def observed_vs_model(self, out='simple_fit/', save=False):
countries = self.countries + ['All Countries']
data = {}
for i, c in enumerate(countries):
data[c] = {}
# first reported cases assumed to have been reported in generation 5
start_generation = 5
fig = plt.figure()
ax = fig.add_subplot(111)
width = 0.35
# up-to-date data
x, y = self.get_SI_series(c, fill_y=True)
x = x + 5
R0, d = f.RMSD_fit(x, y)
y_rmsd = f.cumI(x, R0, d)
ax.bar(x, y, width, color='b', alpha=0.3)
ax.plot(x + width, y_rmsd, color='b', label='Latest Data Fit')
data[c]['R0'] = R0
data[c]['d'] = d
data[c]['RMSD'] = f.RMSD(y, y_rmsd)
# fisman data
x, y = self.get_SI_series(c, fill_y=True, fisman_data=True)
x = x + 5
R0, d = f.RMSD_fit(x, y)
y_rmsd = f.cumI(x, R0, d)
ax.bar(x + width, y, width, color='r', alpha=0.3)
ax.plot(x + width, y_rmsd, color='r', label='Fisman Data Fit')
data[c]['Fisman R0'] = R0
data[c]['Fisman d'] = d
data[c]['Fisman RMSD'] = f.RMSD(y, y_rmsd)
plt.legend(loc=2)
# plt.title(c)
if save:
d = self.root + out
Analysis.check_dir(d)
fn = d + 'simple_fit_%s.png' % (c.lower().replace(' ', '_'))
plt.savefig(fn)
# save latex table
keys = [
'R0', 'Fisman R0', 'd', 'Fisman d', 'RMSD', 'Fisman RMSD'
]
with open(d + 'simple_fit.tex', 'w') as fw:
fw.write(Analysis.to_latex_table(data, keys))
else:
plt.show()
def contour_plot(self, out='contour/', save=False):
countries = self.countries + ['All Countries']
intervals = [12, 15, 18]
R0_range = np.linspace(1.4, 3.6, 100)
d_range = np.linspace(0, 0.07, 100)
data = {}
for i, c in enumerate(countries):
print 'Contour Plot for %s' % (c)
data[c] = {}
x, y = self.get_SI_series(c, fill_y=True)
x = x + 5
R0, d = f.RMSD_fit(x, y)
data[c]['R0'] = R0
data[c]['d'] = d
if save:
d = self.root + out
Analysis.check_dir(d)
fn = d + "param_contour_%s.png" % (c.lower().replace(' ', '_'))
p.parameter_heatmap(x,
y,
R0_range,
d_range,
f.cumI,
f.RMSD,
fn=fn)
else:
p.parameter_heatmap(x, y, R0_range, d_range, f.cumI, f.RMSD)
if save:
d = self.root + out
Analysis.check_dir(d)
keys = ['R0', 'd']
with open(d + 'contour.tex', 'w') as fw:
fw.write(
Analysis.to_latex_table(
data, keys, caption='Best-fit R0 and d by Country'))
def control_assessment(self, out='control/', save=False):
countries = self.countries + ['All Countries']
data = {}
for i, c in enumerate(countries):
data[c] = {}
# first reported cases assumed to have been reported in generation 5
start_generation = 5
# up-to-date data
x0, y0 = self.get_SI_series(c, fill_y=True)
x0 = x0 + start_generation
fig, (ax1, ax2) = plt.subplots(1, 2) #, sharey=True)
fig.set_size_inches(15, 5)
#.plot(x, y) # projection
# axarr[0].set_title('Sharing X axis')
# axarr[1].scatter(x, y) # error
for i in range(2, len(x0)):
x = x0[0:i]
y = y0[0:i]
actual = y0[i]
R0, d = f.RMSD_fit(x, y)
SI = x[-1]
projected = f.cumI(SI + 1, R0, d)
# projection vs actual
if projected < 2 * actual:
ax1.scatter(actual, projected, alpha=0.3)
if actual > 500:
ax1.annotate('%i' % (SI),
(actual - 100, projected + 150))
# error
error = np.abs(projected - actual) / float(actual)
if error > 1.5:
print 'Error for SI=%i is %f !' % (x[-1], error)
else:
plt.scatter(x0[i], error)
ax1.plot(y0, y0, 'b-')
ax1.set_ylabel('Projected Cases')
ax1.set_xlabel('Actual Cases')
ax2.set_ylim([0, 1])
ax2.set_ylabel('Percent Error')
ax2.set_xlabel('Serial Inteval')
