4. Modelling language (basics)¶

4.1. YAML Syntax in a nutshell¶

EMULSION models must respect the YAML format, which is based on lists and key-value mappings. Data structures are delimited by 2-space indentation.

Whatever is put after # is not interpreted.
Values
Numbers (3, 3.14), strings ('some text'), booleans (yes/no), lists or key-value mappings.
Lists

A succession of values, e.g.

[value1, value2, value3]


which is equivalent to:

- value1
- value2
- value3

Key-value mappings

An (unordered) set of associations between unique indentifiers (keys) and any value, e.g.

{key1: value1, key2: value2, key3: value3}


which is equivalent to:

key1: value1
key2: value2
key3: value3


All elements above can be combined and nested to build complex structures, for instance:

# Here a key mapped to a list
key1: [v1, v2, v3]

# Here a key mapped to another mapping
key2:
subkey1:
# the value associated with subkey1 is a list
- item1
- item2
subkey2: 'an important message'
subkey3:
# and each element of the list below is a mapping
- another: value1
withother: value2
- another: value3
withother: value4


4.2. Model structure¶

An EMULSION model is divided in several “sections”, corresponding to the main components of a model. Each section corresponds to a first-level key (i.e. put directly at the beginning of a line without any indentation).

Below is a short description of their nature. This is just an overview of what can be found in a typical EMULSION model. To go further, dive into the next chapter!

model_name¶

The name of the model. Used to name figures and diagrams.

Example

model_name: compart_SIR


model_info¶

Several optional information on the model, such as an abstract to describe the model principles and purpose, the authors, references, a license if any, etc.

Example

model_info:
abstract: 'A very long description of the model'
authors:
- First Author
- Another Colleague
DOI: my_doi/10.10.10.


This part is only intended to provide information to the reader. All subsections can be freely defined according to the modeller’s needs.

time_info¶

This section defines the time unit used in the whole model for parameter values (e.g. hours, days, weeks) and the duration of one time step in the simulation. Optionally, it can specify:

• the date where the simulation starts (origin_date)
• the total duration of the simulation (total_duration)
• calendars with events (see Regulate time)

Example

time_info:
# all paramter values are expressed in days/per day
time_unit: 'days'
# the simulation step is 1 day
delta_t: 1
# simulations start on 01/01 (default: current year)
origin: 'January 1'
# simulations run for 100 days
total_duration: '100'


state_machines¶

State machines are the main way to define processes involved in an EMULSION model. A state machine is defined by a list of states and a list of transitions between the states. It can also define a list of productions links between states, to specify which states can produce new individuals.

An EMULSION model can contain several state machines, the only constraint being that all state names must be different.

Example of a typical state machine

state_machines:
health_state:  # the name of the state machine
desc: 'The state machine which defines the evolution of health
states'
# Below, the list of states with their attributes.
states:
- S:
name: 'Susceptible'
desc: 'suceptible of becoming infected'
fillcolor: 'deepskyblue'
- I:
name: 'Infectious'
desc: 'infected and able to transmit the disease'
fillcolor: 'red'
- R:
name: 'Resistant'
desc: 'healthy again and resistant to infection'
fillcolor: 'limegreen'
# Below, a list of transitions between states
transitions:
- {from: S, to: I, rate: 'force_of_infection'}
- {from: I, to: R, rate: 'recovery'}


levels¶

In EMULSION, a level is an organization level which is explicitly represented in the model. For instance, a compartment model has only one level, the population, since individuals are only considered implicitly. A level can contain other sub-levels, base on a specific aggregation type (compartment, IBM, hybrid or metapopulation). A level can also define aggregated variables calculated from the values of another variable at the sublevel.

Examples

A typical level specification in a compartment-based/hybrid model:

levels:
population:
desc: 'level of the population'
aggregation_type: 'compartment'


A typical levels specification in an individual-based model:

levels:
population:
desc: 'level of the population'
aggregation_type: 'IBM'
contains:
- individuals  # the sublevel
individuals:
desc: 'level of the individuals'


grouping¶

This section only concerns compartment-based and hybrid models. It specifies upon which individual variables a population is partitioned, and optionnally the name of a state machine which drives the evolution of the corresponding groups.

