min_value=0.01,
max_value=10.0,
step=0.25,
value=defaults['r0_dist'][1])
return (r0_inf, r0_sup, .95, 'lognorm')
if __name__ == '__main__':
st.markdown(texts.INTRODUCTION)
st.sidebar.markdown(texts.PARAMETER_SELECTION)
w_granularity = st.sidebar.selectbox('Unidade',
options=['state', 'city'],
index=1,
format_func=global_format_func)
cases_df = data.load_cases(w_granularity)
population_df = data.load_population(w_granularity)
DEFAULT_PLACE = (DEFAULT_CITY
if w_granularity == 'city' else DEFAULT_STATE)
options_place = make_place_options(cases_df, population_df)
w_place = st.sidebar.selectbox('Município',
options=options_place,
index=options_place.get_loc(DEFAULT_PLACE),
format_func=global_format_func)
options_date = make_date_options(cases_df, w_place)
w_date = st.sidebar.selectbox('Data',
options=options_date,
index=len(options_date) - 1)
def write():
st.markdown("## Modelo Epidemiológico (SEIR-Bayes)")
st.markdown(texts.INTRO_MODELO)
st.sidebar.markdown("---")
st.sidebar.markdown(texts.PARAMETER_SELECTION)
w_granularity = st.sidebar.selectbox('Unidade',
options=['country', 'state'],
index=1,
format_func=global_format_func)
cases_df = data.load_cases(w_granularity, 'fiocruz')
population_df = data.load_population(w_granularity)
srag_death_subnotification = data.load_srag_death_subnotification()
vulnerable_population = data.load_vulnerable_population()
DEFAULT_PLACE = (DEFAULT_STATE
if w_granularity == 'state' else DEFAULT_COUNTRY)
options_place = make_place_options(cases_df, population_df, w_granularity)
w_place = st.sidebar.selectbox(global_format_func(w_granularity),
options=options_place,
index=options_place.get_loc(DEFAULT_PLACE),
format_func=global_format_func)
try:
raw_widget_values = (pd.read_csv('data/params.csv').set_index(
'place').T.to_dict()[w_place])
widget_values = {
k: v
for k, v in raw_widget_values.items() if not np.isnan(v)
}
except:
widget_values = {}
if widget_values:
st.markdown("---")
st.markdown("### Parâmetros carregados")
st.write(
f"Foram carregados parâmetros pré-selecionados para a unidade escolhida: **{w_place}**."
)
st.write(' \n'.join(f"{param}: {value}"
for param, value in widget_values.items()))
options_date = make_date_options(cases_df, w_place)
w_date = st.sidebar.selectbox('Data inicial',
options=options_date,
index=len(options_date) - 1)
# Configurações da simulação
st.markdown(texts.SIMULATION_CONFIG)
# Estimativa R0
st.markdown(texts.r0_ESTIMATION_TITLE)
should_estimate_r0 = st.checkbox('Estimar R0 a partir de dados históricos',
value=True)
if should_estimate_r0:
r0_samples, used_brazil = estimate_r0(cases_df, w_place, SAMPLE_SIZE,
MIN_DAYS_r0_ESTIMATE, w_date)
if used_brazil:
st.write(texts.r0_NOT_ENOUGH_DATA(w_place, w_date))
_place = 'Brasil' if used_brazil else w_place
st.markdown(texts.r0_ESTIMATION(_place, w_date))
