laser.generic.SIR
laser.generic.SIR
Export required components for an SIR model.
Agents transition from Susceptible to Infectious upon infection and are infectious for a duration. Agents transition from Infectious to Recovered upon recovery. Agents remain in the Recovered state indefinitely (no waning immunity).
Infectious(model, infdurdist, infdurmin=1, validating=False)
Infectious Component for SIR/SEIR Models (With Recovery to Immune)
This component manages agents in the infectious state for models where infected individuals
recover permanently (i.e., transition to a RECOVERED state without waning). It supports
agent-level infection durations and patch-level tracking of recoveries over time.
Infectious component for an SIR/SEIR model - includes infectious duration, no waning immunity in newly_recovered state.
Agents transition from Infectious to Recovered after the infectious period (itimer).
Tracks number of agents recovering each tick in model.nodes.newly_recovered.
Responsibilities
- Initializes infected agents and their infection timers (
itimer) based on scenario input - Decrements
itimerdaily for infectious agents - Transitions agents from
INFECTIOUStoRECOVEREDwhenitimer == 0 - Updates patch-level state variables:
I[t, i]: infectious count at ticktin nodeiR[t, i]: recovered countrecovered[t, i]: number of recoveries during tickt
Required Inputs
model.scenario.I: number of initially infected individuals per patchinfdurdist: function returning infection durationsinfdurmin: minimum infectious period (default = 1 day)
Outputs
model.people.itimer: countdown timers per agentmodel.nodes.I[t],.R[t]: infectious and recovered counts per patchmodel.nodes.recovered[t]: daily recoveries per patch
Step Behavior
- Infectious agents decrement
itimer - When
itimer == 0, agent state is set toRECOVERED - Patch-level
IandRare updated;recoveredlogs today's transitions
Validation
- Ensures internal consistency between agent state and timer
- Confirms agents with
itimer == 1recover exactly one day later - Validates population conservation (
S + I + R = N)
Plotting
The plot() method shows per-node and total infectious counts across time.
Examples:
1 2 3 4 5 6 | |
step(tick)
Step function for the Infected component.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
tick
|
int
|
The current tick of the simulation. |
required |
Recovered(model, validating=False)
Recovered Component for SIR/SEIR Models (Permanent Immunity)
This component manages agents in the recovered state in models where immunity does not wane (i.e., once recovered, agents stay recovered permanently). It tracks the number of recovered individuals over time at the patch level, but performs no active transitions itself — recovery transitions must be handled by upstream components.
Responsibilities
- Initializes agents as recovered if specified in
model.scenario.R - Tracks per-patch recovered counts over time in
model.nodes.R - Verifies consistency between agent state and aggregate recovered counts
- Propagates recovered totals forward unchanged (unless modified by other components)
Required Inputs
model.scenario.R: number of initially recovered individuals per node
Outputs
model.nodes.R[t, i]: number of recovered individuals at ticktin nodei
Step Behavior
- At each tick, carries forward: R[t+1] = R[t]
- This component does not change any agent's state or internal timers
Validation
- Ensures per-agent state matches aggregate
Rcounts before and after each step - Detects accidental changes to recovered counts not explained by upstream logic
Plotting
The plot() method shows per-node and total recovered counts over time.
Examples:
1 2 3 4 5 | |
Susceptible(model, validating=False)
Susceptible Component for Patch-Based Agent-Based Models (S, SI, SIS, SIR, SEIR, etc.)
This component initializes and tracks the count of susceptible individuals (S) in
a spatially structured agent-based model. It is compatible with all standard LASER
disease progression models that include a "susceptible" state.
Responsibilities
- Initializes agent-level properties:
nodeid: Patch ID of each agent (uint16)state: Infection state (int8), defaulting toState.SUSCEPTIBLE
- Initializes node-level property:
S[t, i]: Susceptible count in nodeiat timet
- At each timestep, propagates the susceptible count forward (
S[t+1] = S[t]), unless modified by other components (e.g., exposure, births). - Validates consistency between patch-level susceptible counts and agent-level state.
Usage
Add this component early in the component list for any model with SUSCEPTIBLE agents, typically before transmission or exposure components. Compatible with:
SIR.TransmissionSIR.ExposureSIR.InfectiousSIR.Recovered- Custom SEIRS extensions
Requires
model.people: A LaserFrame for all agentsmodel.nodes: Patch-level statemodel.scenario: Input DataFrame withpopulationand optionallyScolumnsmodel.params.nticks: Number of simulation ticks
Validation
- Ensures consistency of susceptible counts before and after each step
- Prevents unintentional state drift by validating against agent
statevalues
Output
model.nodes.S: A(nticks+1, num_nodes)array of susceptible counts- Optional plotting via
plot()for visual inspection of per-node and totalS
Step Behavior
For tick t: S[t+1] = S[t] # Unless explicitly modified by other components
This component does not alter agent states directly but serves as a synchronized counter and validator of susceptible individuals.
Examples:
1 2 3 4 5 6 7 | |
Transmission(model, infdurdist, infdurmin=1, seasonality=None, validating=False)
Transmission Component for SIS/SIR/SIRS Models (S → I with Duration)
This component simulates the transition from SUSCEPTIBLE to INFECTIOUS in
models where infectious individuals have a finite infection duration (itimer).
It supports full spatial coupling and allows infection durations to vary by node
and tick.
Agents transition from Susceptible to Infectious based on force of infection.
Sets newly infectious agents' infection timers (itimer) based on infdurdist and infdurmin.
Tracks number of new infections each tick in model.nodes.newly_infected.
Responsibilities
- Computes force of infection (FOI)
λ = β * (I / N)per patch each tick - Applies optional spatial coupling via
model.network(infection pressure transfer) - Converts FOI into Bernoulli probabilities using
p = 1 - exp(-λ) - Infects susceptible agents stochastically, assigning per-agent
itimer - Updates patch-level susceptible (
S) and infectious (I) counts - Records number of new infections per tick in
model.nodes.newly_infected
Required Inputs
model.params.beta: transmission rate (global)model.network: [n x n] matrix of transmission couplinginfdurdist(tick, node): callable sampling the infectious duration distributionmodel.people.itimer: preallocated per-agent infection timer
Outputs
model.nodes.forces[t, i]: computed FOI in nodeiat timetmodel.nodes.newly_infected[t, i]: new infections in nodeion time stept
Step Behavior
- Computes FOI (
λ) for each node - Applies inter-node infection pressure via
model.network - Converts FOI into a Bernoulli probability using:
p = 1 - exp(-λ) - Infects susceptible agents probabilistically
- Updates state and records incidence
Validation
- Ensures consistency between incidence and change in
I - Checks for correct state and population accounting before and after tick
Plotting
The plot() method visualizes per-node FOI (λ) over simulation time.
Examples:
1 2 3 4 5 | |
Initializes the TransmissionSI component.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
model
|
Model
|
The epidemiological model instance. |
required |
infdurdist
|
Callable[[int, int], float]
|
A function that returns the infectious duration for a given tick and node. |
required |
infdurmin
|
int
|
Minimum infectious duration. |
1
|
seasonality
|
Union[ValuesMap, ndarray]
|
Seasonality modifier for transmission rate. Defaults to None. |
None
|
validating
|
bool
|
Enable component-level validation. Defaults to False. |
False
|