def fixaxis(sim, useSI=True, boxoff=False):
''' Make the plotting more consistent -- add a legend and ensure the axes start at 0 '''
delta = 0.5
pl.legend() # Add legend
sc.setylim() # Rescale y to start at 0
pl.xlim((0, sim['n_days'] + delta))
if boxoff:
sc.boxoff() # Turn off top and right lines
return
def format_ax(ax, sim, key=None):
@ticker.FuncFormatter
def date_formatter(x, pos):
return (sim['start_day'] + dt.timedelta(days=x)).strftime('%b')
ax.xaxis.set_major_formatter(date_formatter)
pl.xlim([0, sim.day(calibration_end)])
sc.boxoff()
return
def format_ax(ax, sim, key=None):
@ticker.FuncFormatter
def date_formatter(x, pos):
return (sim['start_day'] + dt.timedelta(days=x)).strftime('%b-%d')
ax.xaxis.set_major_formatter(date_formatter)
pl.xlim([0, 213])
# pl.xlim([0, sim['n_days']])
sc.boxoff()
return
def format_ax(ax, sim, key=None):
''' Format the axes nicely '''
@ticker.FuncFormatter
def date_formatter(x, pos):
return (sim['start_day'] + dt.timedelta(days=x)).strftime('%b-%d')
ax.xaxis.set_major_formatter(date_formatter)
if key != 'r_eff':
sc.commaticks()
pl.xlim([0, sim['n_days']])
sc.boxoff()
return
def format_axs(axs, key=None):
''' Format axes nicely '''
@ticker.FuncFormatter
def date_formatter(x, pos):
# print(x)
return (refsim['start_day'] + dt.timedelta(days=x)).strftime('%b-%d')
for i, ax in enumerate(axs):
bbox = None if i != 1 else (1.05, 1.05) # Move legend up a bit
day_stride = 21
xmin, xmax = ax.get_xlim()
ax.set_xticks(np.arange(xmin, xmax, day_stride))
ax.xaxis.set_major_formatter(date_formatter)
ax.legend(frameon=False, bbox_to_anchor=bbox)
sc.boxoff(ax=ax)
sc.setylim(ax=ax)
sc.commaticks(ax=ax)
return
X = np.arange(len(x))
XX = X + w - off
# Diagnoses
ax[0] = pl.axes([xl, yb, dx, dy])
c1 = [0.3, 0.3, 0.6] # diags
c2 = [0.6, 0.7, 0.9] #diags
pl.bar(X, pos, width=w, label='Data', facecolor=c1)
pl.bar(XX, mpbest, width=w, label='Model', facecolor=c2)
for i, ix in enumerate(XX):
pl.plot([ix, ix], [mplow[i], mphigh[i]], c='k')
ax[0].set_xticks((X + XX) / 2)
ax[0].set_xticklabels(x)
pl.xlabel('Age')
pl.ylabel('Diagnoses')
sc.boxoff(ax[0])
pl.legend(frameon=False, bbox_to_anchor=(0.3, 0.7))
# Deaths
ax[1] = pl.axes([xl + dx + xm, yb, dx, dy])
c1 = [0.5, 0.0, 0.0] # deaths
c2 = [0.9, 0.4, 0.3] # deaths
pl.bar(X, deaths, width=w, label='Data', facecolor=c1)
pl.bar(XX, mdbest, width=w, label='Model', facecolor=c2)
for i, ix in enumerate(XX):
pl.plot([ix, ix], [mdlow[i], mdhigh[i]], c='k')
ax[1].set_xticks((X + XX) / 2)
ax[1].set_xticklabels(x)
pl.xlabel('Age')
pl.ylabel('Deaths')
sc.boxoff(ax[1])
ms=20,
elinewidth=3,
capsize=0)
box_ax.set_xticks(x - 0.15)
#box_ax.set_xticklabels(labels)
@ticker.FuncFormatter
def date_formatter(x, pos):
return (cv.date('2021-01-12') + dt.timedelta(days=x * 7)).strftime('%d-%b')
box_ax.xaxis.set_major_formatter(date_formatter)
pl.ylabel('Estimated daily infections (000s)')
sc.boxoff(ax=box_ax)
sc.commaticks()
box_ax.legend(frameon=False)
# B. Cumulative total infections
width = 0.8 # the width of the bars
x = [0, 1, 2]
data = np.array([
msims[sn].results['cum_infections'].values[-1] -
msims[sn].results['cum_infections'].values[sim.day('2021-01-04')]
for sn in scenarios
])
bar_ax = pl.axes([xgapl + xgapm + dx1, ygapb, dx2, dy])
for sn, scen in enumerate(scenarios):
bar_ax.bar(x[sn], data[sn] / 1e3, width, color=colors[sn], alpha=1.0)
alpha=0.75,
label='Data')
toplot = plotdict['new_diagnoses'][l][date_inds[0]:date_inds[1]]
pl.plot(tvec, toplot, c=colors[i], label=l, lw=4, alpha=1.0)
#low = plotdict_l['new_diagnoses'][l][date_inds[0]:date_inds[1]]
#high = plotdict_h['new_diagnoses'][l][date_inds[0]:date_inds[1]]
#pl.fill_between(tvec, low, high, facecolor=colors[i], alpha=0.2)
pl.ylabel('Daily new infections')