st.altair_chart(
plot_r0(r0_samples, w_date, _place, MIN_DAYS_r0_ESTIMATE))
r0_dist = r0_samples[:, -1]
st.markdown(
f'*O $R_{{0}}$ estimado está entre '
f'${np.quantile(r0_dist, 0.01):.03}$ e ${np.quantile(r0_dist, 0.99):.03}$*'
)
st.markdown(texts.r0_CITATION)
else:
r0_dist = make_r0_widgets(widget_values)
st.markdown(texts.r0_ESTIMATION_DONT)
NEIR0 = make_NEIR0(cases_df, population_df, w_place, w_date,
np.mean(r0_dist))
# Explicação dos parâmetros de óbito e leitos
st.markdown(texts.DESC_PARAMS_DEATHS)
st.markdown(texts.DESC_PARAMS_LEITOS)
# Previsão de infectados
(w_params, lethality_mean_place, lethality_type, death_subr_enable_place,
death_subr_place) = make_param_widgets(NEIR0, widget_values,
vulnerable_population.loc[w_place],
w_place)
# Deaths params
lethality_mean_est, show_leth_age_message = estimate_lethality(
cases_df[w_place]["deaths"], cases_df[w_place]["totalCases"],
w_granularity, w_place, lethality_type)
lethality_mean = make_death_widget(lethality_mean_est,
lethality_mean_place, lethality_type,
widget_values)
death_subnotification = make_death_subr_widget(srag_death_subnotification,
w_place,
death_subr_enable_place,
death_subr_place)
w_params['deaths'] = {
"death_rate": lethality_mean / 100,
"init_deaths": cases_df[w_place]["deaths"][-1]
}
w_params['leitos'] = DERIVATIVES['values']["Leitos"] / 100
#Definições do modelo
model = SEIRBayes(**w_params, r0_dist=r0_dist)
model_output = model.sample(SAMPLE_SIZE)
# Parâmetros de simulação
dists = [w_params['alpha_inv_dist'], w_params['gamma_inv_dist'], r0_dist]
SEIR0 = model._params['init_conditions']
params_intro_txt, seir0_dict, other_params_txt = texts.make_SIMULATION_PARAMS(
SEIR0, dists, should_estimate_r0)
#Outros parâmetros
st.markdown(params_intro_txt)
st.write(pd.DataFrame(seir0_dict).set_index("Compartimento"))
st.markdown(other_params_txt)
ei_df = make_EI_df(model, model_output, SAMPLE_SIZE)
st.markdown(texts.MODEL_INTRO)
w_scale = st.selectbox('Escala do eixo Y', ['log', 'linear'], index=1)
fig = plot_EI(model_output, w_scale, w_date)
st.altair_chart(fig)
download_placeholder = st.empty()
if download_placeholder.button('Preparar dados para download em CSV'):
href = make_download_href(ei_df, w_params, r0_dist, should_estimate_r0)
st.markdown(href, unsafe_allow_html=True)
download_placeholder.empty()
st.markdown(texts.INFECTED_INTRO)
(infected_total_lower_bound, infected_total_upper_bound,
infected_total_mean,
fig_infected) = plot_infected(model_output, 'linear', w_date,
w_params['fator_subr'])
st.altair_chart(fig_infected)
st.markdown(
texts.INFECTED_TOTAL_COUNT(infected_total_lower_bound,
infected_total_mean,
infected_total_upper_bound))
# Plot Deaths
st.markdown(texts.DEATHS_INTRO)
if show_leth_age_message:
st.markdown(
f"**Este estado não apresentou dados de faixa etária."
