#!/usr/bin/env python
# coding: utf-8

# # Disease Outbreak Response Decision-making Under Uncertainty: A retrospective analysis of measles in Sao Paulo

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')
import pandas as pd
import numpy as np
import numpy.ma as ma
from datetime import datetime
import matplotlib.pyplot as plt
import seaborn as sb
sb.set()
import pdb

from pymc3 import Model, sample, find_MAP, Metropolis, NUTS
from pymc3 import (Normal, HalfCauchy, Uniform, MvNormal, Beta, Binomial,
                   Dirichlet, Multinomial, Bernoulli, Poisson, Wishart
                  )
from pymc3 import Potential, Deterministic

import theano.tensor as T

from IPython.core.display import HTML
def css_styling():
    styles = open("styles/custom.css", "r").read()
    return HTML(styles)
css_styling()


# In[2]:


data_dir = "data/"


# Import outbreak data

# In[3]:


measles_data = pd.read_csv(data_dir+"measles.csv", index_col=0)
measles_data.NOTIFICATION = pd.to_datetime(measles_data.NOTIFICATION)
measles_data.BIRTH = pd.to_datetime(measles_data.BIRTH)
measles_data.ONSET = pd.to_datetime(measles_data.ONSET)


# In[4]:


measles_data = (measles_data.replace({'DISTRICT': {'BRASILANDIA':'BRAZILANDIA'}})
                   .drop('AGE', axis=1))


# Sao Paulo population by district

# In[5]:


sp_pop = pd.read_csv(data_dir+'sp_pop.csv', index_col=0)


# In[6]:


_names = sp_pop.index.values
_names[_names=='BRASILANDIA'] = 'BRAZILANDIA'
sp_pop.set_index(_names, inplace = True)


# In[7]:


sp_pop.head()


# Plot of cumulative cases by district

# In[8]:


measles_onset_dist = measles_data.groupby(['DISTRICT','ONSET']).size().unstack(level=0).fillna(0)
measles_onset_dist.cumsum().plot(legend=False, grid=False)


# In[9]:


total_district_cases = measles_onset_dist.sum()


# Top 5 districts by number of cases

# In[10]:


totals = measles_onset_dist.sum()
totals.sort(ascending=False)
totals[:5]


# Age distribution of cases, by confirmation status

# In[11]:


by_conclusion = measles_data.groupby(["YEAR_AGE", "CONCLUSION"])
counts_by_cause = by_conclusion.size().unstack().fillna(0)
ax = counts_by_cause.plot(kind='bar', stacked=True, xlim=(0,50), figsize=(15,5))


# ### Vaccination Data

# In[12]:


vaccination_data = pd.read_csv('data/BrazilVaxRecords.csv', index_col=0)
vaccination_data.head()


# ```python
# 
# sia_susc = np.ones(len(vax_97))
# birth_year = np.arange(1922, 1998)[::-1]
# by_mask = (birth_year > 1983) & (birth_year < 1992)
# by_mask = (birth_year > 1972) & (birth_year < 1986)
# sia_susc[by_mask] *= 0.2
# ```

# In[13]:


vax_97 = np.r_[[0]*(1979-1921+1), vaccination_data.VAX[:17]]
n = len(vax_97)
FOI_mat = np.resize((1 - vax_97*0.9), (n,n)).T


# In[14]:


FOI_mat


# In[15]:


vacc_susc = (1 - vax_97*0.9)[::-1]
vacc_susc[0] = 0.5


# In[16]:


vacc_susc


# In[17]:


sia_susc = np.ones(len(vax_97))
birth_year = np.arange(1922, 1998)[::-1]
by_mask = ((birth_year > 1983) & (birth_year < 1992)) | ((birth_year > 1972) & (birth_year < 1986))
sia_susc[by_mask] *= 0.2


