def run_seir(w_date,
w_location,
cases_df,
population_df,
w_location_granulariy,
r0_dist,
w_params=DEFAULT_PARAMS,
sample_size=SAMPLE_SIZE,
reported_rate=None,
NEIR0=None):
if reported_rate is None:
calc_reported_rate, cCFR = estimate_subnotification(
w_location, w_date, w_location_granulariy)
new_reported_rate = calc_reported_rate * 100
else:
new_reported_rate = reported_rate
if NEIR0 is None:
new_NEIR0 = make_NEIR0(cases_df, population_df, w_location, w_date,
reported_rate)
else:
new_NEIR0 = NEIR0
w_params['r0_dist'] = r0_dist
model = SEIRBayes(new_NEIR0, w_params['r0_dist'],
w_params['gamma_inv_dist'], w_params['alpha_inv_dist'],
new_reported_rate, w_params['t_max'])
model_output = model.sample(sample_size)
return (model, model_output, sample_size,
w_params['t_max']), new_reported_rate, new_NEIR0
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)
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_output, sample_size)
st.markdown(texts.MODEL_INTRO)
st.write(texts.SEIRBAYES_DESC)
w_scale = st.selectbox('Escala do eixo Y', ['log', 'linear'], index=1)
fig = plot_EI(model_output, w_scale)
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, should_estimate_r0)
st.markdown(href, unsafe_allow_html=True)
download_placeholder.empty()
dists = [w_params['alpha_inv_dist'], w_params['gamma_inv_dist'], r0_dist]
SEIR0 = model._params['init_conditions']
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 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')
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)
})
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 -
options=options_date,
index=len(options_date) - 1)
NEIR0 = make_NEIR0(cases_df, population_df, w_place, w_date)
w_params = make_param_widgets(NEIR0)
sample_size = st.sidebar.number_input(
'Qtde. de iterações da simulação (runs)',
min_value=1,
max_value=3_000,
step=100,
value=300)
st.markdown(texts.MODEL_INTRO)
w_scale = st.selectbox('Escala do eixo Y', ['log', 'linear'], index=1)
w_show_uncertainty = st.checkbox('Mostrar intervalo de confiança',
value=True)
model = SEIRBayes.init_from_intervals(**w_params)
model_output = model.sample(sample_size)
ei_df = make_EI_df(model_output, sample_size)
fig = plot(model_output, w_scale, w_show_uncertainty)
st.altair_chart(fig)
download_placeholder = st.empty()
if download_placeholder.button('Preparar dados para download em CSV'):
href = make_download_df_href(ei_df)
st.markdown(href, unsafe_allow_html=True)
download_placeholder.empty()
intervals = [
w_params['alpha_inv_interval'], w_params['gamma_inv_interval'],
w_params['r0_interval']
]
SEIR0 = model._params['init_conditions']
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)
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_output, SAMPLE_SIZE)
st.markdown(texts.MODEL_INTRO)
st.write(texts.SEIRBAYES_DESC)
w_scale = st.selectbox('Escala do eixo Y', ['log', 'linear'], index=1)
fig = plot_EI(model_output, w_scale)
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()
dists = [w_params['alpha_inv_dist'], w_params['gamma_inv_dist'], r0_dist]
SEIR0 = model._params['init_conditions']