f" Por isso, são usados dados do Brasil para estimar as taxas de letalidade.**"
)
(deaths_total_lower_bound, deaths_total_upper_bound, deaths_total_mean,
fig_deaths) = plot_deaths(model_output, 'linear', w_date,
lethality_mean * death_subnotification,
w_params['fator_subr'])
st.altair_chart(fig_deaths)
st.markdown(
texts.DEATHS_TOTAL_COUNT(deaths_total_lower_bound, deaths_total_mean,
deaths_total_upper_bound))
st.markdown(texts.DEATH_DETAIL, unsafe_allow_html=True)
st.markdown(texts.LEITOS_INTRO)
derivatives = make_EI_derivatives(ei_df, w_params['fator_subr'])
derivatives_chart = plot_derivatives(derivatives, w_date)
st.altair_chart(derivatives_chart)
st.markdown(texts.LEITOS_DETAIL, unsafe_allow_html=True)
# Fontes dos dados
st.markdown(texts.DATA_SOURCES)
def generateR0DistributionByDate(r0_samples, date, place, min_days):
r0_samples_cut = r0_samples[-min_days:]
columns = pd.date_range(end=date, periods=r0_samples_cut.shape[1])
data = (pd.DataFrame(
r0_samples_cut,
columns=columns).stack(level=0).reset_index().rename(columns={
"level_1": "Dias",
0: "r0"
})[["Dias", "r0"]])
data = data.groupby('Dias').mean()
return data
cases_df = data.load_cases('state', 'fiocruz')
cases_df.head()
cases_df.info()
SAMPLE_SIZE = 500
MIN_CASES_TH = 10
MIN_DAYS_r0_ESTIMATE = 14
EndDate = '2021-02-05'
Estado = 'RJ'
r0_samples = estimate_r0(
cases_df,
Estado, # estado da estimativa
SAMPLE_SIZE, # dias da estimativa
MIN_DAYS_r0_ESTIMATE, #
EndDate)
data = generateR0DistributionByDate(r0_samples, EndDate, Estado,
def write():
st.markdown("## Modelo Epidemiológico (SEIR-Bayes)")
st.sidebar.markdown(texts.PARAMETER_SELECTION)
w_granularity = st.sidebar.selectbox('Unidade',
options=['state', 'city'],
index=1,
format_func=global_format_func)
source = 'ms' if w_granularity == 'state' else 'wcota'
cases_df = data.load_cases(w_granularity, source)
population_df = data.load_population(w_granularity)
DEFAULT_PLACE = (DEFAULT_CITY
if w_granularity == 'city' else DEFAULT_STATE)
options_place = make_place_options(cases_df, population_df)
w_place = st.sidebar.selectbox('Município',
options=options_place,
index=options_place.get_loc(DEFAULT_PLACE),
format_func=global_format_func)
options_date = make_date_options(cases_df, w_place)
w_date = st.sidebar.selectbox('Data inicial',
options=options_date,
index=len(options_date) - 1)
NEIR0 = make_NEIR0(cases_df, population_df, w_place, w_date)
# Estimativa R0
st.markdown(texts.r0_ESTIMATION_TITLE)
should_estimate_r0 = st.checkbox('Estimar R0 a partir de dados históricos',
value=True)
if should_estimate_r0:
r0_samples, used_brazil = estimate_r0(cases_df, w_place, SAMPLE_SIZE,
MIN_DAYS_r0_ESTIMATE, w_date)
if used_brazil:
st.write(texts.r0_NOT_ENOUGH_DATA(w_place, w_date))
_place = 'Brasil' if used_brazil else w_place
st.markdown(texts.r0_ESTIMATION(_place, w_date))
st.altair_chart(
plot_r0(r0_samples, w_date, _place, MIN_DAYS_r0_ESTIMATE))
r0_dist = r0_samples[:, -1]
st.markdown(
f'*O $R_{{0}}$ estimado está entre '
f'${np.quantile(r0_dist, 0.01):.03}$ e ${np.quantile(r0_dist, 0.99):.03}$*'
)
st.markdown(texts.r0_CITATION)
else:
r0_dist = make_r0_widgets()
st.markdown(texts.r0_ESTIMATION_DONT)
# Previsão de infectados
w_params = make_param_widgets(NEIR0)
model = SEIRBayes(**w_params, r0_dist=r0_dist)
model_output = model.sample(SAMPLE_SIZE)
ei_df = make_EI_df(model, model_output, SAMPLE_SIZE)
st.markdown(texts.MODEL_INTRO)
w_scale = st.selectbox('Escala do eixo Y', ['log', 'linear'], index=1)
fig = plot_EI(model_output, w_scale, w_date)
st.altair_chart(fig)
download_placeholder = st.empty()
if download_placeholder.button('Preparar dados para download em CSV'):
href = make_download_href(ei_df, w_params, r0_dist, should_estimate_r0)
st.markdown(href, unsafe_allow_html=True)
download_placeholder.empty()
