connectors

connectors

Define connectors, which are used to mediate interactions between modules when the sim is run.

While most of the coupling between disease modules happens automatically via shared states (e.g., shared susceptibility), connectors allow explicit cross-module logic such as seasonal forcing or co-infection effects.

Classes

Name	Description
Connector	Base class for Connectors, which mediate interactions between disease (or other) modules
seasonality	Example connector – apply sine-wave seasonality of transmission to one or more diseases

Connector

connectors.Connector(name=None, label=None, **kwargs)

Base class for Connectors, which mediate interactions between disease (or other) modules

Because connectors can do anything, they have no specified structure: it is up to the user to define how they behave.

seasonality

connectors.seasonality(**kwargs)

Example connector – apply sine-wave seasonality of transmission to one or more diseases

This works by modifying the disease’s rel_trans state; note that it replaces it with the seasonality variable, and will overwrite any existing values. (Note: this function would work more or less identically as an intervention, but it is closer in spirit to a connector.)

Parameters

Name	Type	Description	Default
diseases	str / list	disease or list of diseases to apply seasonality to	required
scale	float	how strong of a seasonality effect to apply (0.1 = 90-110% relative transmission rate depending on time of year)	required
shift	float	offset by time of year (0.5 = 6 month offset)	required

Examples

import starsim as ss

pars = dict(
    n_agents = 10_000,
    start = '2020-01-01',
    stop = '2023-01-01',
    dt = ss.weeks(1.0),
    diseases = dict(
        type = 'sis',
        beta = ss.perweek(0.05),
        dur_inf = ss.weeks(5),
        waning = ss.perweek(0.1),
        dt = ss.weeks(1),
    ),
    networks = 'random',
)

s1 = ss.Sim(pars, connectors=None, label='Random network')
s2 = ss.Sim(pars, connectors=ss.seasonality(), label='Seasonality')
s3 = ss.Sim(pars, connectors=ss.seasonality(scale=0.5, shift=0.2), label='Extreme seasonality')

msim = ss.parallel(s1, s2, s3)
msim.plot('sis')

s3.connectors[0].plot()

Methods

Name	Description
step	Apply seasonality

step

connectors.seasonality.step(*args, **kwargs)

Apply seasonality