For instance, the infection process is driven by the health_state state machine. The value of variable health_state is thus the key to partition the population (all S individuals together, all I together, etc.)

Example

grouping:
population:
infection:
machine_name: health_state
key_variables: [health_state]


processes¶

This section specifies the list of major processes that take place at each level during the simulation. A process name can be:

• the name of a grouping in compartment-based or hybrid models (hence taking place at the population level)
• the name of a state machine in individual-based models (hence taking place at the individual level)
• the name of a Python method developed specifically in a code add-on

Examples

A typical processes specification in a compartment-based/hybrid model:

processes:
population:
- infection


A typical processes specification in an individual-based model:

processes:
individuals:
- health_state


parameters¶

This section is intended to define:

• model parameters (stricto sensu), i.e. numerical values coming from experts, data or assumptions and driving the dynamics of the model
• configuration parameters, i.e. numerical values used in initial conditions or scenario definition
• distributions expressed by functions and returning a new sample each time they are “used” in a computation
• expressions which can combine other parameters or variables

Each entry must be endowed with a full description of its role (desc:) and can also provide information on where the value (or expression) comes from (source:).

Example

parameters:
# a model parameter
transmission_I:
desc: 'transmission rate from infectious individuals (/day)'
value: 0.5
# an expression (of another parameter and variables)
force_of_infection:
desc: 'infection function'
value: 'transmission_I * total_I / total_population'
source: 'classical function assuming frequency dependence'
# a distribution
initial_age:
desc: 'distribution of ages when initializing individuals'
value: 'random_integers(0, 20)'


prototypes¶

Prototypes are used for IBM/hybrid models and for metapopulations. They are intended to specify typical individuals or populations which are characterized by specific values of their variables.

Prototypes are used mainly in two occasions:

• in initial conditions (see below), to specify how many individuals of each kind must be created
• in production links of state machines, to indicate the nature of individuals produced
• in the built-in action become, to make a state machine induce changes in another state machine

Example

prototypes:
# here the level for which the prototypes are defined
individuals:
- healthy:  # the name of the prototype
desc: 'healthy individuals'
health_state: S
# variable age_group is one of the existing states
age_group: random
- infected:
desc: 'infected individuals'
health_state: I
age_group: J


initial_conditions¶

Initial conditions specify how to initialize each level.

In compartment-based models, initial conditions give the total population and the repartition in each compartment.

In IBM, hybrid models and metapopulations, initial conditions rely on prototypes.

Examples

A typical specification of initial conditions in a compartment-based model:

initial_conditions:
# the level for which the initial conditions are defined
population:
- population:
- total: 'initial_population_size'
- vars: [S]  # amount of individuals in state S
amount: 'initial_population_size - initial_infected'
- vars: [I] # amount of individuals in state I
amount: 'initial_infected'
# the amount of individuals in state R is computed as:
# (total - S - I)


A typical specification of initial conditions in an IBM:

initial_conditions:
population:
# a list of prototypes with the number of individuals
# to create with each prototype
- prototype: healthy
amount: 'initial_population_size - initial_infected'
- prototype: infected
amount: 'initial_infected'


outputs¶

When running an EMULSION model, the amounts of individuals in each state for all state machines are computed automatically at each time step. The outputs section specifies how the output data are stored (CSV file, database…) and at which period (in time steps). Additional variables can also be logged (extra_vars).

Using command-line option --plot, one plot per state machine is automatically produced, as well as one plot for all extra variables.

Example

outputs:
type: csv  # produces counts.csv in output directory
population:      # outputs for level population
period: 1  # at each time step
extra_vars:
# add an expression (from 'parameters')
- 'prevalence (%)'
- total_population


actions¶

This section appears when a level requires actions which are not provided in EMULSION generic engine. In that case, the section contains a simple description of the meaning of the action, which has to be defined in a separate Python file.

Example

actions:
desc: 'Check if messages were received from other
individuals and modify contact network accordingly.'


statevars¶

This section appears when a level requires variables which are not defined automatically by their state machines, nor defined as expressions in the parameters section, nor computed by aggregating variables from a sublevel. In that case, the section contains a simple description of the meaning of the variable, which has to be defined in a separate Python file.

Example

statevars:
nb_mothers_of_infected_calf:
desc: 'identify and counts cows which gave birth to an
infected calf'