ax = pl.gca()
ax.set_xticks(datemarks)
cv.date_formatter(start_day=start_day, ax=ax, dateformat=dateformat)
sc.setylim()
sc.commaticks()
pl.legend(frameon=False)
sc.boxoff()
# Plot B: R_eff
pl.subplot(2, 2, 2)
colors = pl.cm.GnBu([0.9, 0.6, 0.3])
for i, l in enumerate(labels):
toplot = plotdict['r_eff'][l][date_inds[0]:date_inds[1]]
pl.plot(tvec, toplot, c=colors[i], label=l, lw=4, alpha=1.0)
low = plotdict_l['r_eff'][l][date_inds[0]:date_inds[1]]
high = plotdict_h['r_eff'][l][date_inds[0]:date_inds[1]]
pl.fill_between(tvec, low, high, facecolor=colors[i], alpha=0.2)
pl.ylabel('R')
pl.axhline(1, linestyle=':', c='k', alpha=0.3)
ax = pl.gca()
ax.set_xticks(datemarks)
cv.date_formatter(start_day=start_day, ax=ax, dateformat=dateformat)
def plot():
fig = pl.figure(num='Fig. 2: Transmission dynamics', figsize=(20,14))
piey, tsy, r3y = 0.68, 0.50, 0.07
piedx, tsdx, r3dx = 0.2, 0.9, 0.25
piedy, tsdy, r3dy = 0.2, 0.47, 0.35
pie1x, pie2x = 0.12, 0.65
tsx = 0.07
dispx, cumx, sympx = tsx, 0.33+tsx, 0.66+tsx
ts_ax = pl.axes([tsx, tsy, tsdx, tsdy])
pie_ax1 = pl.axes([pie1x, piey, piedx, piedy])
pie_ax2 = pl.axes([pie2x, piey, piedx, piedy])
symp_ax = pl.axes([sympx, r3y, r3dx, r3dy])
disp_ax = pl.axes([dispx, r3y, r3dx, r3dy])
cum_ax = pl.axes([cumx, r3y, r3dx, r3dy])
off = 0.06
txtdispx, txtcumx, txtsympx = dispx-off, cumx-off, sympx-off+0.02
tsytxt = tsy+tsdy
r3ytxt = r3y+r3dy
labelsize = 40-wf
pl.figtext(txtdispx, tsytxt, 'a', fontsize=labelsize)
pl.figtext(txtdispx, r3ytxt, 'b', fontsize=labelsize)
pl.figtext(txtcumx, r3ytxt, 'c', fontsize=labelsize)
pl.figtext(txtsympx, r3ytxt, 'd', fontsize=labelsize)
#%% Fig. 2A -- Time series plot
layer_keys = list(sim.layer_keys())
layer_mapping = {k:i for i,k in enumerate(layer_keys)}
n_layers = len(layer_keys)
colors = sc.gridcolors(n_layers)
layer_counts = np.zeros((sim.npts, n_layers))
for source_ind, target_ind in tt.count_transmissions():
dd = tt.detailed[target_ind]
date = dd['date']
layer_num = layer_mapping[dd['layer']]
layer_counts[date, layer_num] += sim.rescale_vec[date]
mar12 = cv.date('2020-03-12')
mar23 = cv.date('2020-03-23')
mar12d = sim.day(mar12)
mar23d = sim.day(mar23)
labels = ['Household', 'School', 'Workplace', 'Community', 'LTCF']
for l in range(n_layers):
ts_ax.plot(sim.datevec, layer_counts[:,l], c=colors[l], lw=3, label=labels[l])
sc.setylim(ax=ts_ax)
sc.boxoff(ax=ts_ax)
ts_ax.set_ylabel('Transmissions per day')
ts_ax.set_xlim([sc.readdate('2020-01-18'), sc.readdate('2020-06-09')])
ts_ax.xaxis.set_major_formatter(mdates.DateFormatter('%b-%d'))
ts_ax.set_xticks([sim.date(d, as_date=True) for d in np.arange(0, sim.day('2020-06-09'), 14)])
ts_ax.legend(frameon=False, bbox_to_anchor=(0.85,0.1))
color = [0.2, 0.2, 0.2]
ts_ax.axvline(mar12, c=color, linestyle='--', alpha=0.4, lw=3)
ts_ax.axvline(mar23, c=color, linestyle='--', alpha=0.4, lw=3)
yl = ts_ax.get_ylim()
labely = yl[1]*1.015
ts_ax.text(mar12, labely, 'Schools close ', color=color, alpha=0.9, style='italic', horizontalalignment='center')
ts_ax.text(mar23, labely, ' Stay-at-home', color=color, alpha=0.9, style='italic', horizontalalignment='center')
#%% Fig. 2A inset -- Pie charts
pre_counts = layer_counts[0:mar12d, :].sum(axis=0)
post_counts = layer_counts[mar23d:, :].sum(axis=0)
pre_counts = pre_counts/pre_counts.sum()*100
post_counts = post_counts/post_counts.sum()*100
lpre = [
f'Household\n{pre_counts[0]:0.1f}%',
f'School\n{pre_counts[1]:0.1f}% ',
f'Workplace\n{pre_counts[2]:0.1f}% ',
f'Community\n{pre_counts[3]:0.1f}%',
f'LTCF\n{pre_counts[4]:0.1f}%',
]
lpost = [
f'Household\n{post_counts[0]:0.1f}%',
f'School\n{post_counts[1]:0.1f}%',
f'Workplace\n{post_counts[2]:0.1f}%',
f'Community\n{post_counts[3]:0.1f}%',
