagates through to output measures, causing an error known as input uncertainty. Input uncertainty depends on the samples of data that are collected and used to estimate the input models for the simulation. In this thesis, we consider problems relating to input uncertainty and data collection in the field of stochastic simulation. Firstly, we propose an algorithm that guides the data collection procedure for a simulation experiment in a manner that minimises input uncertainty. Reducing the uncertainty around the simulation response allows for improved insights to be inferred from simulation results. Secondly, we outline an approach for comparing data collection strategies in terms of the input uncertainty passed to outputs in simulations of viral loads. This represents a different type of data collection problem to the ones usually studied in simulation experiments. Thirdly, we adapt two techniques for quantifying input uncertainty to consider a quantile of the simulation outputs, rather than the mean. Quantiles are regularly used to provide alternative information regarding the simulation outputs relative to the mean, therefore it is equally important to understand the uncertainty of such measures. Finally, we begin to investigate how input uncertainty impacts predictive models fit to simulation data. This relates to the field of simulation analytics, a novel and emergent area of research where the problem of input uncertainty has not previously been examined

A framework to support the decision-making process for modelling of communicable diseases

Thesis (MEng)--Stellenbosch University, 2019.ENGLISH ABSTRACT: Infectious disease outbreaks have the potential to disrupt and strain the global health care system, even more so when a localised disease outbreak propagates rapidly to a large area. Such a disease outbreak is referred to as a pandemic disease outbreak. Pandemic outbreaks often inspire global collaboration between researchers and modelling practitioners with a view to devise strategies, disease propagation models and actions on how to address the outbreak. Modelling of infectious disease is a complex endeavour. The literature on the available modelling approaches and general application to disease modelling is well documented in the literature. What is, however, less evident, especially to a modelling practitioner with less rigorous modelling experience, is the selection and consideration of modelling considerations based on the specific context of the disease outbreak. To address this challenge, a modelling support framework is designed in this research project, with a view to formalise the most salient universal modelling steps and assist novice modelling practitioners in the consideration and selection of appropriate approaches for modelling infectious diseases. The research consists of three phases, namely the design and execution of a structured literature review, analysis of the findings of the literature review, and the construction of a modelling support and guidance framework. During the first phase of the research, the chain of infection is used as an overarching metaphor to guide the process in identifying relevant considerations, disease characteristics and contextual factors which may potentially affect disease propagation, and this is used as the basis for determining the scope of the structured literature review. The review is designed to construct a sufficiently detailed dataset which is well representative of the various modelling approaches as applied in literature. The 283 identified literature pieces are methodically analysed and the relevant modelling considerations, disease characteristics and contextual factors from each of the pieces are captured to the dataset. During the second phase of the research the dataset is analysed. The modelling considerations are analysed in relation to the disease transmission mode, and the relationship between modelling considerations are also analysed. In general, the selection of modelling approaches and considerations were not reducible to a single factor. This suggests that numerous factors must be considered in the model decision making process, and additionally, it highlights the importance of contextualising the disease outbreak. The third phase of the research consists of the framework construction. Both the first and the second phases of the research are used to inform and guide the framework construction. The framework is constructed with two goals in mind, namely to inform modelling considerations from a holistic viewpoint and to aid in the selection of the relevant modelling considerations. The framework use is verified with an illustrative case study and validated with semi-structured interviews that are conducted with external subject matter experts with a background in engineering and health care modelling.AFRIKAANSE OPSOMMING: Die uitbreek van ’n aansteeklike siekte het die potensiaal om die globale gesondheidsorgsisteem te ontwrig en onder geweldige druk te plaas, des te meer wanneer so ’n gelokaliseerde uitbreking spoedig na ’n groter area versprei. Sulke siekte-uitbrekings staan bekend as pandemiese siektes. Die ontstaan van pandemiese uitbrekings van siektes lei tipies tot wêreldwye samewerking tussen navorsers en modelleerders. Die doel van samewerking hou verband met die skep van strategieë, modelle wat siekte-oordrag modelleer en aksieplanne om die uitbreking te bestuur. Die modellering van aansteeklike siektes is ’n komplekse onderneming. Beskikbare modellerings-benaderings en die generiese gebruik daarvan om siektes te modelleer is goed opgeteken in die literatuur. Wat minder ooglopend is van hierdie benaderings, veral vir die modelleerder met elementêre modelleringskennis, is die oorweging en selektering van modelleringelemente gebaseer op die spesifieke kontekstuele omstandighede van die siekte-uitbreking. Om hierdie uitdaging aan te pak word daar in hierdie navorsingsprojek ’n ondersteuningsraamwerk vir modellering geskep. Die doel hiervan is die formalisering van die belangrikste modellerings-stappe en om onervare modelleerders te ondersteun in die oorweging en selektering van toepaslike benaderings om aansteeklike siektes te modelleer. Die navorsing bestaan uit drie fases, naamlik die ontwerp en uitvoering van ’n gestruktureerde literatuuroorsig, ’n analise van die bevindinge van die literatuuroorsig, en die opstel van ’n raamwerk wat ondersteuning en raadgewing ten opsigte van modellering bied. As deel van die eerste fase van die navorsing, word die ketting van infeksie as ’n oorhoofse metafoor gebruik. Hierdie metafoor word gebruik om relevante oorwegings, siekte-eienskappe en kontekstuele faktore te identifiseer wat die potensiaal het om die verspreiding van siektes te beïnvloed. Dit word ook as die basis gebruik om die bestek van die gestruktureerde literatuuroorsig te bepaal. Die gestruktureerde literatuuroorsig is ontwerp om ’n gedetailleerde datastel op te stel wat ’n goeie verteenwoordiging is van die verskeie modelleringsbenaderings soos dit in die literatuur toegepas is. Die geïdentifiseerde 283 literatuurstukke is stapsgewys geanaliseer en die relevante modelleringsbenaderings, siekte-eienskappe en kontekstuele faktore van die literatuurstukke is in die datastel opgeneem. As deel van die tweede fase van die navorsing word die datastel geanaliseer. Die modelleringsoorwegings is geanaliseer met betrekking tot die siekte-oordragsmetode en die verhoudings tussen ander modelleringsoorwegings. Oor die algemeen is daar bevind dat die keuse van ’n modelleringsbenadering of -oorweging nie reduseerbaar is tot die oorweging van ’n enkele faktor nie. Die afleiding is dus dat verskeie faktore in ag geneem moet word in die seleksieproses van ’n modelleringsbenadering, en dat die belangrikheid van die kontekstualisering van ’n siekte-uitbreking benadruk moet word. As deel van die derde fase van die navorsing is die raamwerk opgestel. Beide die eerste en tweede fases van die navorsing is gebruik om die opstelproses van die raamwerk te lei en die opstelkeuses in te lig. Die raamwerk is opgestel met twee verwagte uitkomstes, naamlik om die modellerings-oorwegings vanuit ’n holistiese oogpunt in te lig, sowel as om die selektering van relevante modelleringsoorwegings te ondersteun. Die gebruik van die raamwerk is geverifieer met behulp van ’n verduidelikende gevallestudie. Die validasie is voltooi met behulp van semi-gestruktureerde onderhoude met eksterne vakgebied-kenners met ’n agtergrond in die ingenieurswese en gesondheidssorg-modelleringsvelde