BACKGROUND: Mathematical and computational models are widely used to study the transmission, pathogenicity, and propagation of infectious diseases. Unfortunately, complex mathematical models are difficult to define, reuse and reproduce because they are composed of several concerns that are intertwined. The problem is even worse for computational models because the epidemiological concerns are also intertwined with low-level implementation details that are not easily accessible to non-computing scientists. Our goal is to make compartmental epidemiological models easier to define, reuse and reproduce by facilitating implementation of different simulation approaches with only very little programming knowledge. RESULTS: We achieve our goal through the definition of a domain-specific language (DSL), Kendrick, that relies on a very general mathematical definition of epidemiological concerns as stochastic automata that are combined using tensor-algebra operators. A very large class of epidemiological concerns, including multi-species, spatial concerns, control policies, sex or age structures, are supported and can be defined independently of each other and combined into models to be simulated by different methods. Implementing models does not require sophisticated programming skills any more. The various concerns involved within a model can be changed independently of the others as well as reused within other models. They are not plagued by low-level implementation details. CONCLUSIONS: Kendrick is one of the few DSLs for epidemiological modelling that does not burden its users with implementation details or required sophisticated programming skills. It is also currently the only language for epidemiology modelling that supports modularity through clear separation of concerns hence fostering reproducibility and reuse of models and simulations. Future work includes extending Kendrick to support non-compartmental models and improving its interoperability with existing complementary tools.

Following publication of the original article [1], the author noticed that the following lines were missing from the published article. The original article has been corrected.

To investigate cell spatial organization in complex biological dynamics, an individual-based model that represents cell motion in a deterministic way is proposed and then experimented on avascular tumor growth case. Cell motion remains a fundamental process in many complex biological dynamics such as morphogenesis or cell aggregation. Mathematical models are often used to represent cellular motility and spatial dynamics. However, the variability and the interactions between individuals, which are crucial to build accurate predictive models, are difficult to represent using differential equations. We propose, in this paper, an individual-based approach that allows describing cell motion at deterministic way with diffusion and convection processes. Such approach is built using Smoothed Particle Hydrodynamics (SPH) method and allows representing spatially each cell and its basic biological primitives. The relevance of this approach is illustrated on avascular tumor growth to study the effects of microenvironment (nutrients) and cell individual motion in tumor development. The results shown that the model can qualitatively predict a number of cellular behaviors that have been observed in experiments.

Maintaining the network connectivity in mobile Multi-Robot Systems (MRSs) is a key issue in many robotic applications. In our view, the solution to this problem consists of two main steps: (i) making robots aware of the network connectivity; and (ii), making use of this knowledge to plan robots tasks without compromising connectivity. In this paper, we view the ad-hoc network connectivity as an abstraction that is independent from application issues. We propose a new distributed algorithm executing on individual robots to build the connectivity-awareness. The correctness and theoretical analysis of the proposed algorithm are given. We also show how our solution allows checking network bi-connectivity more efficiently than existing work and can be used, for example, during distributed control motion.

Exploration of an unknown environment is one of the major applications of multi-robot systems. Many works have proposed multi-robot coordination algorithms to accomplish exploration missions based on multi-agent techniques. Some of these works focus on multi-robot exploration under communication constraints. In this paper, we propose an original way to formalize and solve this issue. Our proposal relies on distributed constraint satisfaction problems (disCSP) which are an extension of classical constraint satisfaction problems (CSP). Compared to other works, our proposal is fully distributed and guaranties the exploration of an unknown environment with maintenance of connectivity between all the members of a robots' team.