f'LTCF\n{post_counts[4]:0.1f}%',
]
pie_ax1.pie(pre_counts, colors=colors, labels=lpre, **pieargs)
pie_ax2.pie(post_counts, colors=colors, labels=lpost, **pieargs)
pie_ax1.text(0, 1.75, 'Transmissions by layer\nbefore schools closed', style='italic', horizontalalignment='center')
pie_ax2.text(0, 1.75, 'Transmissions by layer\nafter stay-at-home', style='italic', horizontalalignment='center')
#%% Fig. 2B -- histogram by overdispersion
# Process targets
n_targets = tt.count_targets(end_day=mar12)
# Handle bins
max_infections = n_targets.max()
edges = np.arange(0, max_infections+2)
# Analysis
counts = np.histogram(n_targets, edges)[0]
bins = edges[:-1] # Remove last bin since it's an edge
norm_counts = counts/counts.sum()
raw_counts = counts*bins
total_counts = raw_counts/raw_counts.sum()*100
n_bins = len(bins)
index = np.linspace(0, 100, len(n_targets))
sorted_arr = np.sort(n_targets)
sorted_sum = np.cumsum(sorted_arr)
sorted_sum = sorted_sum/sorted_sum.max()*100
change_inds = sc.findinds(np.diff(sorted_arr) != 0)
pl.set_cmap('Spectral_r')
sscolors = sc.vectocolor(n_bins)
width = 1.0
for i in range(n_bins):
disp_ax.bar(bins[i], total_counts[i], width=width, facecolor=sscolors[i])
disp_ax.set_xlabel('Number of transmissions per case')
disp_ax.set_ylabel('Proportion of transmissions (%)')
sc.boxoff()
disp_ax.set_xlim([0.5, 32.5])
disp_ax.set_xticks(np.arange(0, 32.5, 4))
sc.boxoff(ax=disp_ax)
dpie_ax = pl.axes([dispx+0.05, 0.20, 0.2, 0.2])
trans1 = total_counts[1:3].sum()
trans2 = total_counts[3:5].sum()
trans3 = total_counts[5:8].sum()
trans4 = total_counts[8:].sum()
labels = [
f'1-2:\n{trans1:0.0f}%',
f' 3-4:\n {trans2:0.0f}%',
f'5-7: \n{trans3:0.0f}%\n',
f'>7: \n{trans4:0.0f}%\n',
]
dpie_args = sc.mergedicts(pieargs, dict(labeldistance=1.2)) # Slightly smaller label distance
dpie_ax.pie([trans1, trans2, trans3, trans4], labels=labels, colors=sscolors[[0,4,7,12]], **dpie_args)
#%% Fig. 2C -- cumulative distribution function
rev_ind = 100 - index
n_change_inds = len(change_inds)
change_bins = bins[counts>0][1:]
for i in range(n_change_inds):
ib = int(change_bins[i])
ci = change_inds[i]
ici = index[ci]
sci = sorted_sum[ci]
color = sscolors[ib]
if i>0:
cim1 = change_inds[i-1]
icim1 = index[cim1]
scim1 = sorted_sum[cim1]
cum_ax.plot([icim1, ici], [scim1, sci], lw=4, c=color)
cum_ax.scatter([ici], [sci], s=150, zorder=50-i, c=[color], edgecolor='w', linewidth=0.2)
if ib<=6 or ib in [8, 10, 25]:
xoff = 5 - 2*(ib==1) + 3*(ib>=10) + 1*(ib>=20)
yoff = 2*(ib==1)
cum_ax.text(ici-xoff, sci+yoff, ib, fontsize=18-wf, color=color)
cum_ax.set_xlabel('Proportion of primary infections (%)')
cum_ax.set_ylabel('Proportion of transmissions (%)')
xmin = -2
ymin = -2
cum_ax.set_xlim([xmin, 102])
cum_ax.set_ylim([ymin, 102])
sc.boxoff(ax=cum_ax)
# Draw horizontal lines and annotations
ancol1 = [0.2, 0.2, 0.2]
ancol2 = sscolors[0]
ancol3 = sscolors[6]
i01 = sc.findlast(sorted_sum==0)
i20 = sc.findlast(sorted_sum<=20)
i50 = sc.findlast(sorted_sum<=50)
cum_ax.plot([xmin, index[i01]], [0, 0], '--', lw=2, c=ancol1)
cum_ax.plot([xmin, index[i20], index[i20]], [20, 20, ymin], '--', lw=2, c=ancol2)
cum_ax.plot([xmin, index[i50], index[i50]], [50, 50, ymin], '--', lw=2, c=ancol3)
# Compute mean number of transmissions for 80% and 50% thresholds
q80 = sc.findfirst(np.cumsum(total_counts)>20) # Count corresponding to 80% of cumulative infections (100-80)
q50 = sc.findfirst(np.cumsum(total_counts)>50) # Count corresponding to 50% of cumulative infections
n80, n50 = [sum(bins[q:]*norm_counts[q:]/norm_counts[q:].sum()) for q in [q80, q50]]
# Plot annotations
kw = dict(bbox=dict(facecolor='w', alpha=0.9, lw=0), fontsize=20-wf)
cum_ax.text(2, 3, f'{index[i01]:0.0f}% of infections\ndo not transmit', c=ancol1, **kw)