# In[18]:


sia_susc


# ## Stochastic Disease Transmission Model
# 
# As a baseline for comparison, we can fit a model to all the clinically-confirmed cases, regardless of lab confirmation status. For this, we will use a simple SIR disease model, which will be fit using MCMC.
# 
# This model fits the series of 2-week infection totals in each district $i$ as a set of Poisson models:
# 
# \\[Pr(I(t)_{i} | \lambda(t)_i) = \text{Poisson}(\lambda(t)_i) \\]
# 
# Where the outbreak intensity is modeled as:
# 
# \\[\lambda(t)_i = \beta [I^{(w)}(t-1)_i]^{\alpha} S(t-1)_i\\]
# 
# \\[\alpha \sim \text{Exp}(1)\\]
# 
# We will assume here that the transmission rate is constant over time (and across districts):
# 
# \\[\beta \sim \text{Gamma}(1, 0.1)\\]
# 
# To account for the influence of infected individuals from neighboring districts on new infections, the outbreak intensity was modeled using a spatial-weighted average of infecteds across districts, where populations were weighted as an exponential function of the distance between district centroids:
# 
# \\[w_{d} = \text{exp}(-\theta d)\\]
# 
# \\[\theta \sim \text{Exp}(1)\\]
# 
# ### Confirmation Sub-model
# 
# Rather than assume all clinical cases are true cases, we can adjust the model to account for lab confirmation probability. This is done by including a sub-model that estimates age group-specific probabilities of confirmation, and using these probabilities to estimate the number of lab-confirmed cases. These estimates are then plugged into the model in place of the clinically-confirmed cases.
# 
# We specified a structured confirmation model to retrospectively determine the age group-specific probabilities of lab confirmation for measles, conditional on clinical diagnosis. Individual lab confirmation events $c_i$ were modeled as Bernoulli random variables, with the probability of confirmation being allowed to vary by age group:
# 
# $$c_i \sim \text{Bernoulli}(p_{a(i)})$$
# 
# where $a(i)$ denotes the appropriate age group for the individual indexed by i. There were 16 age groups, the first 15 of which were 5-year age intervals $[0,5), [5, 10), \ldots , [70, 75)$, with the 16th interval including all individuals 75 years and older.
# 
# Since the age interval choices were arbitrary, and the confirmation probabilities of adjacent groups likely correlated, we modeled the correlation structure directly, using a multivariate logit-normal model. Specifically, we allowed first-order autocorrelation among the age groups, whereby the variance-covariance matrix retained a tridiagonal structure. 
# 
# $$\begin{aligned}
# \Sigma = \left[{
# \begin{array}{c}
#   {\sigma^2} & {\sigma^2 \rho} & 0& \ldots & {0} & {0}  \\
#   {\sigma^2 \rho} & {\sigma^2} &  \sigma^2 \rho & \ldots & {0}  & {0} \\
#   {0} & \sigma^2 \rho & {\sigma^2} & \ldots & {0} & {0} \\
#   \vdots & \vdots & \vdots &  & \vdots & \vdots\\
#   {0} & {0} & 0 & \ldots &  {\sigma^2} & \sigma^2 \rho  \\
# {0} & {0} & 0 & \ldots & \sigma^2 \rho &  {\sigma^2} 
# \end{array}
# }\right]
# \end{aligned}$$
# 
# From this, the confirmation probabilities were specified as multivariate normal on the inverse-logit scale.
# 
# $$ \text{logit}(p_a) = \{a\} \sim N(\mu, \Sigma)$$
# 
# Priors for the confirmation sub-model were specified by:
# 
# $$\begin{aligned}
# \mu_i &\sim N(0, 100) \\
# \sigma &\sim \text{HalfCauchy}(25) \\
# \rho &\sim U(-1, 1)
# \end{aligned}$$

# Age classes are defined in 5-year intervals.

# In[19]:


age_classes = [0,5,10,15,20,25,30,35,40,100]
measles_data.dropna(subset=['YEAR_AGE'], inplace=True)
measles_data['YEAR_AGE'] = measles_data.YEAR_AGE.astype(int)
measles_data['AGE_GROUP'] = pd.cut(measles_data.YEAR_AGE, age_classes, right=False)


# Lab-checked observations are extracted for use in estimating lab confirmation probability.

# In[20]:


CONFIRMED = measles_data.CONCLUSION == 'CONFIRMED'
CLINICAL = measles_data.CONCLUSION == 'CLINICAL'
DISCARDED = measles_data.CONCLUSION == 'DISCARDED'


# Extract confirmed and clinical subset, with no missing county information.

# In[21]:


lab_subset = measles_data[(CONFIRMED | CLINICAL) & measles_data.COUNTY.notnull()].copy()


# In[22]:


age = lab_subset.YEAR_AGE.values
ages = lab_subset.YEAR_AGE.unique()
counties = lab_subset.COUNTY.unique()
confirmed = (lab_subset.CONCLUSION=='CONFIRMED').values


# In[23]:


clinic_subset = measles_data[CLINICAL & measles_data.COUNTY.notnull()].copy()


