def _get_model_data(state='NC', type='SIR', sds=0):
"""
Get model output data
:param state: name of the state to return data for
:param type: 'SIR' or 'Hospital Use' or 'Hospital Census'
:param sds: social distance rate within [0, 1] with 0 meaning no social distancing and 1 meaning maximal
social distancing
:return:
"""
n = states[state]
census = pd.read_csv("data/census.csv")
pops = census[["NAME", "POPESTIMATE2019"]]
nms = pops["NAME"]
t_recovery = 23
conf, dead, rec = (pd.DataFrame.from_dict(get_state_level(x)).drop(
["Lat", "Long", "Country/Region"], axis=1) for x in get_hopkins())
# the zeros mess up our slope
state_conf = conf[conf['Province/State'] == n]
data = state_conf.drop("Province/State", axis=1)
data = data.iloc[0]
data = data.loc[data != 0]
state_growth = get_slope(data)
td = doubling_time(state_growth)
new_names = {i: v for i, v in enumerate(conf["Province/State"])}
c_tot, d_tot, r_tot = (x.sum(1).rename(new_names)
for x in [conf, dead, rec])
state_curve = {}
N = pops[pops["NAME"] == n]["POPESTIMATE2019"].values[0]
I = c_tot[n]
R = r_tot[n]
D = d_tot[n]
model = PennDeath(N,
I,
R,
D,
0,
contact_reduction=sds,
t_double=td,
recover_time=t_recovery)
curve, occ = model.sir(60)
sir = {
k: v
for k, v in curve.items()
if k in ["susceptible", "infected", "recovered", "dead"]
}
hosp_use = {
k: v
for k, v in curve.items()
if k not in ["susceptible", "infected", "recovered", "dead"]
}
state_curve = {
"SIR": sir,
"Hospital Use": hosp_use,
"Hospital Census": occ
}
return state_curve
def plot_penn(Pdp: PennDeath, n_days: int) -> None:
curve, admissions = Pdp.sir(n_days)
fig, ax = plt.subplots(1, 3, figsize=(15, 5))
for k, v in curve.items():
if k not in tx.rates.keys():
ax[0].plot(v, label=k)
ax[0].legend()
else:
ax[1].plot(v, label=k)
ax[1].legend()
ax[1].set_title("Hospital Resource Usage")
ax[0].set_title("SIR curve")
for k, v in admissions.items():
ax[2].plot(v, label=k)
ax[2].legend()
ax[2].set_title("Additional Resource Usage by day")
fig.suptitle(f"No social distancing, total deaths = {int(max(curve['dead']))}")
plt.show()
d_tot["United States"] = d_tot.sum()
r_tot["United States"] = r_tot.sum()
pops[pops["NAME"] == "United States"]["POPESTIMATE2019"].values[0]
out = {}
sds = [0, 0.2, 0.5, 0.7]
for n in names:
state_curve = {}
for s in sds:
N = pops[pops["NAME"] == n]["POPESTIMATE2019"].values[0]
I = c_tot[n]
R = r_tot[n]
D = r_tot[n]
td = d_times[n]
model = PennDeath(N, I, R, D, 0, t_double=td, recover_time=t_recovery)
curve, occ = model.sir(60)
sir = {
k: v
for k, v in curve.items()
if k in ["susceptible", "infected", "recovered"]
}
hosp_use = {
k: v
for k, v in curve.items()
if k not in ["susceptible", "infected", "recovered"]
}
state_curve[s] = {
"SIR": sir,
"Hospital Use": hosp_use,
"Hospital Census": occ
# Third party modules
import matplotlib
import matplotlib.pyplot as plt
# First party modules
from comodels import PennDeath
matplotlib.use("tkagg")
plt.style.use("seaborn-paper")
tx = PennDeath(
330460832, 14250, 125, 205, 57, death_rate=0.01, birth_rate=0, contact_reduction=0.0
)
def plot_penn(Pdp: PennDeath, n_days: int) -> None:
curve, admissions = Pdp.sir(n_days)
fig, ax = plt.subplots(1, 3, figsize=(15, 5))
for k, v in curve.items():
if k not in tx.rates.keys():
ax[0].plot(v, label=k)
ax[0].legend()
else:
ax[1].plot(v, label=k)
ax[1].legend()
ax[1].set_title("Hospital Resource Usage")
ax[0].set_title("SIR curve")
for k, v in admissions.items():
ax[2].plot(v, label=k)
ax[2].legend()
ax[2].set_title("Additional Resource Usage by day")
r_tot["United States"] = r_tot.sum()
pops[pops["NAME"] == "United States"]["POPESTIMATE2019"].values[0]
out = {}
sds = list(np.round(np.arange(0, 1, 0.1), decimals=1))
for n in names:
state_curve = {}
for s in sds:
N = pops[pops["NAME"] == n]["POPESTIMATE2019"].values[0]
I = c_tot[n]
R = r_tot[n]
D = r_tot[n]
td = d_times[n]
model = PennDeath(N,
I,
R,
D,
0,
t_double=td,
recover_time=t_recovery,
contact_reduction=s)
curve, occ = model.sir(60)
sir = {
k: v
for k, v in curve.items()
if k in ["susceptible", "infected", "recovered"]
}
hosp_use = {
k: v
for k, v in curve.items()
if k not in ["susceptible", "infected", "recovered"]
}
state_curve[s] = {
import comodels
help(comodels)
help(comodels.PennDeath)
help(comodels.Penn)
# import the penn model
import matplotlib.pyplot as plt
from comodels import PennDeath
help(PennDeath)
tx = PennDeath(N=28304596, I=223, R=0, D=3, D_today=2)
help(PennDeath.sir)
def plot_penn(Pdp: PennDeath, n_days: int) -> None:
# predict the coming storm and plot it
curve, admissions = Pdp.sir(n_days)
fig, ax = plt.subplots(1, 3, figsize=(15, 5))
for k, v in curve.items():
if k not in Pdp.rates.keys():
ax[0].plot(v, label=k)
ax[0].legend()
else:
ax[1].plot(v, label=k)
ax[1].legend()
ax[1].set_title('Hospital Resource Usage')
ax[0].set_title('SIR curve')
for k, v in admissions.items():
ax[2].plot(v, label=k)
ax[2].legend()