from __future__ import annotations
import numpy as np
import pandas as pd
from covsirphy.util.validator import Validator
from covsirphy.dynamics.sird import SIRDModel
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class SIRFModel(SIRDModel):
"""Class of SIR-F model.
Args:
date_range: start date and end date of simulation
tau: tau value [min]
initial_dict: initial values
- Susceptible (int): the number of susceptible cases
- Infected (int): the number of infected cases
- Fatal (int): the number of fatal cases
- Recovered (int): the number of recovered cases
param_dict: non-dimensional parameter values
- theta: direct fatality probability of un-categorized confirmed cases
- kappa: non-dimensional mortality rate of infected cases
- rho: non-dimensional effective contact rate
- sigma: non-dimensional recovery rate
Note:
SIR-F model is original to Covsirphy, https://www.kaggle.com/code/lisphilar/covid-19-data-with-sir-model/notebook
"""
# Name of ODE model
_NAME = "SIR-F Model"
# Non-dimensional parameters
_PARAMETERS = ["theta", "kappa", "rho", "sigma"]
# Dimensional parameters
_DAY_PARAMETERS = ["alpha1 [-]", "1/alpha2 [day]", "1/beta [day]", "1/gamma [day]"]
# Sample data
_SAMPLE_DICT = {
"initial_dict": {SIRDModel.S: 999_000, SIRDModel.CI: 1000, SIRDModel.R: 0, SIRDModel.F: 0},
"param_dict": {"theta": 0.002, "kappa": 0.005, "rho": 0.2, "sigma": 0.075}
}
def __init__(self, date_range: tuple[str, str], tau: int, initial_dict: dict[str, int], param_dict: dict[str, float]) -> None:
super().__init__(date_range, tau, initial_dict, param_dict)
self._theta = Validator(self._param_dict["theta"], "theta", accept_none=False).float(value_range=(0, 1))
def _discretize(self, t: int, X: np.ndarray) -> np.ndarray:
"""Discretize the ODE.
Args:
t: discrete time-steps
X: the current values of the model
Returns:
numpy.array: the next values of the model
"""
n = self._population
s, i, *_ = X
dsdt = 0 - self._rho * s * i / n
drdt = self._sigma * i
dfdt = self._kappa * i + (0 - dsdt) * self._theta
didt = 0 - dsdt - drdt - dfdt
return np.array([dsdt, didt, drdt, dfdt])
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def r0(self) -> float:
"""Calculate basic reproduction number.
Raises:
ZeroDivisionError: sigma + kappa value was over 0
Returns:
reproduction number of the ODE model and parameters
"""
try:
return round(self._rho * (1 - self._theta) / (self._sigma + self._kappa), 2)
except ZeroDivisionError:
raise ZeroDivisionError(
f"Sigma + kappa must be over 0 to calculate reproduction number with {self._NAME}.") from None
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def dimensional_parameters(self) -> dict[str, float | int]:
"""Calculate dimensional parameter values.
Raises:
ZeroDivisionError: either kappa or rho or sigma value was over 0
Returns:
dictionary of dimensional parameter values
- "alpha1 [-]" (float): direct fatality probability of un-categorized confirmed cases
- "1/alpha2 [day]" (int): mortality period of infected cases
- "1/beta [day]" (int): infection period
- "1/gamma [day]" (int): recovery period
"""
try:
return {
"alpha1 [-]": round(self._theta, 3),
"1/alpha2 [day]": round(self._tau / 24 / 60 / self._kappa),
"1/beta [day]": round(self._tau / 24 / 60 / self._rho),
"1/gamma [day]": round(self._tau / 24 / 60 / self._sigma)
}
except ZeroDivisionError:
raise ZeroDivisionError(
f"Kappa, rho and sigma must be over 0 to calculate dimensional parameters with {self._NAME}.") from None
@classmethod
def _param_quantile(cls, data: pd.DataFrame, q: float | pd.Series = 0.5) -> dict[str, float | pd.Series]:
"""With combinations (X, dX/dt) for X=S, I, R, F, calculate quantile values of ODE parameters.
Args:
data: transformed data with covsirphy.SIRFModel.transform(data=data, tau=tau)
q: the quantile(s) to compute, value(s) between (0, 1)
Returns:
parameter values at the quantile(s)
Note:
We can get approximate parameter values with difference equations as follows.
- theta -> +0 (i.e. around 0 and not negative)
- kappa -> (dF/dt) / I when theta -> +0
- rho = - n * (dS/dt) / S / I
- sigma = (dR/dt) / I
"""
df = data.copy()
periods = round((df.index.max() - df.index.min()) / len(df))
# Remove negative values and set variables
df = df.loc[(df[cls.S] > 0) & (df[cls.CI] > 0)]
n = df.loc[df.index[0], cls._VARIABLES].sum()
# Calculate parameter values with non-dimensional difference equation
kappa_series = df[cls.F].diff() / periods / df[cls.CI]
rho_series = 0 - n * df[cls.S].diff() / periods / df[cls.S] / df[cls.CI]
sigma_series = df[cls.R].diff() / periods / df[cls.CI]
# Guess representative values
return {
"theta": 0.0 if isinstance(q, float) else pd.Series([0.0, 0.5]).repeat([1, len(q) - 1]),
"kappa": cls._clip(kappa_series.quantile(q=q), 0, 1),
"rho": cls._clip(rho_series.quantile(q=q), 0, 1),
"sigma": cls._clip(sigma_series.quantile(q=q), 0, 1),
}
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@classmethod
def sr(cls, data: pd.DataFrame) -> pd.DataFrame:
"""Return log10(S) and R of model-specific variables for S-R trend analysis.
Args:
data:
Index
Date (pd.Timestamp): Observation date
Columns
Susceptible (int): the number of susceptible cases
Infected (int): the number of currently infected cases
Recovered (int): the number of recovered cases
Fatal (int): the number of fatal cases
Returns:
Index
Date (pandas.Timestamp): date
Columns
log10(S) (np.float64): common logarithm of Susceptible
R (np.int64): Recovered
"""
Validator(data, "data", accept_none=False).dataframe(time_index=True, columns=cls._SIRF)
df = data.rename(columns={cls.R: cls._r})
df[cls._logS] = np.log10(df[cls.S])
return df.loc[:, [cls._logS, cls._r]].astype({cls._logS: np.float64, cls._r: np.int64})