SEIR 0.2.3

[simulation]

The [simulation] section defines parameters relating to the numerical simulation of the SEIR ordinary differential equation. Supported parameters are

[simulation]
max_simulation_time = 300
method = DOP853
max_step = 0.5

Here the only required parameter is max_simulation_time, i.e., the number of simulated days.

The parameter method can be used to change the numerical integration routine. For supported values, please check the documentation of scipy.integrate.solve_ivp.

max_step defines the maximum time-step used in the integration.

[model] (no compartmentalization)

The [model] section defines the parameters of the disease model. In its simplest form, where you wish to model the entire population and do not wish to compartmentalize it, the [model] section looks like

[model]
population = 5e6
incubation_period = 3
infectious_period = 7
initial_R0 = 2.5
hospitalization_probability = 0.1
hospitalization_duration = 20
hospitalization_lag_from_onset = 7
icu_probability = 0.01
icu_duration = 10
icu_lag_from_onset = 11
death_probability = 0.1
death_lag_from_onset = 25

Here the parameters are

incubation_period

Incubation period of the disease in days.

infectious_period

How long a patient can infect others (in days) after the incubation period.

initial_R0

Basic reproductive number of the disease

hospitalization_probability

Probability that an infected person needs hospitalization

hospitalization_duration

Average duration of a hospitalization in days.

hospitalization_lag_from_onset

Average time (in days) from the onset of symptoms to admission to hospital

icu_probability

Probability that an infected person needs hospitalization.

icu_duration

Average duration of the need for intensive care in days.

icu_lag_from_onset

Average time (in days) from the onset of symptoms to admission to ICU.

death_probability

Probability that an infected person dies from the disease.

death_lag_from_onset

Average time from the onset of symptoms to death (in days).

population

The total population.

[model] (compartmentalization)

The [model] section defines the parameters of the disease model. When you wish to separate your population into various compartments (e.g., age groups), your [model] section becomes more involved.

As an example, consider the population of Finland, divided to three compartments by ages: 0…15, 16…65, and 65+

[model]
compartments =
    0-15,
    16-65,
    65+

population =
    871036,
    3422996,
    1231274

incubation_period = 3
infectious_period = 7
initial_R0 = 2.5

hospitalization_probability =
    0.11,
    0.17,
    0.29

hospitalization_duration = 20
hospitalization_lag_from_onset = 7
icu_probability = 0.01
icu_duration = 10
icu_lag_from_onset = 11
death_probability = 0.1
death_lag_from_onset = 25

Here the parameters are

compartments

A comma-separated list of the compartment names

population

A comma-separated list of population of each compartment

incubation_period

Incubation period of the disease in days. If a single number, the same number is used for all compartments. You can define a different incubation period for each compartment by supplying a comma-separated list.

infectious_period

How long a patient can infect others (in days) after the incubation period. If a single number, the same number is used for all compartments. You can use a different value for each compartment by supplying a comma-separated list.

initial_R0

Basic reproductive number of the disease. A single number.

hospitalization_probability

Probability that an infected person needs hospitalization. If a single number, the same number is used for all compartments. You can use a different value for each compartment by supplying a comma-separated list.

hospitalization_duration

Average duration of a hospitalization in days.

hospitalization_lag_from_onset

Average time (in days) from the onset of symptoms to admission to hospital.

icu_probability

Probability that an infected person needs hospitalization. If a single number, the same number is used for all compartments. You can use a different value for each compartment by supplying a comma-separated list.

icu_duration

Average duration of the need for intensive care in days.

icu_lag_from_onset

Average time (in days) from the onset of symptoms to admission to ICU.

death_probability

Probability that an infected person dies from the disease. If a single number, the same number is used for all compartments. You can use a different value for each compartment by supplying a comma-separated list.

death_lag_from_onset

Average time from the onset of symptoms to death (in days).

[initial state] (no compartmentalization)

When there are no compartments in the model, the [initial state] section of the configuration file should look something like

[initial state]
probabilities = True
population_susceptible = 0.8
population_exposed = 0.15
population_infected = 0.05

Here the parameters are

probabilities

If true, the rest of the parameters in this section are considered as probabilities, and the total number of exposed/infected people is computed by multiplying the total population by the provided value.

population_exposed

The total number (or probability) of exposed people

population_infected

The total number (or probability) of infected people

[initial state] (compartmentalized)

When there are compartments in the model, the [initial state] section of the configuration file should look something like

[initial state]
probabilities = True
population_exposed =
    0.001,
    0.01,
    0.005
population_infected =
    0.001,
    0.01,
    0.005

Here the parameters are

probabilities

If true, the rest of the parameters in this section are considered as probabilities, and the total number of exposed/infected people is computed by multiplying the total population by the provided value.

population_exposed

The total number (or probability) of exposed people

population_infected

The total number (or probability) of infected people

[restrictions]

We can model restrictions such as social distancing and closing of schools by introducing time-dependence in the infectivity rate (matrix, if compartmentalized model).

Restrictions can be defined in the config file within sections named [restriction TITLE]. You can define multiple restrictions in the same file.

The restrictions are implemented as prefactors of the infectivity rate as

[restriction social-distancing]
day-begins = 20
day-ends = 180
infectivity modifier = 0.7

[restriction social-distancing experiment 2]
day-begins = 20
day-ends = 180
infectivity modifier =
    [ 0-4, 5-9, 15-19 ] : [ 35-39, 40-44 ] : 0.8
    all : [ 60-64, 65+ ] : 0.2

[restriction social-distancing experiment 2]
day-begins = 20
day-ends = 180
infectivity modifier = file://my_data/restrictions_prefactor.csv