# Parâmetros de simulação
dists = [w_params['alpha_inv_dist'], w_params['gamma_inv_dist'], r0_dist]
SEIR0 = model._params['init_conditions']
params_intro_txt, seir0_dict, other_params_txt = texts.make_SIMULATION_PARAMS(
SEIR0, dists, should_estimate_r0)
st.markdown(params_intro_txt)
st.write(pd.DataFrame(seir0_dict).set_index("Compartimento"))
st.markdown(other_params_txt)
# Configurações da simulação
st.markdown(texts.SIMULATION_CONFIG)
# Fontes dos dados
st.markdown(texts.DATA_SOURCES)
def generate_seir(
# Municipio
city,
date,
# Condições iniciais
S0=None, # População total (N)
I0=None, # Indivíduos infecciosos inicialmente (I0)
E0=None, # Indivíduos expostos inicialmente (E0)
R0=0, # Indivíduos removidos com imunidade inicialmente (R0)
# R0, período de infecção (1/γ) e tempo incubação (1/α)
r0_inf=1.9, # Limite inferior do número básico de reprodução médio (R0)
r0_sup=5, # Limite superior do número básico de reprodução médio (R0)
reduce_r0=[0, 0.25, 0.50, 0.65],
gamma_inf=10, # Limite inferior do período infeccioso médio em dias (1/γ)
gamma_sup=14, # Limite superior do período infeccioso médio em dias (1/γ)
alpha_inf=4.2, # Limite inferior do tempo de incubação médio em dias (1/α)
alpha_sup=5, # Limite superior do tempo de incubação médio em dias (1/α)
# Parâmetros gerais
t_max=180, # Período de simulação em dias (t_max)
sample_size=1000 #Qtde. de iterações da simulação (runs)
):
cases = load_cases('city')[city]
population = load_population('city')[city]
S0 = S0 or population
I0 = I0 or cases.loc[date]['totalCases']
E0 = E0 or 2 * I0
R0 = R0 or 0
sample_size = sample_size or 1000
t_max = t_max or 180
for reduce_by in reduce_r0:
model = SEIRBayes.init_from_intervals(
NEIR0=(S0, E0, I0, R0),
r0_interval=((1 - reduce_by) * r0_inf,
(1 - reduce_by) * r0_sup, 0.95),
gamma_inv_interval=(gamma_inf, gamma_sup, 0.95),
alpha_inv_interval=(alpha_inf, alpha_sup, 0.95),
t_max=t_max)
S, E, I, R, t = model.sample(sample_size)
pred = pd.DataFrame(index=(pd.date_range(
start=date, periods=t.shape[0]).strftime('%Y-%m-%d')),
data={
'S': S.mean(axis=1),
'E': E.mean(axis=1),
'I': I.mean(axis=1),
'R': R.mean(axis=1)
})
df = (pred.join(cases, how='outer').assign(
cases=lambda df: df.totalCases.fillna(df.I)).assign(
newly_infected=lambda df: df.cases - df.cases.shift(
1) + df.R - df.R.shift(1)).assign(
newly_R=lambda df: df.R.diff()).rename(
columns={'cases': 'totalCases OR I'}))
df = df.assign(days=range(1, len(df) + 1))
print(model._params)
df.to_csv(f'seir_output-{reduce_by}_2.csv')
import pandas as pd
from covid19.models import SEIRBayes
from covid19.data import load_cases, load_population
if __name__ == '__main__':
city = 'Rio de Janeiro/RJ'
cases = load_cases('city')[city]
population = load_population('city')[city]
date_for_pred = '2020-03-24'
S0 = population
I0 = cases.loc[date_for_pred]['totalCases']
E0 = 2 * I0
R0 = 0
for reduce_by in [0, 0.25, 0.50, 0.65]:
model = SEIRBayes.init_from_intervals(
NEIR0=(population, E0, I0, R0),
r0_interval=((1 - reduce_by) * 1.9, (1 - reduce_by) * 5, 0.95),
gamma_inv_interval=(10, 14, 0.95),
alpha_inv_interval=(4.2, 5, 0.95),
t_max=180)
S, E, I, R, t = model.sample(1000)
pred = pd.DataFrame(index=(pd.date_range(
start=date_for_pred, periods=t.shape[0]).strftime('%Y-%m-%d')),
data={
'S': S.mean(axis=1),
'E': E.mean(axis=1),
'I': I.mean(axis=1),
'R': R.mean(axis=1)
})
def create_basic_sidebar():
MIN_DATA_BRAZIL = '2020-03-26'
DEFAULT_CITY = 'São Paulo/SP'
DEFAULT_STATE = 'SP'
MIN_CASES_TH = 10
def format_local(key):
return {'state': 'Estado', 'city': 'Município'}.get(key, key)
def format_date(date):
return (datetime.strptime(date, "%Y-%m-%d").strftime("%d/%m/%Y"))
@st.cache(show_spinner=False)
def make_place_options(cases_df, population_df):
return (cases_df.swaplevel(
0, 1,
axis=1)['totalCases'].pipe(lambda df: df >= MIN_CASES_TH).any().
