HIV

State

class whynot.simulators.hiv.State

State of the HIV simulator.

The default state corresponds to an early infection state, defined by Adams et al. The early infection state is designed based on an unstable uninfected steady state by 1) adding one virus particle per ml of blood plasma, and 2) adding low levels of infected T-cells.

free_virus = 1

Free virus (copies/ml)

immune_response = 10

Immune response CTL E (cells/ml)

infected_T1 = 0.0001

Infected CD4+ T-lymphocytes (cells/ml)

infected_T2 = 0.0001

Infected macrophages (cells/ml)

uninfected_T1 = 1000000.0

Uninfected CD4+ T-lymphocytes (cells/ml)

uninfected_T2 = 3198

Uninfected macrophages (cells/ml)

Config

class whynot.simulators.hiv.Config

Parameters for the simulation dynamics.

Examples

# Run the simulation for 200 days with infected cell death rate 0.3 hiv.Config(duration=200, delta=0.3)

K_B = 100

Saturation constant for immune effector birth

K_D = 500

Saturation constant for immune effector death

N_T = 100

Virions produced per infected cell

atol = 1e-06

solver absolute tolerance

b_E = 0.3

Maximum birth rate for immune effectors

c = 13

Virus natural death rate

d_1 = 0.01

Target cell type 1 death rate

d_2 = 0.01

Target cell type 2 death rate

d_E = 0.25

Maximum death rate for immune effectors

delta = 0.7

Infected cell death rate

delta_E = 0.1

Natural death rate for immune effectors

delta_t = 0.05

How frequently to measure simulator state

end_time = 400

Simulation end time (in days)

epsilon_1 = 0.0

Drug efficacy

epsilon_2 = 0.0

Efficacy of protease inhibitors

f = 0.34

Treatment efficacy reduction in population 2

k_1 = 8e-07

Population 1 infection rate

k_2 = 0.0001

Population 2 infection rate

lambda_1 = 10000.0

Target cell type 1 production (source) rate

lambda_2 = 31.98

Target cell type 2 production (source) rate

lambda_E = 1

Immune effector production (source) rate

m_1 = 1e-05

Immune-induced clearance rate for population 1

m_2 = 1e-05

Immune-induced clearance rate for population 2

rho_1 = 1

Average number virions infecting a type 1 cell

rho_2 = 1

Average number virions infecting a type 2 cell

rtol = 1e-06

solver relative tolerance

start_time = 0

Simulation start time (in day)

Interventions

class whynot.simulators.hiv.Intervention(time=100, **kwargs)

Parameterization of an intervention in the HIV model.

Examples

>>> # Starting in step 100, set epsilon_1 to 0.7 (leaving other variables unchanged)
>>> Intervention(time=100, epsilon_1=0.7)

__init__(time=100, **kwargs)

Specify an intervention in the dynamical system.

Parameters:

• time (int) – Time of the intervention (days)
• kwargs (dict) – Only valid keyword arguments are parameters of Config.

Experiments

Adams et al. HIV simulator initialization.