# In[24]:


_lab_subset = lab_subset.replace({"CONCLUSION": {"CLINICAL": "UNCONFIRMED"}})
by_conclusion = _lab_subset.groupby(["YEAR_AGE", "CONCLUSION"])
counts_by_cause = by_conclusion.size().unstack().fillna(0)
ax = counts_by_cause.plot(kind='bar', stacked=True, xlim=(0,50), 
                          figsize=(15,5), grid=False)


# In[25]:


lab_subset.shape


# In[26]:


confirmed.sum()


# Proportion of lab-confirmed cases older than 20 years

# In[27]:


(measles_data[CONFIRMED].YEAR_AGE>20).mean()


# In[28]:


age_classes


# In[29]:


age_group = pd.cut(age, age_classes, right=False)
age_index = np.array([age_group.categories.tolist().index(i) for i in age_group])
age_groups = age_group.categories
age_groups


# In[30]:


#Extract cases by age and time.
age_group = pd.cut(age, age_classes, right=False)
age_index = np.array([age_group.categories.tolist().index(i) for i in age_group])


# In[31]:


age_group.categories


# In[32]:


age_slice_endpoints = [g[1:-1].split(',') for g in age_groups]
age_slices = [slice(int(i[0]), int(i[1])) for i in age_slice_endpoints]


# In[33]:


# Get index from full crosstabulation to use as index for each district
dates_index = measles_data.groupby(
        ['ONSET', 'AGE_GROUP']).size().unstack().index


# In[34]:


unique_districts = measles_data.DISTRICT.dropna().unique()


# In[35]:


excludes = ['BOM RETIRO']


# In[36]:


N = sp_pop.drop(excludes).ix[unique_districts].sum().drop('Total')


# In[37]:


N_age = N.iloc[:8]
N_age.index = age_groups[:-1]
N_age[age_groups[-1]] = N.iloc[8:].sum()
N_age


# Compile bi-weekly confirmed and unconfirmed data by Sao Paulo district

# In[38]:


# All confirmed cases, by district
confirmed_data = lab_subset[lab_subset.CONCLUSION=='CONFIRMED']
confirmed_counts = (confirmed_data.groupby(['ONSET', 'AGE_GROUP'])
                    .size()
                    .unstack()
                    .reindex(dates_index)
                    .fillna(0)
                    .sum())

all_confirmed_cases = (confirmed_counts.reindex_axis(measles_data['AGE_GROUP'].unique())
                       .fillna(0).values.astype(int))


# In[39]:


confirmed_counts_2w = (confirmed_data
                        .groupby(['ONSET', 'AGE_GROUP'])
                        .size()
                        .unstack()
                        .reindex(dates_index)
                        .fillna(0)
                        .resample('2W', how='sum'))


# In[40]:


# All clinical cases, by district
clinical_counts = (clinic_subset.groupby(['ONSET', 'AGE_GROUP'])
                   .size()
                   .unstack()
                   .reindex(dates_index)
                   .fillna(0)
                   .sum())

all_clinical_cases = (clinical_counts.reindex_axis(measles_data['AGE_GROUP'].unique())
                      .fillna(0).values.astype(int))


# In[41]:


clinical_counts_2w = (clinic_subset
                        .groupby(['ONSET', 'AGE_GROUP'])
                        .size()
                        .unstack()
                        .reindex(dates_index)
                        .fillna(0)
                        .resample('2W', how='sum'))


# In[42]:


age_groups = np.sort(measles_data['AGE_GROUP'].unique())
age_groups


# Check shape of data frame
# 
# - 28 bi-monthly intervals, 9 age groups

# In[43]:


assert clinical_counts_2w.shape == (28, len(age_groups))


# In[44]:


confirmed_counts_2w


# In[45]:


clinical_counts_2w


# In[46]:


obs_date = '1997-06-15'
confirmed_obs_t = confirmed_counts_2w[confirmed_counts_2w.index <= obs_date].values
clinical_obs_t = clinical_counts_2w[clinical_counts_2w.index <= obs_date].values


# In[47]:


model_june = Model()
model_july = Model()
model_june_noconf = Model()
model_july_noconf = Model()


# In[48]:


N_age.values.astype(int)


# In[49]:


# Downsample annual series to observed age groups
downsample = lambda x: np.array([x[s].mean() for s in age_slices])


# In[50]:


invlogit = T.nnet.sigmoid

confirmation = True

def specify_model(model, observation_date, confirmation=True):

    with model:

        n_periods, n_age_groups = confirmed_counts_2w.shape

        ### Confirmation sub-model

        if confirmation:

