Module epispot.pre
This module (short for 'precompiled') contains models that have already been compiled and can be put to use immediately.
Each function returns an Model
object and its corresponding methods.
Model parameters can still be changed even after compilation, but this will require recompilation which can be performed with:
Model.compile(custom=False)
# if adding custom compartments:
Model.compile(custom=True)
Expand source code
"""
This module (short for 'precompiled') contains models that have already been compiled and can be put to use immediately.
Each function returns an `epispot.models.Model` object and its corresponding methods.
Model parameters can still be changed even after compilation, but this will require recompilation which can be performed with:
```python
Model.compile(custom=False)
# if adding custom compartments:
Model.compile(custom=True)
```
"""
from . import comps, models, np
def sir(r_0, gamma, n):
"""
The wellknown
[SIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model);
a staple of epidemiology and the most basic tool for modeling
infectious diseases.
Susceptible → Infected → Removed
## Parameters
`r_0 (floatfunc(t: float)>float)`: The
[basic reproduction number](https://en.wikipedia.org/wiki/Basic_reproduction_number),
indicating how infectious a given disease is.
A value of above 1 indicates a high probability of transmission and thus an increasing infected population.
A value of 1 indicates a low probability of transmission and thus a constant infected population.
A value below 1 indicates a low probability of transmission and also a decreasing infected population.
`gamma (floatfunc(t: float)>float)`: The total recovery rate of patients.
This is *not* a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness.
`n`: The initial population size; should be the same as that passed into the `epispot.models.Model` class.
## Returns
A `epispot.models.Model` object
"""
# compile compartments
susceptible = comps.Susceptible(r_0, gamma, n)
infected = comps.Infected()
removed = comps.Removed()
# compile parameters
if callable(n):
n = n(0)
matrix = np.empty((3, 3), dtype=tuple)
matrix.fill((1.0, 1.0)) # default probability and rate
matrix[1][2] = (1.0, gamma) # I => R
# compile model
sir_model = models.Model(n)
sir_model.add(susceptible, [1], matrix[0])
sir_model.add(infected, [2], matrix[1])
sir_model.add(removed, [], matrix[2])
sir_model.compile()
return sir_model
def seir(r_0, gamma, n, delta):
"""
An extension on the basic
[SIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model)
to include an 'exposed' compartment useful for modeling contact tracing.
This is known as the
[SEIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SEIR_model)
and is commonly used for diseases that have long incubation periods.
Susceptible → Exposed → Infected → Removed
## Parameters
`r_0 (floatfunc(t: float)>float)`: The
[basic reproduction number](https://en.wikipedia.org/wiki/Basic_reproduction_number),
indicating how infectious a given disease is.
A value of above 1 indicates a high probability of transmission and thus an increasing infected population.
A value of 1 indicates a low probability of transmission and thus a constant infected population.
A value below 1 indicates a low probability of transmission and also a decreasing infected population.
`gamma (floatfunc(t: float)>float)`: The total recovery rate of patients.
This is *not* a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness.
`n (floatfunc(t: float)>float)`: The initial population size;
should be the same as that passed into the `epispot.models.Model` class.
`delta (floatfunc(t: float)>float)`: The reciprocal of the incubation period for the disease.
This is one divided by the time it takes an individual to transition from the `epispot.comps.Exposed` compartment to the `epispot.comps.Infected` compartment.
## Returns
An `epispot.models.Model` object
"""
# compile compartments
susceptible = comps.Susceptible(r_0, gamma, n)
exposed = comps.Exposed()
infected = comps.Infected()
removed = comps.Removed()
# compile parameters
if callable(n):
n = n(0)
matrix = np.empty((4, 4), dtype=tuple)
matrix.fill((1.0, 1.0)) # default probability and rate
matrix[1][2] = (1.0, delta) # E => I
matrix[2][3] = (1.0, gamma) # I => R
# compile model
seir_model = models.Model(n)
seir_model.add(susceptible, [1], matrix[0])
seir_model.add(exposed, [2], matrix[1])
seir_model.add(infected, [3], matrix[2])
seir_model.add(removed, [], matrix[3])
seir_model.compile()
return seir_model
def sird(r_0, gamma, n, alpha, rho=1.0):
"""
An addition to the
[SIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model),
which separates the `epispot.comps.Removed` compartment into the `epispot.comps.Recovered` compartment and the `epispot.comps.Dead` compartment.
Susceptible → Infected → Recovered, Dead
## Parameters
`r_0 (floatfunc(t: float)>float)`: The
[basic reproduction number](https://en.wikipedia.org/wiki/Basic_reproduction_number),
indicating how infectious a given disease is.
A value of above 1 indicates a high probability of transmission and thus an increasing infected population.
A value of 1 indicates a low probability of transmission and thus a constant infected population.
A value below 1 indicates a low probability of transmission and also a decreasing infected population.
`gamma (floatfunc(t: float)>float)`: The total recovery rate of patients.