if save:
d = self.root + out
Analysis.check_dir(d)
fn = d + 'control_%s.png' % (c.lower().replace(' ', '_'))
plt.savefig(fn)
else:
plt.show()
def progressive_projections(self, out='sensitivity/', save=False):
countries = self.countries + ['All Countries']
data = {}
for i, c in enumerate(countries):
data[c] = {}
# first reported cases assumed to have been reported in generation 5
start_generation = 5
# up-to-date data
x0, y0 = self.get_SI_series(c, fill_y=True)
x0 = x0 + start_generation
intervals = range(2, len(x0))
plt.figure()
fig = plt.figure()
ax = plt.subplot(111)
colors = self.get_colors(n=len(intervals))
for ind, i in enumerate(intervals):
x = x0[:i + 1]
y = y0[:i + 1]
R0, d = f.RMSD_fit(x, y)
y_fit = f.cumI(x0, R0, d)
ax.plot(x,
y_fit[:i + 1],
color=colors[ind],
label='SI=%i' % (i))
ax.plot(x0, y_fit, '--', color=colors[ind]) #, ls='--')
ax.scatter(x[-1], y_fit[i], color=colors[ind])
width = 0.8
ax.bar(x0 - 0.5 * width, y0, width, color='blue', alpha=0.1)
ax.set_ylim(0, y0[-1] * 1.5)
ax.legend(loc='upper center',
bbox_to_anchor=(0.5, 1.05),
ncol=5,
fancybox=True,
shadow=True)
ax.set_ylabel('Cumulative Incidence Count')
ax.set_xlabel('Generations Available for Fitting')
if save:
d = self.root + out
Analysis.check_dir(d)
fn = d + 'progressive_projections_%s.png' % (c.lower().replace(
' ', '_'))
plt.savefig(fn)
else:
plt.show()
def sensitivity_analysis(self, out='sensitivity/', save=False):
countries = self.countries + ['All Countries']
keys = [
'Base Case', '12 day generation time', '18 day generation time',
'Outbreak recognized generation 3',
'Outbreak recognized generation 7', 'Outbreak 50% underreported',
'Outbreak 99% underreported', 'Deaths only'
]
for c in countries:
series = {'up-to-date': {}, 'Fisman': {}}
for k, xy in series.iteritems():
if k == 'Fisman':
self.fisman_data = True
else:
self.fisman_data = False
x0, y0 = self.get_SI_series(c, fill_y=True)
xy['Base Case'] = (x0 + 5, y0)
self.serial_interval = 12
x0, y0 = self.get_SI_series(c, fill_y=True)
xy['12 day generation time'] = (x0 + 5, y0)
self.serial_interval = 18
x0, y0 = self.get_SI_series(c, fill_y=True)
xy['18 day generation time'] = (x0 + 5, y0)
#reset
self.serial_interval = 15
x0, y0 = self.get_SI_series(c, fill_y=True)
xy['Outbreak recognized generation 3'] = (x0 + 3, y0)
xy['Outbreak recognized generation 7'] = (x0 + 7, y0)
xy['Outbreak 50% underreported'] = ((x0 + 5) * 0.5, y0)
xy['Outbreak 99% underreported'] = ((x0 + 5) * 0.01, y0)
self.incidence_type = 'deaths'
x0, y0 = self.get_SI_series(c, fill_y=True)
xy['Deaths only'] = (x0 + 5, y0)
# reset
self.incidence_type = 'cases'
data = {}
categories = ['R0', 'R0 (Fisman)', 'd', 'd (Fisman)']
for category in categories:
if 'Fisman' in category:
sub_series = series['Fisman']
else:
sub_series = series['up-to-date']
column = {}
for k in keys:
x, y = sub_series[k]
R0, d = f.RMSD_fit(x, y)
if 'R0' in category:
column[k] = R0
else:
column[k] = d
data[category] = column
table = Analysis.to_latex_table(data,
keys,
countries=categories,
caption=c)
if save:
d = self.root + out
Analysis.check_dir(d)
fn = d + 'sensitivity_%s.tex' % (c.lower().replace(' ', '_'))
with open(fn, 'w') as fw:
fw.write(table)
else:
print table
def deltaD(self, out='deltaD/', save=False):
countries = self.countries + ['All Countries']
data = {}
for i, c in enumerate(countries):
