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Explicit Composition
Constructs in DSLs
The case of the epidemiological language Kendrick
Bùi Thị Mai Anh, Nick Papoulias, Mikal Ziane
Serge Stinckwich
IWST 2016, Prague
Cameroon
Université de
Yaoundé 1
Vietnam
IFI-VNU & USTH
Morocco
LMDP, Université Cadi Ayyad de Marrakech
Senegal
UGB (Université Gaston Berger de Saint-
Louis)
Senegal
UCAD (Université Cheikh Anta Diop de
Dakar)
65 members + Phd Students: Professors, Researchers, Associates (26 HDR),1 research engineer, 3 admin
staff, 3 post-docts and 45 PhD students
Texte
International	Research	Unit	UMMISCO	Mathematical	and	Computational	Modeling	of	Complex	
Systems	Laboratory
3
UMMISCO’s	approach	is	based	on	the	building	of	models	that	are	abstract	
(simplified)	representation	of	a	system	which	supports	answering	questions	about	
the	system.				
question
System
Model
represents
3	Key	application	domains		:(i)	Emerging	diseases		(ii)	Climate	change	and	natural	hazards.(iii)	
Ecosystems	and	natural	resources.
Simulation
interprets/analyses
Analysis or
UMMISCO East-Central Africa Unit (Yaoundé)
Research Activities on Model-Driven Epidemiology
1 Professor (Maurice Tchuenté)
2 researchers from UMMISCO/Bondy (3 months in 2016)
6 PhD students
EPICAM KENDRICK
Epidemiological
Surveillance
System
Modeling&Simulation
of Epidemiology Models
Process
Mining
Ontologies
Domain-
Specific
Languages
and Tools
Model-
Driven
Engineering
Multi-Agent
Simulation
https://github.com/UMMISCO/EPICAM
EPICAM is a Model-Driven Engineering Platform
for Epidemiological Surveillance System
Deployed on 25 sites
around Cameroon
3 PhD students from
Yaoundé 1 University involved
Applied to Tuberculosis but adaptable to other diseases
Model-Driven Engineering
that allows automatic generation
of the platform
http://ummisco.github.io/kendrick/
Kendrick is a platform for
epidemiological modeling and analysis
Visualizations
It helps epidemiologists craft
custom analyses cheaply
BrowsersModels
2 PhD students from
Yaoundé 1 University involved
What is Epidemiology
Modeling ?
• Building mathematical
models to study
speed of a disease in
a population
• Compartmental
models
SIR Model
Models Simulations
• 3 ways to do simulations:
• Population-level: deterministic simulation (ODEs
solver)
• Individual-Level: stochastic simulation (Gillepsie
simulation)
• Agent-based level
Multi-concerns Models of
Epidemiology
• Seasonality
• Multi-hosts
• Multi-strains
• Age/Risk structure
• Spatial aspect
• Control strategies
Multi-hosts concerns
Multi-strains concerns
Spatial concerns
Control Strategies
concerns
How epidemiologists
are building their
models ?
SEIRS model
Concerns are scattered
Concerns are scattered
Concerns are tangled
Concerns are scattered
Concerns are tangled
Not modular, not reusable
Separation of Concerns in
Epidemiology Modeling
• Decompose highly-coupled monolithic models into
modular concerns
• define concerns with as few dependencies as
possible
• Combine concerns as freely as possible
Solution
• Generic mathematical meta-model that provides
abstractions to define epidemiological concerns
• Models are expressed as Stochastic Automata
• Composition operator (tensor sum)
• Transforms concerns to modify automata
Tensor Composition of a
spatial concern with SIR model
Kendrick DSL
• Implemented the generic mathematical meta-
models in Smalltalk
• Embedded DSL in Pharo
• Allows definition and composition of concerns
• https://github.com/UMMISCO/kendrick
Kendrick
Demo
If you want to try Kendrick
download a MOOSE 6.0 image from
http://agilevisualization.com/
Participants
• Fabrice Atrevi (IFI-VN)
• Bui Thi Mai Anh (UMMISCO-VN)
• Ho Tuong Vinh (UMMISCO-VN)
• Cheikhou Oumar Ka (UMMISCO-Senegal)
• Nick Papoulias (UMMISCO-France)
• Benjamin Roche (UMMISCO-France)
• Aboubakar Sidiki (UMMISCO-Cameroon)
• Serge Stinckwich (UMMISCO-France)
• Mikal Ziane (LIP6, UPMC)
Conclusions
• Kendrick is an embedded DSL for epidemiology
modeling/simulations that promote separation of
concerns
• Chapter 15 in “Agile Visualisation Book”: http://
agilevisualization.com/
• We rely a lot on the Pharo community: PolyMath,
Roassal, PetitParser, Moose, STon, SmalltalkCI
Ongoing Work
• GPU (VirtualGPU) implementation of stochastic
algorithms - Cheik Oumar Ka
• Network concerns - Aboubakar Sidiki (April-May
2016)
• Metamorphic tests of Epi models - Herman
Mekontso Tchinda
• User eXperiments (in collaboration with Nick
Papoulias during 2017)

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