The Kendrick modelling platform: language abstractions and tools for epidemiology

Abstract

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. Electronic supplementary material The online version of this article (10.1186/s12859-019-2843-0) contains supplementary material, which is available to authorized users.

DTU‐DADS‐Aqua: A simulation framework for modelling waterborne spread of highly infectious pathogens in marine aquaculture

“…The DTU-DADS framework was chosen as a template to develop DTU-DADS-Aqua, given its availability in open-source software (R Core Team, 2018), the publiclyaccessible framework not being computationally demanding, and the technical features described herein. Like DTU-DADS, other modelling frameworks also include several features of relevance to epidemiologic research (Bradhurst et al, 2015;BUI T et al, 2019;Garner & Beckett, 2005;Harvey et al, 2007;Jenness et al, 2018;King et al, 2016;Marques et al, 2020;O'Hare et al, 2016;Picault et al, 2019;Stevenson et al, 2013;Widgren et al, 2019). This framework offers a high degree of configurability and several built-in functions for the user to explore.…”

“…Many frameworks for infectious disease modelling in terrestrial animals have been developed and are available to elucidate the role of different disease transmission pathways and reproduce the dynamics of such diseases (Bradhurst et al, 2015;BUI T et al, 2019;Garner & Beckett, 2005;Harvey et al, 2007;Jenness et al, 2018;King et al, 2016;Marques et al, 2020;O'Hare et al, 2016;Picault et al, 2019;Stevenson et al, 2013;Widgren et al, 2019). These modelling frameworks offer a broad range of functionalities relevant to epidemiologic research and have been applied in different contexts, such as to study zoonotic, vector-borne and emerging infectious diseases (Bradhurst et al, 2015;Ferrari et al, 2021;Picault et al, 2019;Salines et al, 2020).…”

Leveraging artificial intelligence and software engineering methods in epidemiology for the co-creation of decision-support tools based on mechanistic models

“…So far and to the best of our knowledge, there is no equivalent approach to foster such continuum between academic research in animal health and on-farm applications. Regarding epidemiological modelling, DSLs have been proposed in only two platforms to represent mechanistic models: Kendrick (Bui T. et al, 2019) and EMULSION (Picault et al, 2019).…”

EMULSION: Transparent and flexible multiscale stochastic models in human, animal and plant epidemiology

“…Artificial intelligence (AI) can help going further, as demonstrated in a promising approach, KENDRICK [18], which defines a domain-specific language (DSL, [19]), which allows to describe models as text files rather than executable code, enforces a clear separation of concerns (infection, demography, etc.) to facilitate model assessment by scientists from several domains, and is used to generate an optimized simulation code, which guarantees that model features are translated without bias into the program.…”