data[c] = {}
# first reported cases assumed to have been reported in generation 5
start_generation = 5
# up-to-date data
x0, y0 = self.get_SI_series(c, fill_y=True)
x0 = x0 + start_generation
fig, ax1 = plt.subplots()
intervals = range(5, len(x0))
R0_list = [None] * len(x0)
d_list = [None] * len(x0)
deltaD_list = [None] * len(x0)
colors = self.get_color_map(n=len(intervals))
prev_d = None
for i in intervals:
x = x0[:i]
y = y0[:i]
R0, d = f.RMSD_fit(x, y)
R0_list[i] = R0
d_list[i] = d
if prev_d is None:
prev_d = d
else:
deltaD = d - prev_d
deltaD_list[i] = deltaD
prev_d = d
ax1.set_xlabel("Generations Available For fitting")
# plot d
ax1.plot(x0, d_list, 'b--', label='d')
d_tuples = [i for i in zip(x0, d_list) if i[1] is not None]
ax1.scatter(*zip(*d_tuples), alpha=0.5)
ax1.plot(x0, deltaD_list, 'b-', label='delta d')
ax1.set_ylabel('', color='b')
for tl in ax1.get_yticklabels():
tl.set_color('b')
# plot deltaD
deltaD_tuples = [
i for i in zip(x0, deltaD_list) if i[1] is not None
]
_x, _y = zip(*deltaD_tuples)
ax1.fill_between(_x, 1e-6, _y, facecolor='blue', alpha=0.5)
plt.legend(loc=2)
# plot R0
ax2 = ax1.twinx()
# ax2.set_yscale('log')
ax2.plot(x0, R0_list, 'r-', label='R0')
R0_tuples = [i for i in zip(x0, R0_list) if i[1] is not None]
ax2.scatter(*zip(*R0_tuples), color='red', alpha=0.5)
ax2.set_ylabel('', color='r')
for t2 in ax2.get_yticklabels():
t2.set_color('r')
plt.legend()
if save:
d = self.root + out
Analysis.check_dir(d)
fn = d + 'deltaD_%s.png' % (c.lower().replace(' ', '_'))
plt.savefig(fn)
else:
plt.show()
def duration_and_size_projection(self, out='projected/', save=False):
countries = self.countries + ['All Countries']
data = {}
for i, c in enumerate(countries):
data[c] = {}
# first reported cases assumed to have been reported in generation 5
start_generation = 5
# up-to-date data
x, y = self.get_SI_series(c, fill_y=True)
x = x + start_generation
start_generation_index = 2
generations = range(start_generation_index, len(x))
I_total = [None] * len(x)
t_max = [None] * len(x)
for i in generations:
_x = x[:i]
_y = y[:i]
R0, d = f.RMSD_fit(_x, _y)
try:
I_total[i] = f.get_I_total(R0, d)
except:
I_total[i] = None
if I_total[i] > 1e6:
I_total[i] = None
t_max[i] = f.get_t_max(R0, d)
colors = ['red', 'blue', 'green']
series = [t_max, I_total, y]
labels = [
'Projected Outbreak Duration', 'Projected Outbreak Size',
'Cumulative Incidence'
]
fig, ax1 = plt.subplots()
ax1.set_xlabel('Generations Available for Fitting')
ax2 = ax1.twinx()
# ax2.set_ylim(0, 200000)
ax3 = ax1.twinx()
ax3.spines['right'].set_position(('outward', 55))
plt.subplots_adjust(right=0.8)
axes = [ax1, ax2, ax3]
width = 1
for i, s in enumerate(series):
ax = axes[i]
if i == 2:
ax.bar(x - width * 0.5,
s,
width,
color=colors[i],
alpha=0.3)
else:
ax.plot(x, s, color=colors[i])
scatter_tuples = [(n, m) for n, m in zip(x, s)
if m is not None]
xS, sS = zip(*scatter_tuples)
print xS, sS
ax.scatter(xS, sS, color=colors[i], alpha=0.5)
ax.set_ylabel(labels[i], color=colors[i])
for tl in ax.get_yticklabels():
tl.set_color(colors[i])
# p.align_yaxis(ax1, 0, ax2, 0)
if save:
d = self.root + out
Analysis.check_dir(d)
fn = d + 'projected_size_and_duration_%s.png' % (
c.lower().replace(' ', '_'))
plt.savefig(fn)
else:
plt.show()
print c
print 'I total:', I_total[-1]
print 't max:', t_max[-1]