            # Specify priors on age-specific means
            age_classes = np.unique(age_index)

            p_age = Beta('p_age', 1, 1, shape=n_age_groups)
            p_confirm = p_age[age_index]

            # Confirmation likelihood
            lab_confirmed = Bernoulli('lab_confirmed', p=p_confirm, 
                                      observed=confirmed)


        '''
        Truncate data at observation period
        '''  
        obs_index = confirmed_counts_2w.index <= observation_date
        confirmed_obs_t = confirmed_counts_2w[obs_index].values
        clinical_obs_t = clinical_counts_2w[obs_index].values

        # Index for observation date, used to index out values of interest 
        # from the model.
        t_obs = obs_index.sum() - 1

        if confirmation:

            clinical_cases = [Binomial('clinical_cases_%i' % n, i, 
                               p_age, 
                               shape=n_age_groups) for n,i in enumerate(clinical_obs_t)]
            I_age = [Deterministic('I_age_%i' % i, clinical + confirmed_obs_t[i]) 
                                   for i,clinical in enumerate(clinical_cases)]
            I = T.stacklists(I_age)

        else:

            I = confirmed_obs_t + clinical_obs_t

        # Calcuate age distribution from observed distribution of infecteds to date
        age_dist = Dirichlet('age_dist', np.ones(n_age_groups), 
                             shape=n_age_groups)

        age_dist_like = Potential('age_dist_like', 
                        Multinomial.dist(I.sum(), age_dist).logp(I.sum(0)))

        # Transmission parameter
        beta = Uniform('beta', 1, 100)
        
        A = 75./(beta - 1)
        lt_sum = downsample(np.tril(FOI_mat).sum(0)[::-1])
        natural_susc = Deterministic('natural_susc', T.exp((-1/A) * lt_sum))

        # Weakly-informative prior on proportion susceptible being 
        # between 0 and 0.07
#         natural_susc = Beta('natural_susc', 2, 100, shape=n_age_groups)
        p_susceptible = Deterministic('p_susceptible', 
                            downsample(sia_susc) * downsample(vacc_susc) * natural_susc)

        # Estimated total initial susceptibles
        S_0 = Binomial('S_0', n=N_age.values.astype(int), p=p_susceptible, 
                       shape=n_age_groups)

        S = Deterministic('S', S_0 - I.cumsum(axis=0))

        # Force of infection
        λ = Deterministic('λ', beta * I.sum(1) * S.sum(1) / N_age.sum())

        new_cases = Potential('new_cases', Poisson.dist(λ[:-1]).logp(I.sum(1)[1:]))


    return model


# In[51]:


from theano import config
config.warn.reduce_join = False


# Run models for June 15 and July 15 observation points, both with and without clinical confirmation.

# In[ ]:


n_iterations = 100000
n_burn = 90000


# June 15, with lab confirmation

# In[ ]:


model_june = specify_model(model_june, '1997-06-15')

with model_june:
    june_trace = sample(n_iterations)


# July 15, with lab confirmation

# In[ ]:


model_july = specify_model(model_july, '1997-07-15')

with model_july:
    july_trace = sample(n_iterations)


# June 15, no lab confirmation

# In[ ]:


model_june_noconf = specify_model(model_june_noconf, '1997-06-15', 
                                  confirmation=False)

with model_june_noconf:
    june_noconf_trace = sample(n_iterations)


# July 15, no lab confirmation

# In[ ]:


model_july_noconf = specify_model(model_july_noconf, '1997-07-15', 
                                  confirmation=False)

with model_july_noconf:
    july_noconf_trace = sample(n_iterations)


# ## Summary of model output
# 
# Age-specific confirmation, June model

# In[57]:


from pymc3 import traceplot, forestplot

forestplot(june_trace[n_burn:], vars=['p_age'])


# Lab confirmation rates, June model

# In[58]:


p_age = pd.DataFrame(june_trace.get_values('p_age'), columns=age_groups)

f, axes = plt.subplots(figsize=(14,6))
sb.boxplot(data=p_age, linewidth=0.3, fliersize=0, ax=axes,
          color=sb.color_palette("coolwarm", 5)[0],
          order=age_group.categories)
axes.set_ylabel('Confirmation rate')
axes.set_xlabel('Age group')


# Proportion of population susceptible, June model.

# In[59]:


forestplot(june_trace, vars=['p_susceptible'])