cum_ax.text(8, 23, f'{rev_ind[i20]:0.0f}% of infections cause\n80% of transmissions\n(mean: {n80:0.1f} per infection)', c=ancol2, **kw)
cum_ax.text(14, 53, f'{rev_ind[i50]:0.0f}% of infections cause\n50% of transmissions\n(mean: {n50:0.1f} per infection)', c=ancol3, **kw)
#%% Fig. 2D -- histogram by date of symptom onset
# Calculate
asymp_count = 0
symp_counts = {}
minind = -5
maxind = 15
for _, target_ind in tt.transmissions:
dd = tt.detailed[target_ind]
date = dd['date']
delta = sim.rescale_vec[date] # Increment counts by this much
if dd['s']:
if tt.detailed[dd['source']]['date'] <= date: # Skip dynamical scaling reinfections
sdate = dd['s']['date_symptomatic']
if np.isnan(sdate):
asymp_count += delta
else:
ind = int(date - sdate)
if ind not in symp_counts:
symp_counts[ind] = 0
symp_counts[ind] += delta
# Convert to an array
xax = np.arange(minind-1, maxind+1)
sympcounts = np.zeros(len(xax))
for i,val in symp_counts.items():
if i<minind:
ind = 0
elif i>maxind:
ind = -1
else:
ind = sc.findinds(xax==i)[0]
sympcounts[ind] += val
# Plot
total_count = asymp_count + sympcounts.sum()
sympcounts = sympcounts/total_count*100
presymp = sc.findinds(xax<=0)[-1]
colors = ['#eed15b', '#ee943a', '#c3211a']
asymp_frac = asymp_count/total_count*100
pre_frac = sympcounts[:presymp].sum()
symp_frac = sympcounts[presymp:].sum()
symp_ax.bar(xax[0]-2, asymp_frac, label='Asymptomatic', color=colors[0])
symp_ax.bar(xax[:presymp], sympcounts[:presymp], label='Presymptomatic', color=colors[1])
symp_ax.bar(xax[presymp:], sympcounts[presymp:], label='Symptomatic', color=colors[2])
symp_ax.set_xlabel('Days since symptom onset')
symp_ax.set_ylabel('Proportion of transmissions (%)')
symp_ax.set_xticks([minind-3, 0, 5, 10, maxind])
symp_ax.set_xticklabels(['Asymp.', '0', '5', '10', f'>{maxind}'])
sc.boxoff(ax=symp_ax)
spie_ax = pl.axes([sympx+0.05, 0.20, 0.2, 0.2])
labels = [f'Asymp-\ntomatic\n{asymp_frac:0.0f}%', f' Presymp-\n tomatic\n {pre_frac:0.0f}%', f'Symp-\ntomatic\n{symp_frac:0.0f}%']
spie_ax.pie([asymp_frac, pre_frac, symp_frac], labels=labels, colors=colors, **pieargs)
return fig
date = dd['date']
layer_num = layer_mapping[dd['layer']]
layer_counts[date, layer_num] += sim.rescale_vec[date]
lockdown1 = [sc.readdate('2020-03-23'),sc.readdate('2020-05-31')]
lockdown2 = [sc.readdate('2020-11-05'),sc.readdate('2020-12-03')]
lockdown3 = [sc.readdate('2021-01-04'),sc.readdate('2021-02-08')]
labels = ['Household', 'School', 'Workplace', 'Community']
for l in range(n_layers):
ax.plot(sim.datevec, layer_counts[:,l], c=colors[l], lw=3, label=labels[l])
ax.axvspan(lockdown1[0], lockdown1[1], color='steelblue', alpha=0.2, lw=0)
ax.axvspan(lockdown2[0], lockdown2[1], color='steelblue', alpha=0.2, lw=0)
ax.axvspan(lockdown3[0], lockdown3[1], color='lightblue', alpha=0.2, lw=0)
sc.setylim(ax=ax)
sc.boxoff(ax=ax)
ax.set_ylabel('Transmissions per day')
ax.set_xlim([sc.readdate('2020-01-21'), sc.readdate('2021-03-01')])
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b\n%y'))
datemarks = pl.array([sim.day('2020-02-01'), sim.day('2020-03-01'), sim.day('2020-04-01'), sim.day('2020-05-01'), sim.day('2020-06-01'),
sim.day('2020-07-01'), sim.day('2020-08-01'), sim.day('2020-09-01'), sim.day('2020-10-01'),
sim.day('2020-11-01'), sim.day('2020-12-01'), sim.day('2021-01-01'), sim.day('2021-02-01'), sim.day('2021-03-01')])
ax.set_xticks([sim.date(d, as_date=True) for d in datemarks])
ax.legend(frameon=False)
yl = ax.get_ylim()
labely = yl[1]*1.015
cv.savefig(f'{figsfolder}/fig_trans.png', dpi=100)
def plot(self, which=None, n=None, axsize=None, figsize=None):
'''
Create a bar plot of the top causes of burden. By default, plots the top
10 causes of DALYs.
