ModelSEIRMixingQuarantine

ModelSEIRMixingQuarantine#

class epiworldpy.epimodels.ModelSEIRMixingQuarantine(self: epiworldpy._core.epimodels.ModelSEIRMixingQuarantine, vname: str, n: SupportsInt | SupportsIndex, prevalence: SupportsFloat | SupportsIndex, transmission_rate: SupportsFloat | SupportsIndex, avg_incubation_days: SupportsFloat | SupportsIndex, recovery_rate: SupportsFloat | SupportsIndex, contact_matrix: collections.abc.Sequence[SupportsFloat | SupportsIndex], hospitalization_rate: SupportsFloat | SupportsIndex, hospitalization_period: SupportsFloat | SupportsIndex, days_undetected: SupportsFloat | SupportsIndex, quarantine_period: SupportsInt | SupportsIndex, quarantine_willingness: SupportsFloat | SupportsIndex, isolation_willingness: SupportsFloat | SupportsIndex, isolation_period: SupportsInt | SupportsIndex, contact_tracing_success_rate: SupportsFloat | SupportsIndex = 1.0, contact_tracing_days_prior: SupportsInt | SupportsIndex = 4)#

Bases: Model

SEIR model with mixing, quarantine, isolation, hospitalization, and contact tracing.

Infected agents are isolated once detected, and the agents they were in contact with are traced and quarantined.

States: Susceptible, Exposed, Infected, Isolated, Quarantined Susceptible, Quarantined Exposed, Isolated Recovered, Hospitalized, Recovered.

Agents are split into groups with Entity objects (add them with add_entity() before running), and contacts between groups follow contact_matrix. The model creates its n agents itself.

Create a SEIR model with mixing and quarantine.

Parameters:

  • vname (str) – Name of the virus.

  • n (int) – Number of agents.

  • prevalence (float) – Initial proportion of agents with the virus.

  • transmission_rate (float) – Probability of transmission per contact, between 0 and 1.

  • avg_incubation_days (float) – Average number of days in the exposed state; greater than 0.

  • recovery_rate (float) – Daily probability of recovery, between 0 and 1.

  • contact_matrix (list of float) – Flattened G x G matrix, where G is the number of entities; entry (i, j) is the expected number of daily contacts an agent in group i has with agents in group j, so the row sums are the expected contacts per day of each group. The matrix is stored by columns: flatten a NumPy array with m.flatten(order="F").

  • hospitalization_rate (float) – Daily probability that an infected agent is hospitalized.

  • hospitalization_period (float) – Average number of days in the hospital.

  • days_undetected (float) – Average number of days an infection goes undetected.

  • quarantine_period (int) – Number of days in quarantine. A negative value turns quarantine off.

  • quarantine_willingness (float) – Proportion of agents willing to quarantine.

  • isolation_willingness (float) – Proportion of agents willing to isolate.

  • isolation_period (int) – Number of days in isolation. A negative value turns isolation off.

  • contact_tracing_success_rate (float, default 1.0) – Probability that a contact is traced.

  • contact_tracing_days_prior (int, default 4) – Number of days before detection during which contacts are traced.