# Proportion of population susceptible, June model with no confirmation correction

# In[60]:


forestplot(june_noconf_trace[n_burn:], vars=['p_susceptible'])


# Epidemic intensity estimates at June and July.

# In[61]:


forestplot(june_trace[n_burn:], vars=['λ'])


# In[62]:


forestplot(july_trace[n_burn:], vars=['λ'])


# Transmission paramter

# In[63]:


traceplot(june_trace[n_burn:], vars=['beta'])


# In[64]:


traceplot(june_noconf_trace[n_burn:], vars=['beta'])


# Epidemic intensity for lab- versus clinical-confirmation models

# In[65]:


from pymc3 import df_summary


# In[66]:


lam_june = df_summary(june_trace[n_burn:], vars='λ')

fig, axes = plt.subplots(2, 1, sharey=True)

lam_june['mean'].plot(ax=axes[0], alpha=0.4)
axes[0].set_ylabel('Epidemic intensity')
axes[0].set_xlabel('time (2-week periods)')
axes[0].set_title('Lab confirmation')

lam_june_noconf = df_summary(june_noconf_trace[n_burn:], vars='λ')

lam_june_noconf['mean'].plot(ax=axes[1], alpha=0.4)
axes[1].set_ylabel('Epidemic intensity')
axes[1].set_xlabel('time (2-week periods)')
axes[1].set_title('Clinical confirmation')

plt.tight_layout()


# In[67]:


plt.boxplot(july_trace.get_values('S')[n_burn:,-1,:]);


# In[68]:


S_age_june = pd.DataFrame(june_trace.get_values('S')[n_burn:, -1, :], 
                          columns=age_group.categories).unstack().reset_index()
S_age_june.columns = 'Age', 'Iteration', 'S'
S_age_june['Confirmation'] = 'Lab'


# In[69]:


S_age_june = pd.DataFrame(june_trace.get_values('S')[n_burn:, -1, :], 
                          columns=age_group.categories).unstack().reset_index()
S_age_june.columns = 'Age', 'Iteration', 'S'
S_age_june['Confirmation'] = 'Lab'

S_age_june_noconf = pd.DataFrame(june_noconf_trace.get_values('S')[n_burn:, -1, :], 
                                 columns=age_group.categories).unstack().reset_index()
S_age_june_noconf.columns = 'Age', 'Iteration', 'S'
S_age_june_noconf['Confirmation'] = 'Clinical'

S_age_june = pd.concat([S_age_june, S_age_june_noconf], ignore_index=True)


# In[70]:


S_age_july = pd.DataFrame(july_trace.get_values('S')[n_burn:, -1, :],  
                          columns=age_group.categories).unstack().reset_index()
S_age_july.columns = 'Age', 'Iteration', 'S'
S_age_july['Confirmation'] = 'Lab'

S_age_july_noconf = pd.DataFrame(june_noconf_trace.get_values('S')[n_burn:, -1, :],  
                                 columns=age_group.categories).unstack().reset_index()
S_age_july_noconf.columns = 'Age', 'Iteration', 'S'
S_age_july_noconf['Confirmation'] = 'Clinical'

S_age_july = pd.concat([S_age_july, S_age_july_noconf], ignore_index=True)


# Numbers of suscepibles in each age group, under lab vs clinical confirmation

# In[71]:


g = sb.factorplot("Age", "S", "Confirmation", S_age_june, kind="box",
                   palette="hls", size=6, aspect=2, linewidth=0.3, fliersize=0, 
                  order=age_group.categories)
g.despine(offset=10, trim=True)
g.set_axis_labels("Age Group", "Susceptibles");


# In[72]:


g = sb.factorplot("Age", "S", "Confirmation", S_age_july, kind="box",
                   palette="hls", size=6, aspect=2, linewidth=0.3, fliersize=0, 
                  order=age_group.categories)
g.despine(offset=10, trim=True)
g.set_axis_labels("Age Group", "Susceptibles");


# ## Vaccination coverage

# In[73]:


n_age_groups = len(age_groups)

coverage_5 = np.r_[[0.95], [0]*(n_age_groups - 1)]
coverage_15 = np.r_[[0.95]*3, [0]*(n_age_groups - 3)]
coverage_30 = np.r_[[0.95]*6, [0]*(n_age_groups - 6)]
coverage_adult = np.r_[[0.95, 0, 0, 0, 0.95, 0.95] + [0]*(n_age_groups - 6)]

coverage_dict = {'pct_5': coverage_5,
                'pct_15': coverage_15,
                'pct_30': coverage_30,
                'pct_adult': coverage_adult}


