Exploring Trade and Health Policies Influence on Dengue Spread with an Agent-Based Model

Philippon, Damien; Choisy, Marc; Drogoul, Alexis; Gaudou, Benoît; Marilleau, Nicolas; Taillandier, Patrick; Truong, Quang Chi

doi:10.1007/978-3-319-67477-3_6

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…The wide range of possible interventions makes it extremely difficult to decide which ones are most appropriate in a given context. In this regard, computer modeling is an invaluable tool for exploring a range of intervention strategies in silico before the potential field implementation phase (11)(12)(13). It has been widely used, for example, to justify public health policies based on locking down entire populations (6,14).…”

Section: Proposal: An Agent-based Spatially Explicit Modeling Kitmentioning

confidence: 99%

COMOKIT: A Modeling Kit to Understand, Analyze, and Compare the Impacts of Mitigation Policies Against the COVID-19 Epidemic at the Scale of a City

Gaudou

Huynh

Philippon

et al. 2020

Front. Public Health

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Since its emergence in China, the COVID-19 pandemic has spread rapidly around the world. Faced with this unknown disease, public health authorities were forced to experiment, in a short period of time, with various combinations of interventions at different scales. However, as the pandemic progresses, there is an urgent need for tools and methodologies to quickly analyze the effectiveness of responses against COVID-19 in different communities and contexts. In this perspective, computer modeling appears to be an invaluable lever as it allows for the in silico exploration of a range of intervention strategies prior to the potential field implementation phase. More specifically, we argue that, in order to take into account important dimensions of policy actions, such as the heterogeneity of the individual response or the spatial aspect of containment strategies, the branch of computer modeling known as agent-based modeling is of immense interest. We present in this paper an agent-based modeling framework called COVID-19 Modeling Kit (COMOKIT), designed to be generic, scalable and thus portable in a variety of social and geographical contexts. COMOKIT combines models of person-to-person and environmental transmission, a model of individual epidemiological status evolution, an agenda-based 1-h time step model of human mobility, and an intervention model. It is designed to be modular and flexible enough to allow modelers and users to represent different strategies and study their impacts in multiple social, epidemiological or economic scenarios. Several large-scale experiments are analyzed in this paper and allow us to show the potentialities of COMOKIT in terms of analysis and comparison of the impacts of public health policies in a realistic case study.

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Section: Proposal: An Agent-based Spatially Explicit Modeling Kitmentioning

confidence: 99%

COMOKIT: A Modeling Kit to Understand, Analyze, and Compare the Impacts of Mitigation Policies Against the COVID-19 Epidemic at the Scale of a City

Gaudou

Huynh

Philippon

et al. 2020

Front. Public Health

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“…Other examples could have been cited such as the management of evacuation sign in case of Tsunami in Vietnam [46] or the spreading of dengue fever in Delhi [48] and in Vietnam [52].…”

Section: Crisis Managementmentioning

confidence: 99%

Building, composing and experimenting complex spatial models with the GAMA platform

Taillandier¹,

et al. 2018

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Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible To cite this version: Taillandier AbstractThe agent-based modeling approach is now used in many domains such as geography, ecology, or economy, and more generally to study (spatially explicit) socio-environmental systems where the heterogeneity of the actors and the numerous feedback loops between them requires a modular and incremental approach to modeling. One major reason of this success, besides this conceptual facility, can be found in the support provided by the development of increasingly powerful software platforms, which now allow modelers without a strong background in computer science to easily and quickly develop their own models. Another trend observed in the latest years is the development of much more descriptive and detailed models able not only to better represent complex systems, but also answer more intricate questions. In that respect, if all agent-based modeling platforms support the design of small to mid-size models, i.e. models with little heterogeneity between agents, simple representation of the environment, simple agent decisionmaking processes, etc., very few are adapted to the design of large-scale models. GAMA is one of the latter. It has been designed with the aim of supporting the writing (and composing) of fairly complex models, with a strong support of the spatial dimension, while guaranteeing non-computer scientists an easy access to high-level, otherwise complex, operations. This paper presents GAMA 1.8, the latest revision to date of the platform, with a focus on its modeling language and its capabilities to manage the spatial dimension of models. The capabilities of GAMA are illustrated by the presentation of applications that take advantage of its new features.

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“…Becu et al [7] used a participatory simulation model to develop a simulation to foster social learning about coastal risk prevention measures with local authorities and managers. D. Philippon et al [5] explored trade and health policies' influence on Dengue spread with an agent-based model. The research contributed to the domain by presenting a sophisticated model to deal with the shortage of data.…”

Section: Related Workmentioning

confidence: 99%

Covid-19 Disease Simulation using GAMA platform

Duong

Sơn

Dinh

2020

2020 International Conference on Computational Intelligence (ICCI)

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In less than three months after its emergence in China, the Covid-19 pandemic has spread to at least 180 countries. In the absence of previous experience with this new disease, public health authorities have implemented many experiments in a short period and, in a mostly uninformed way, various combinations of interventions at different scales. These include a ban on large gatherings, closure of bordersindividual and collective containment, monitoring of population movements, social tracing, social distancing, etc. However, as the pandemic is progressing, data are collected from various sources. On the one hand, authorities allow to make informed adjustments to the current and planned interventions and reveal them. On the other hand, an urgent need for tools and methodologies that enable fast analysis, understanding, comparison, and forecasting of the effectiveness of the responses against COVID-19 across different communities and contexts. In this perspective, computational modeling appears as invaluable leverage as it allows us to explore in silico a range of intervention strategies before the potential phase of field implementation.

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Exploring Trade and Health Policies Influence on Dengue Spread with an Agent-Based Model

Cited by 6 publications

References 9 publications

COMOKIT: A Modeling Kit to Understand, Analyze, and Compare the Impacts of Mitigation Policies Against the COVID-19 Epidemic at the Scale of a City

COMOKIT: A Modeling Kit to Understand, Analyze, and Compare the Impacts of Mitigation Policies Against the COVID-19 Epidemic at the Scale of a City

Building, composing and experimenting complex spatial models with the GAMA platform

Covid-19 Disease Simulation using GAMA platform

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