Version: 2018sep27
'''
# Set labels
titles = {
'dalys': 'Top causes of DALYs',
'deaths': 'Top causes of mortality',
'prevalence': 'Most prevalent conditions'
}
# Handle options
if which is None: which = list(titles.keys())
if n is None: n = 10
if axsize is None: axsize = (0.65, 0.15, 0.3, 0.8)
if figsize is None: figsize = (7, 4)
barw = 0.8
# Pull out data
df = sc.dcp(self.data)
nburdens = df.nrows
colors = sc.gridcolors(nburdens + 2, asarray=True)[2:]
colordict = sc.odict()
for c, cause in enumerate(df[self.colnames['cause']]):
colordict[cause] = colors[c]
# Convert to list
if not isinstance(which, list):
asarray = False
whichlist = sc.promotetolist(which)
else:
asarray = True
whichlist = which
# Loop over each option (may only be one)
figs = []
for which in whichlist:
colname = self.colnames[which]
try:
thistitle = titles[which]
thisxlabel = colname
except Exception as E:
errormsg = '"%s" not found, "which" must be one of %s (%s)' % (
which, ', '.join(list(titles.keys())), str(E))
raise Exception(errormsg)
# Process data
df.sort(col=colname, reverse=True)
topdata = df[:n]
try:
barvals = hp.arr(topdata[colname])
except Exception as E:
for r in range(topdata.nrows):
try:
float(topdata[colname, r])
except Exception as E2:
if topdata[colname, r] in ['', None]:
errormsg = 'For cause "%s", the "%s" value is missing or empty' % (
topdata[self.colnames['cause'], r], colname)
else:
errormsg = 'For cause "%s", could not convert "%s" value "%s" to number: %s' % (
topdata[self.colnames['cause'], r], colname,
topdata[colname, r], str(E2))
raise Exception(errormsg)
errormsg = 'An exception was encountered, but could not be reproduced: %s' % str(
E)
raise Exception(errormsg)
barlabels = topdata[self.colnames['cause']].tolist()
# Figure out the units
largestval = barvals[0]
if largestval > 1e6:
barvals /= 1e6
unitstr = ' (millions)'
elif largestval > 1e3:
barvals /= 1e3
unitstr = ' (thousands)'
else:
unitstr = ''
# Create plot
fig = pl.figure(facecolor='none', figsize=figsize)
ax = fig.add_axes(axsize)
ax.set_facecolor('none')
yaxis = pl.arange(n, 0, -1)
for i in range(n):
thiscause = topdata[self.colnames['cause'], i]
color = colordict[thiscause]
pl.barh(yaxis[i],
barvals[i],
height=barw,
facecolor=color,
edgecolor='none')
ax.set_yticks(pl.arange(10, 0, -1))
ax.set_yticklabels(barlabels)
sc.SIticks(ax=ax, axis='x')
ax.set_xlabel(thisxlabel + unitstr)
ax.set_title(thistitle)
sc.boxoff()
figs.append(fig)
if asarray: return figs
else: return figs[0]
def plot():
fig = pl.figure(num='Fig. 4: Suppression scenarios', figsize=(figw, figh))
rx = 0.07
r1y = 0.74
rdx = 0.26
r1dy = 0.20
rδ = 0.30
r1δy = 0.29
r1ax = {}
for i in range(6):
xi = i % 3
yi = i // 3
r1ax[i] = pl.axes([rx + rδ * xi, r1y - r1δy * yi, rdx, r1dy])
r2y = 0.05
r2dy = rdx * figw / figh # To ensure square
r2ax = {}
for i in range(3):
r2ax[i] = pl.axes([rx + rδ * i, r2y, rdx, r2dy])
cax = pl.axes([0.96, r2y, 0.01, r2dy])
# Labels
lx = 0.015
pl.figtext(lx, r1y + r1dy + 0.02, 'a', fontsize=40)
pl.figtext(lx, r2y + r2dy + 0.02, 'b', fontsize=40)
slopes = sc.objdict().make(keys=df1map.keys(), vals=[])
slopes2 = sc.objdict().make(keys=df1map.keys(), vals=[])
for plotnum, key, label in df1map.enumitems():