# In[74]:


def calc_coverage(trace, p):
    n = trace.get_values('S')[:,-1,:].copy().astype(int)
    n[n<0] = 0
    vacc = np.random.binomial(n, p).sum(1)
    pct = vacc/trace.get_values('S')[:,-1,:].sum(1)
    
    return pct


# In[75]:


labels = ['pct_5', 'pct_15', 'pct_30', 'pct_adult']

june_coverage = pd.DataFrame({v: calc_coverage(june_trace[n_burn:], 
                                               coverage_dict[v]) for v in labels})
june_coverage['Month'] = 'June'
june_coverage['Confirmation'] = 'Lab'


# In[76]:


june_noconf_coverage = pd.DataFrame({v: calc_coverage(june_noconf_trace[n_burn:], 
                                               coverage_dict[v]) for v in labels})
june_noconf_coverage['Month'] = 'June'
june_noconf_coverage['Confirmation'] = 'Clinical'

july_coverage = pd.DataFrame({v: calc_coverage(july_trace[n_burn:], 
                                               coverage_dict[v]) for v in labels})
july_coverage['Month'] = 'July'
july_coverage['Confirmation'] = 'Lab'

july_noconf_coverage = pd.DataFrame({v: calc_coverage(july_noconf_trace[n_burn:], 
                                               coverage_dict[v]) for v in labels})
july_noconf_coverage['Month'] = 'July'
july_noconf_coverage['Confirmation'] = 'Clinical'


# In[77]:


coverage = pd.concat([june_coverage, june_noconf_coverage, july_coverage, july_noconf_coverage], 
                     ignore_index=True)


# In[78]:


sb.factorplot(row="Month", col="Confirmation", data=coverage, kind='box',
              row_order=['June', 'July'],
              order=['pct_5', 'pct_15', 'pct_30', 'pct_adult'],
               palette="YlGnBu_d", linewidth=0.7, fliersize=0, aspect=1.25).despine(left=True)


# ## Mapping spatial effects

# In[ ]:


from mpl_toolkits.basemap import Basemap
import geopandas as gpd

lllat=-24
urlat=-23.3
lllon=-47
urlon=-46.3

SP_base = Basemap(ax=None, lon_0=(urlon + lllon) / 2, lat_0=(urlat + lllat) / 2,
        llcrnrlat=lllat, urcrnrlat=urlat, llcrnrlon=lllon, urcrnrlon=urlon, 
                  resolution='i',
                 epsg='4326')


# In[ ]:


SP_dist = gpd.GeoDataFrame.from_file('Sao Paulo/Brazil_full/BRA_adm3.shp').to_crs({'proj': 'longlat', 
                                                                               'ellps': 'WGS84', 
                                                                               'datum': 'WGS84'})


# In[ ]:


SP_dist['DIST_NAME'] = [trans.trans(_).upper() for _ in SP_dist.NAME_3]


# In[ ]:


λ_june = pd.Series(model_june.λ_t.stats()['mean'], index=sp_districts)


# In[ ]:


λ_june


# In[ ]:


SP_dist_merged = SP_dist.merge(pd.DataFrame(λ_june, columns=['λ']), left_on='DIST_NAME', right_index=True)


# In[ ]:


measles_onset_conf = measles_data[CONFIRMED].groupby(['DISTRICT','ONSET']).size().unstack(level=0).fillna(0).sum()


# In[ ]:


measles_onset_conf


# In[ ]:


_rates = measles_onset_conf/sp_pop.sum(1)


# In[ ]:


SP_dist_conf = SP_dist.merge(pd.DataFrame(_rates, columns=['rate']), left_on='DIST_NAME', right_index=True)


# Estimated expected value for infecteds, by district

# In[ ]:


from matplotlib.pyplot import cm

map_fig = plt.figure(figsize=(16,12))
map_ax = plt.gca()
SP_base.drawcoastlines()
SP_base.drawrivers()

SP_dist_merged.plot(column='λ', colormap=cm.Reds, axes=map_ax)


# Observed confirmed cases, by district

# In[ ]:


map_fig = plt.figure(figsize=(16,12))
map_ax = plt.gca()
SP_base.drawcoastlines()
SP_base.drawrivers()

SP_dist_conf.plot(column='rate', colormap=cm.Reds, axes=map_ax)