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.
Background Avian influenza A viruses (AIVs) are among the most concerning emerging and re-emerging pathogens because of the potential risk for causing an influenza pandemic with catastrophic impact. The recent increase in domestic animals and poultry worldwide was followed by an increase of human AIV outbreaks reported. Methods We reviewed the epidemiology of human infections with AIV from the literature including reports from the World Health Organization, extracting information on virus subtype, time, location, age, sex, outcome, and exposure. Results We described the characteristics of more than 2500 laboratory-confirmed human infections with AIVs. Human infections with H5N1 and H7N9 were more frequently reported than other subtypes. Risk of death was highest among reported cases infected with H5N1, H5N6, H7N9, and H10N8 infections. Older people and males tended to have a lower risk of infection with most AIV subtypes, except for H7N9. Visiting live poultry markets was mostly reported by H7N9, H5N6, and H10N8 cases, while exposure to sick or dead bird was mostly reported by H5N1, H7N2, H7N3, H7N4, H7N7, and H10N7 cases. Conclusions Understanding the profile of human cases of different AIV subtypes would guide control strategies. Continued monitoring of human infections with AIVs is essential for pandemic preparedness.
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.
In recent years, agent-based simulation has become an important tool to study complex systems. However, the models produced are rarely used for decision-making support because stakeholders are o en not involved in the modeling and simulation processes. Indeed, while several tools dedicated to participatory modeling and simulation exist, these are limited to the design of simple KISS models, thus reducing their potential impact. In this article, we present the new participatory tools integrated within the GAMA modeling and simulation platform. These tools, which take advantage of the GAMA platform with respect to the definition of rich KIDS models, allow the modelers to build models graphically and develop distributed serious games easily. Several application examples illustrate their use and potential.
OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 18788The contribution was presented at MABS 2016 : https://sites.google.com/site/mabsworkshop2016/ Abstract. With the globalization, several free trade areas have been and are being created all around the world. They usually have positive consequences for increasing economic exchanges, but negative ecological or health side effects. These negative effects are difficult to predict or even to understand due to the complexity of the system and of the number of involved processes. In this article, we focus on the Southeast Asia free trade area (the ASEAN) and specifically in the East-West economic corridor. A significant correlation has been observed in this area between the corridor opening and dengue fever cases, without being able to establish a causality relationship. We choose to tackle this issue by building an agent-based geographically explicit model. We propose an approach coupling dengue fever dynamics, climate data, economic mobility and health policies, following a design methodology decomposing these processes in sub-models and linking them to make one integrated model. In addition, we propose a way to deal with lack of data in the modeling process. Our simulation results show that there is influence of the increase in mobility and application of different control policies on the increase of dengue cases.
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