cv.set_seed(plotnum)
ax = r1ax[plotnum]
for ei in eis:
theseinds = sc.findinds(~np.isnan(df1[key].values))
if sepinds:
ei_ok = sc.findinds(df1['eind'].values == ei)
theseinds = np.intersect1d(theseinds, ei_ok)
x = xvals[key][theseinds]
rawy = df1[ykey].values[theseinds]
y = rawy / kcpop * 100 if logy else rawy
xm = x.max()
if key in ['testdelay', 'trtime']:
xnoise = 0.01
ynoise = 0.05
else:
xnoise = 0
ynoise = 0
rndx = (np.random.randn(len(x))) * xm * xnoise
rndy = (np.random.randn(len(y))) * ynoise
ax.scatter(x + rndx,
y * (1 + rndy),
alpha=0.2,
c=[cols[ei]],
edgecolor='w')
# Calculate slopes
slopey = np.log(rawy) if logy else rawy
slopex = xvals[key].values[theseinds]
tmp, res = np.polyfit(slopex, slopey, 1, cov=True)
fitm, fitb = tmp
factor = np.exp(fitm * slopepoint[key] +
fitb) / slopedenom[key] * slopex.max()
slope = fitm * factor
slopes[key].append(slope)
# Calculate slopes, method 2 -- used for std calculation
X = sm.add_constant(slopex)
mod = sm.OLS(slopey, X)
res = mod.fit()
conf = res.conf_int(alpha=0.05, cols=None)
best = res.params[1] * factor
high = conf[1, 1] * factor
slopes2[key].append(res)
# Plot fit line
bflx = np.array([x.min(), x.max()])
ploty = np.log10(y) if logy else y
plotm, plotb = np.polyfit(x, ploty, 1)
bfly = plotm * bflx + plotb
if logy:
ax.semilogy(bflx, 10**(bfly), lw=3, c=c2)
else:
ax.plot(bflx, bfly, lw=3, c=c2)
plot_baseinds = False
if plot_baseinds:
default_x = default_xvals[key][baseinds]
default_rawy = df1[ykey].values[baseinds]
default_y = default_rawy / kcpop * 100 if logy else default_rawy
ax.scatter(default_x,
default_y,
marker='x',
alpha=1.0,
c=[cols[i]])
if verbose:
print(
f'Slope for {key:10s}: {np.mean(slopes[key]):0.3f} ± {high-best:0.3f}'
)
sc.boxoff(ax=ax)
ax.yaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
if plotnum in [0, 3]:
if logy:
ax.set_ylabel('Attack rate (%)')
ax.set_yticks((0.01, 0.1, 1.0, 10, 100))
ax.set_yticklabels(('0.01', '0.1', '1.0', '10', '100'))
else:
ax.set_ylabel(r'$R_{e}$')
ax.set_xlabel(xlabelmap[key])
if key in ['iqfactor']:
ax.set_xticks(np.linspace(0, 100, 6))
elif key in ['trprob']:
ax.set_xticks(np.linspace(0, 10, 6))
elif key in ['testprob']:
ax.set_xticks(np.arange(7))
elif key in ['testqprob']:
ax.set_xticks(np.arange(7))
else:
ax.set_xticks(np.arange(8))
if logy:
ax.set_ylim([0.1, 100])
else:
ax.set_ylim([0.7, 1.7])
xl = ax.get_xlim()
if logy:
ypos1 = 150
ypos2 = 40
else:
ypos1 = 1.9
ypos2 = 1.7
xlpos = dict(
iqfactor=0.86,
testprob=0.13,
trprob=0.83,
testqprob=0.86,
testdelay=0.13,
trtime=0.00,
)
if key in ['iqfactor', 'testqprob', 'trprob']:
align = 'right'
else:
align = 'left'
ax.text((xl[0] + xl[1]) / 2,
ypos1,
label,
fontsize=26,
horizontalalignment='center')
ax.text(xlpos[key] * xl[1],
ypos2,
f'{abs(best):0.2f} ± {high-best:0.2f} {slopelabels[key]}',
color=pointcolor,
horizontalalignment=align)
ax.axvline(slopepoint[key],
ymax=0.83,
linestyle='--',
c=pointcolor,
alpha=0.5,
lw=2)
reop = [0.6, 0.8, 1.0]
for ri, r in enumerate(reop):
dfr = df2[df2['reopen'] == r]
im = plot_surface(r2ax[ri], dfr, col=ri, colval=r)
bbox = dict(facecolor='w', alpha=0.0, edgecolor='none')
pointsize = 150
if ri == 0:
dotx = 1900 * 1000 / kcpop
doty = 0.06
