Agent-Based Simulation Tools in Computational Epidemiology

Patlolla, Padmavathi; Gunupudi, Vandana; Mikler, Armin R.; Jacob, Roy T.

doi:10.1007/11553762_21

Cited by 29 publications

(21 citation statements)

References 2 publications

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“…The spread of infectious diseases has been simulated using two major classes of models: multi-agent models which aim at studying the phenomenon at the individual level [26,27] and meta-population models that operate at an aggregate level [2]. The plausibility and usefulness of individual-based models and simulations can be questioned since it is a quasi-impossible task to try to plausibly model and simulate the activities and mobility behaviours of individuals in an urban area.…”

Section: Resultsmentioning

confidence: 99%

Integrated epidemiologic simulation for person to person contagion through urban mobility within GIS

Haddad

Moulin

Thériault

et al. 2012

Proceedings of the First ACM SIGSPATIAL International Workshop on Use of GIS in Public Health

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In recent years, advances in Health Geography, Geographical Epidemiology and Public Health Informatics have led to an extensive use of Geographic Information Systems (GIS) to study a variety of public health issues. Considering infectious disease outbreaks, time becomes a critical factor and Public Health officers require tools to support rapid decision making. In this context, GIS technology presents some limits. Mainly, the study of communicable diseases requires the development of complicated spatial-temporal models which is often time and effort consuming. In addition, this type of dynamic analysis is hard to realize by means of the GIS functionalities commonly available. Addressing such limits, we present in this paper a new GIS-based spatial-temporal simulation approach and software to support public health decision making in the context of communicable diseases. Our approach stands out by the integrative perspective and the explicit spatial aspect that it offers. On the one hand, it fully integrates epidemiological, mobility and GIS-data models at an aggregate population level in order to support public health decision making. This is made possible because our approach is built on data automatically processed from transportation surveys that are widely available, at least in North America and Europe. Our approach is thus simple and can be promptly put into use. On the other hand, our approach particularly aims at supporting decision makers with respect to "spatialized" intervention policies. Mainly, it allows for the assessment of different public intervention actions in different spatial locations of the studied area and the evaluation of their effects on the disease spatial evolution and distribution.

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Section: Resultsmentioning

confidence: 99%

Integrated epidemiologic simulation for person to person contagion through urban mobility within GIS

Haddad

Moulin

Thériault

et al. 2012

Proceedings of the First ACM SIGSPATIAL International Workshop on Use of GIS in Public Health

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“…Agents are used in modelling the spread of infectious diseases with the capability of tracking the movement of a disease and the contacts between each individual in a social group located in a geographic area (Bagni et al, 2002;Patlolla et al, 2004). Agent-based models describe the stochastic propagation of a disease at the individual level, thus taking into account the explicit social and spatial structure of the population under consideration (Gordon, 2003;Dunham, 2005;Hotchkiss et al 2005).…”

Section: Proximity-based Modelling In Healthcare: Why Abs?mentioning

confidence: 99%

“…By modelling the correlations between individuals, it is possible to understand the role of spatial heterogeneity in spreading dynamics. Different ABS models have been developed in order to depict the spatial behaviour of diverse infectious diseases through structured and realistic urban networks (Patlolla et al, 2004;Eidelson et al, 2004;Carley et al, 2006).…”

Section: Proximity-based Modelling In Healthcare: Why Abs?mentioning

confidence: 99%

Proximity-based modelling of cross-contamination through agent-based simulation: a feasibility study

et al. 2013

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Proximity-based modelling methodology enables mathematical representation of a real system that is characterised by the existence of entities that come into physical contact. Healthcare systems can benefit from this methodology since physical proximity between entities (e.g., patients, clinical items like surgical equipment and blood units) can result in the spread of infectious diseases and cross-contamination. The existing analytical techniques, which are mainly based on differential equations, are unsuitable for representing the fine-grained, micro-world view of the entity interactions that we intend to model. We therefore extend Agent-Based Simulation (ABS) by enabling individual agents to be aware of the physical location of the other agents being modelled in a 3-Dimensional space -this is a perquisite for our proximitybased modelling methodology. To demonstrate the feasibility of our approach we experiment with a scenario wherein boxes of degradable clinical items, modelled as agents, are stored in close proximity. We use Cutting and Packing Optimisation (CPO) algorithms from literature to define the arrangement of these agents in the 3-D space and to make the individual agents "location-aware". An ABS model then simulates crosscontamination by modelling the spread of contaminants amongst the agents confined in the well-defined space. Our approach can be used to model analogous situations wherein physical proximity between entities in the underlying system is a necessary condition for entity interactions.

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“…This is an important factor to consider in highly contagious diseases and therefore this methodology gave way to a new approach. Agent-based modeling (ABM), is also a bottom-up approach, similar to CA models, but has the advanced capability of tracking the movement of a disease and the contacts between each individual in a social group located in a geographic area [ 15 , 16 ]. The potentials that ABM possess to model epidemic spread, have been used in epidemiology to study and track the movement of infected individuals and their contacts in a social system [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…ABMs and their ability to produce emergent macro-effects from micro-rules have served as a cornerstone for the development of different methodological frameworks in epidemiology [ 16 ]. Epidemiologic applications using ABM approach are mostly designed to allow epidemiological researchers to do a preliminary "what-if" analysis with the purpose of assessing systems' behaviour under various conditions and evaluating which alternative control policies to adopt in order to fight epidemics such as smallpox [ 22 - 24 ].…”

Section: Introductionmentioning

confidence: 99%