This is *not* a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness.
`n (floatfunc(t: float)>float)`: The initial population size;
should be the same as that passed into the `epispot.models.Model` class.
`alpha (floatfunc(t: float)>float)`: The probability of death
(often referred to as—but not to be confused with—the 'death rate')
for a certain disease.
This is the percentage of individuals that are infected with the disease that will die.
`rho=1.0 (floatfunc(t: float)>float)`: The reciprocal of the average time before death.
This is the real 'death *rate*' of the disease, which represents the *time* of death rather than its probability.
## Returns
A `epispot.models.Model` object
"""
# compile compartments
susceptible = comps.Susceptible(r_0, gamma, n)
infected = comps.Infected()
recovered = comps.Recovered()
dead = comps.Dead()
# compile parameters
if callable(n):
n = n(0)
matrix = np.empty((4, 4), dtype=tuple)
matrix.fill((1.0, 1.0)) # default probability and rate
recovery_rate = (gamma  alpha * rho) / (1  alpha)
matrix[1][2] = (1.0  alpha, recovery_rate) # I => R
matrix[1][3] = (alpha, rho) # I => D
# compile model
sir_model = models.Model(n)
sir_model.add(susceptible, [1], matrix[0])
sir_model.add(infected, [2, 3], matrix[1])
sir_model.add(recovered, [], matrix[2])
sir_model.add(dead, [], matrix[3])
sir_model.compile()
return sir_model
Functions
def seir(r_0, gamma, n, delta)

An extension on the basic SIR Model to include an 'exposed' compartment useful for modeling contact tracing. This is known as the SEIR Model and is commonly used for diseases that have long incubation periods.
Susceptible → Exposed → Infected → Removed
Parameters
r_0 (floatfunc(t: float)>float)
: The basic reproduction number, indicating how infectious a given disease is. A value of above 1 indicates a high probability of transmission and thus an increasing infected population. A value of 1 indicates a low probability of transmission and thus a constant infected population. A value below 1 indicates a low probability of transmission and also a decreasing infected population.gamma (floatfunc(t: float)>float)
: The total recovery rate of patients. This is not a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness.n (floatfunc(t: float)>float)
: The initial population size; should be the same as that passed into theModel
class.delta (floatfunc(t: float)>float)
: The reciprocal of the incubation period for the disease. This is one divided by the time it takes an individual to transition from theExposed
compartment to theInfected
compartment.Returns
An
Model
objectExpand source code
def seir(r_0, gamma, n, delta): """ An extension on the basic [SIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model) to include an 'exposed' compartment useful for modeling contact tracing. This is known as the [SEIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SEIR_model) and is commonly used for diseases that have long incubation periods. Susceptible → Exposed → Infected → Removed ## Parameters `r_0 (floatfunc(t: float)>float)`: The [basic reproduction number](https://en.wikipedia.org/wiki/Basic_reproduction_number), indicating how infectious a given disease is. A value of above 1 indicates a high probability of transmission and thus an increasing infected population. A value of 1 indicates a low probability of transmission and thus a constant infected population. A value below 1 indicates a low probability of transmission and also a decreasing infected population. `gamma (floatfunc(t: float)>float)`: The total recovery rate of patients. This is *not* a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness. `n (floatfunc(t: float)>float)`: The initial population size; should be the same as that passed into the `epispot.models.Model` class. `delta (floatfunc(t: float)>float)`: The reciprocal of the incubation period for the disease. This is one divided by the time it takes an individual to transition from the `epispot.comps.Exposed` compartment to the `epispot.comps.Infected` compartment. ## Returns An `epispot.models.Model` object """ # compile compartments susceptible = comps.Susceptible(r_0, gamma, n) exposed = comps.Exposed() infected = comps.Infected() removed = comps.Removed() # compile parameters if callable(n): n = n(0) matrix = np.empty((4, 4), dtype=tuple) matrix.fill((1.0, 1.0)) # default probability and rate matrix[1][2] = (1.0, delta) # E => I matrix[2][3] = (1.0, gamma) # I => R # compile model seir_model = models.Model(n) seir_model.add(susceptible, [1], matrix[0]) seir_model.add(exposed, [2], matrix[1]) seir_model.add(infected, [3], matrix[2]) seir_model.add(removed, [], matrix[3]) seir_model.compile() return seir_model
def sir(r_0, gamma, n)

The wellknown SIR Model; a staple of epidemiology and the most basic tool for modeling infectious diseases.