r2ax[ri].scatter([dotx], [doty],
c='k',
s=pointsize,
zorder=10,
marker='d')
r2ax[ri].text(dotx * 1.20,
doty * 1.50,
'Estimated\nconditions\non June 1',
bbox=bbox)
if ri == 1:
dotx = 3000 * 1000 / kcpop
doty = 0.20
r2ax[ri].scatter([dotx], [doty],
c=[pointcolor2],
s=pointsize,
zorder=10,
marker='d')
r2ax[ri].text(dotx * 1.10,
doty * 0.20,
'Estimated\nconditions\non July 15',
color=pointcolor2,
bbox=bbox)
if ri == 2:
dotx = 2.80 # 7200*1000/kcpop
doty = 0.70 # 0.66
r2ax[ri].scatter([dotx], [doty],
c=[pointcolor],
s=pointsize,
zorder=10,
marker='d')
r2ax[ri].text(dotx * 1.05,
doty * 1.05,
'High mobility,\nhigh test + trace\nscenario',
color=pointcolor,
bbox=bbox)
cbar = pl.colorbar(im, ticks=np.linspace(0.4, 1.6, 7), cax=cax)
cbar.ax.set_title('$R_{e}$', rotation=0, pad=20, fontsize=24)
return fig
def plot():
# Create the figure
fig = pl.figure(num='Fig. 1: Calibration', figsize=(24, 20))
tx1, ty1 = 0.005, 0.97
tx2, ty2 = 0.545, 0.66
ty3 = 0.34
fsize = 40
pl.figtext(tx1, ty1, 'a', fontsize=fsize)
pl.figtext(tx1, ty2, 'b', fontsize=fsize)
pl.figtext(tx2, ty1, 'c', fontsize=fsize)
pl.figtext(tx2, ty2, 'd', fontsize=fsize)
pl.figtext(tx1, ty3, 'e', fontsize=fsize)
pl.figtext(tx2, ty3, 'f', fontsize=fsize)
#%% Fig. 1A: diagnoses
x0, y0, dx, dy = 0.055, 0.73, 0.47, 0.24
ax1 = pl.axes([x0, y0, dx, dy])
format_ax(ax1, base_sim)
plotter('cum_diagnoses',
sims,
ax1,
calib=True,
label='Model',
ylabel='Cumulative diagnoses')
pl.legend(loc='lower right', frameon=False)
#%% Fig. 1B: deaths
y0b = 0.42
ax2 = pl.axes([x0, y0b, dx, dy])
format_ax(ax2, base_sim)
plotter('cum_deaths',
sims,
ax2,
calib=True,
label='Model',
ylabel='Cumulative deaths')
pl.legend(loc='lower right', frameon=False)
#%% Fig. 1A-B inserts (histograms)
agehists = []
for s, sim in enumerate(sims):
agehist = sim['analyzers'][0]
if s == 0:
age_data = agehist.data
agehists.append(agehist.hists[-1])
# Observed data
x = age_data['age'].values
pos = age_data['cum_diagnoses'].values
death = age_data['cum_deaths'].values
# Model outputs
mposlist = []
mdeathlist = []
for hists in agehists:
mposlist.append(hists['diagnosed'])
mdeathlist.append(hists['dead'])
mposarr = np.array(mposlist)
mdeatharr = np.array(mdeathlist)
low_q = 0.025
high_q = 0.975
mpbest = pl.median(mposarr, axis=0)
mplow = pl.quantile(mposarr, q=low_q, axis=0)
mphigh = pl.quantile(mposarr, q=high_q, axis=0)
mdbest = pl.median(mdeatharr, axis=0)
mdlow = pl.quantile(mdeatharr, q=low_q, axis=0)
mdhigh = pl.quantile(mdeatharr, q=high_q, axis=0)
w = 4
off = 2
# Insets
x0s, y0s, dxs, dys = 0.11, 0.84, 0.17, 0.13
ax1s = pl.axes([x0s, y0s, dxs, dys])
c1 = [0.3, 0.3, 0.6]
c2 = [0.6, 0.7, 0.9]
xx = x + w - off
pl.bar(x - off, pos, width=w, label='Data', facecolor=c1)
pl.bar(xx, mpbest, width=w, label='Model', facecolor=c2)
for i, ix in enumerate(xx):
pl.plot([ix, ix], [mplow[i], mphigh[i]], c='k')
ax1s.set_xticks(np.arange(0, 81, 20))
pl.xlabel('Age')
pl.ylabel('Cases')
sc.boxoff(ax1s)
pl.legend(frameon=False, bbox_to_anchor=(0.7, 1.1))
y0sb = 0.53
ax2s = pl.axes([x0s, y0sb, dxs, dys])
c1 = [0.5, 0.0, 0.0]
c2 = [0.9, 0.4, 0.3]
pl.bar(x - off, death, width=w, label='Data', facecolor=c1)
pl.bar(x + w - off, mdbest, width=w, label='Model', facecolor=c2)