pipe(lambda s: s[s & s.index.isin(population_df.index)]).index)
@st.cache(show_spinner=False)
def make_date_options(cases_df, place):
return (cases_df[place]['totalCases'].pipe(lambda s: s[
s >= MIN_CASES_TH])[MIN_DATA_BRAZIL:].index.strftime('%Y-%m-%d'))
st.sidebar.markdown(texts.INTRODUCTION_SIDEBAR)
w_r0_model = st.sidebar.checkbox(texts.R0_MODEL_DESC)
w_seir_model = st.sidebar.checkbox(texts.SEIR_MODEL_DESC)
w_queue_model = st.sidebar.checkbox(texts.QUEUE_MODEL_DESC)
st.sidebar.markdown(texts.BASE_PARAMETERS_DESCRIPTION)
st.sidebar.markdown("**Parâmetro de UF/Município**")
w_location_granularity = st.sidebar.radio("Unidade",
options=("state", "city"),
index=1,
format_func=format_local)
cases_df = data.load_cases(w_location_granularity)
if w_location_granularity == 'city':
population_df = data.load_population(w_location_granularity)
options_place = make_place_options(cases_df, population_df)
index = options_place.get_loc(DEFAULT_CITY)
w_location = st.sidebar.selectbox('Município',
options=options_place,
index=index)
elif w_location_granularity == "state":
population_df = data.load_population(w_location_granularity)
options_place = make_place_options(cases_df, population_df)
index = options_place.get_loc(DEFAULT_STATE)
w_location = st.sidebar.selectbox('Estado',
options=options_place,
index=index)
options_date = make_date_options(cases_df, w_location)
w_date = st.sidebar.selectbox('Data',
options=options_date,
index=len(options_date) - 1,
format_func=format_date)
real_cases = ur.estimate_subnotification(w_location,
w_date,
w_location_granularity,
period=True)
return {
"location_granularity": w_location_granularity,
"date": w_date,
"location": w_location,
"cases": cases_df,
"real_cases": real_cases,
"population": population_df,
"r0_model": w_r0_model,
"seir_model": w_seir_model,
"queue_model": w_queue_model
}
cCFR_place = calculateCCFR(cases)
subnotification_rate = FATAL_RATE_BASELINE / cCFR_place
return subnotification_rate, cCFR_place
if __name__ == '__main__':
st.markdown(texts.INTRODUCTION)
st.sidebar.markdown(texts.PARAMETER_SELECTION)
w_granularity = st.sidebar.selectbox('Unidade',
options=['state', 'city'],
index=1,
format_func=global_format_func)
source = 'ms' if w_granularity == 'state' else 'wcota'
cases_df = data.load_cases(w_granularity, source)
population_df = data.load_population(w_granularity)
DEFAULT_PLACE = (DEFAULT_CITY
if w_granularity == 'city' else DEFAULT_STATE)
options_place = make_place_options(cases_df, population_df)
w_place = st.sidebar.selectbox('Município',
options=options_place,
index=options_place.get_loc(DEFAULT_PLACE),
format_func=global_format_func)
options_date = make_date_options(cases_df, w_place)
w_date = st.sidebar.selectbox('Data',
options=options_date,
index=len(options_date) - 1)