Susceptible → Infected → Removed
Parameters
r_0 (floatfunc(t: float)>float)
: The basic reproduction number, indicating how infectious a given disease is. A value of above 1 indicates a high probability of transmission and thus an increasing infected population. A value of 1 indicates a low probability of transmission and thus a constant infected population. A value below 1 indicates a low probability of transmission and also a decreasing infected population.gamma (floatfunc(t: float)>float)
: The total recovery rate of patients. This is not a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness.n
: The initial population size; should be the same as that passed into theModel
class.Returns
A
Model
objectExpand source code
def sir(r_0, gamma, n): """ The wellknown [SIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model); a staple of epidemiology and the most basic tool for modeling infectious diseases. Susceptible → Infected → Removed ## Parameters `r_0 (floatfunc(t: float)>float)`: The [basic reproduction number](https://en.wikipedia.org/wiki/Basic_reproduction_number), indicating how infectious a given disease is. A value of above 1 indicates a high probability of transmission and thus an increasing infected population. A value of 1 indicates a low probability of transmission and thus a constant infected population. A value below 1 indicates a low probability of transmission and also a decreasing infected population. `gamma (floatfunc(t: float)>float)`: The total recovery rate of patients. This is *not* a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness. `n`: The initial population size; should be the same as that passed into the `epispot.models.Model` class. ## Returns A `epispot.models.Model` object """ # compile compartments susceptible = comps.Susceptible(r_0, gamma, n) infected = comps.Infected() removed = comps.Removed() # compile parameters if callable(n): n = n(0) matrix = np.empty((3, 3), dtype=tuple) matrix.fill((1.0, 1.0)) # default probability and rate matrix[1][2] = (1.0, gamma) # I => R # compile model sir_model = models.Model(n) sir_model.add(susceptible, [1], matrix[0]) sir_model.add(infected, [2], matrix[1]) sir_model.add(removed, [], matrix[2]) sir_model.compile() return sir_model
def sird(r_0, gamma, n, alpha, rho=1.0)

An addition to the SIR Model, which separates the
Removed
compartment into theRecovered
compartment and theDead
compartment.Susceptible → Infected → Recovered, Dead
Parameters
r_0 (floatfunc(t: float)>float)
: The basic reproduction number, indicating how infectious a given disease is. A value of above 1 indicates a high probability of transmission and thus an increasing infected population. A value of 1 indicates a low probability of transmission and thus a constant infected population. A value below 1 indicates a low probability of transmission and also a decreasing infected population.gamma (floatfunc(t: float)>float)
: The total recovery rate of patients. This is not a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness.n (floatfunc(t: float)>float)
: The initial population size; should be the same as that passed into theModel
class.alpha (floatfunc(t: float)>float)
: The probability of death (often referred to as—but not to be confused with—the 'death rate') for a certain disease. This is the percentage of individuals that are infected with the disease that will die.rho=1.0 (floatfunc(t: float)>float)
: The reciprocal of the average time before death. This is the real 'death rate' of the disease, which represents the time of death rather than its probability.Returns
A
Model
objectExpand source code
def sird(r_0, gamma, n, alpha, rho=1.0): """ An addition to the [SIR Model](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SIR_model), which separates the `epispot.comps.Removed` compartment into the `epispot.comps.Recovered` compartment and the `epispot.comps.Dead` compartment. Susceptible → Infected → Recovered, Dead ## Parameters `r_0 (floatfunc(t: float)>float)`: The [basic reproduction number](https://en.wikipedia.org/wiki/Basic_reproduction_number), indicating how infectious a given disease is. A value of above 1 indicates a high probability of transmission and thus an increasing infected population. A value of 1 indicates a low probability of transmission and thus a constant infected population. A value below 1 indicates a low probability of transmission and also a decreasing infected population. `gamma (floatfunc(t: float)>float)`: The total recovery rate of patients. This is *not* a measure of how long it takes patients in any given compartment to recover but rather a measure of one divided by the average time of infectiousness. `n (floatfunc(t: float)>float)`: The initial population size; should be the same as that passed into the `epispot.models.Model` class. `alpha (floatfunc(t: float)>float)`: The probability of death (often referred to as—but not to be confused with—the 'death rate') for a certain disease. This is the percentage of individuals that are infected with the disease that will die. `rho=1.0 (floatfunc(t: float)>float)`: The reciprocal of the average time before death. This is the real 'death *rate*' of the disease, which represents the *time* of death rather than its probability. ## Returns A `epispot.models.Model` object """ # compile compartments susceptible = comps.Susceptible(r_0, gamma, n) infected = comps.Infected() recovered = comps.Recovered() dead = comps.Dead() # compile parameters if callable(n): n = n(0) matrix = np.empty((4, 4), dtype=tuple) matrix.fill((1.0, 1.0)) # default probability and rate recovery_rate = (gamma  alpha * rho) / (1  alpha) matrix[1][2] = (1.0  alpha, recovery_rate) # I => R matrix[1][3] = (alpha, rho) # I => D # compile model sir_model = models.Model(n) sir_model.add(susceptible, [1], matrix[0]) sir_model.add(infected, [2, 3], matrix[1]) sir_model.add(recovered, [], matrix[2]) sir_model.add(dead, [], matrix[3]) sir_model.compile() return sir_model