for i, ix in enumerate(xx):
pl.plot([ix, ix], [mdlow[i], mdhigh[i]], c='k')
ax2s.set_xticks(np.arange(0, 81, 20))
pl.xlabel('Age')
pl.ylabel('Deaths')
sc.boxoff(ax2s)
pl.legend(frameon=False)
sc.boxoff(ax1s)
#%% Fig. 1C: infections
x0, dx = 0.60, 0.38
ax3 = pl.axes([x0, y0, dx, dy])
format_ax(ax3, sim)
# Plot SCAN data
pop_size = 2.25e6
scan = pd.read_csv(scan_file)
for i, r in scan.iterrows():
label = "Data" if i == 0 else None
ts = np.mean(sim.day(r['since'], r['to']))
low = r['lower'] * pop_size
high = r['upper'] * pop_size
mean = r['mean'] * pop_size
ax3.plot([ts] * 2, [low, high], alpha=1.0, color='k', zorder=1000)
ax3.plot(ts,
mean,
'o',
markersize=7,
color='k',
alpha=0.5,
label=label,
zorder=1000)
# Plot simulation
plotter('cum_infections',
sims,
ax3,
calib=True,
label='Cumulative\ninfections\n(modeled)',
ylabel='Infections')
plotter('n_infectious',
sims,
ax3,
calib=True,
label='Active\ninfections\n(modeled)',
ylabel='Infections',
flabel=False)
pl.legend(loc='upper left', frameon=False)
pl.ylim([0, 130e3])
plot_intervs(sim)
#%% Fig. 1C: R_eff
ax4 = pl.axes([x0, y0b, dx, dy])
format_ax(ax4, sim, key='r_eff')
plotter('r_eff',
sims,
ax4,
calib=True,
label='$R_{eff}$ (modeled)',
ylabel=r'Effective reproduction number')
pl.axhline(1, linestyle='--', lw=3, c='k', alpha=0.5)
pl.legend(loc='upper right', frameon=False)
plot_intervs(sim)
#%% Fig. 1E
# Do the plotting
pl.subplots_adjust(left=0.04,
right=0.52,
bottom=0.03,
top=0.35,
wspace=0.12,
hspace=0.50)
for i, k in enumerate(keys):
eax = pl.subplot(2, 2, i + 1)
c1 = [0.2, 0.5, 0.8]
c2 = [1.0, 0.5, 0.0]
c3 = [0.1, 0.6, 0.1]
sns.kdeplot(df1[k],
shade=1,
linewidth=3,
label='',
color=c1,
alpha=0.5)
sns.kdeplot(df2[k],
shade=0,
linewidth=3,
label='',
color=c2,
alpha=0.5)
pl.title(mapping[k])
pl.xlabel('')
pl.yticks([])
if not i % 4:
pl.ylabel('Density')
yfactor = 1.3
yl = pl.ylim()
pl.ylim([yl[0], yl[1] * yfactor])
m1 = np.median(df1[k])
m2 = np.median(df2[k])
m1std = df1[k].std()
m2std = df2[k].std()
pl.axvline(m1, c=c1, ymax=0.9, lw=3, linestyle='--')
pl.axvline(m2, c=c2, ymax=0.9, lw=3, linestyle='--')
def fmt(lab, val, std=-1):
if val < 0.1:
valstr = f'{lab} = {val:0.4f}'
elif val < 1.0:
valstr = f'{lab} = {val:0.2f}±{std:0.2f}'
else:
valstr = f'{lab} = {val:0.1f}±{std:0.1f}'
if std < 0:
valstr = valstr.split('±')[0] # Discard STD if not used
return valstr
if k.startswith('bc'):
pl.xlim([0, 100])
elif k == 'beta':
pl.xlim([3, 5])
elif k.startswith('tn'):
pl.xlim([0, 50])
else:
raise Exception(f'Please assign key {k}')
xl = pl.xlim()
xfmap = dict(
beta=0.15,
bc_wc1=0.30,
bc_lf=0.35,
tn=0.55,
)
xf = xfmap[k]
x0 = xl[0] + xf * (xl[1] - xl[0])
ypos1 = yl[1] * 0.97
ypos2 = yl[1] * 0.77
ypos3 = yl[1] * 0.57
if k == 'beta': # Use 2 s.f. instead of 1
pl.text(x0, ypos1, f'M: {m1:0.2f} ± {m1std:0.2f}', c=c1)
pl.text(x0, ypos2, f'N: {m2:0.2f} ± {m2std:0.2f}', c=c2)
pl.text(x0,
ypos3,
rf'$\Delta$: {(m2-m1):0.2f} ± {(m1std+m2std):0.2f}',
c=c3)
else:
pl.text(x0, ypos1, f'M: {m1:0.1f} ± {m1std:0.1f}', c=c1)
pl.text(x0, ypos2, f'N: {m2:0.1f} ± {m2std:0.1f}', c=c2)
pl.text(x0,
ypos3,
rf'$\Delta$: {(m2-m1):0.1f} ± {(m1std+m2std):0.1f}',
c=c3)
sc.boxoff(ax=eax)
#%% Fig. 1F: SafeGraph
x0, y0c, dyc = 0.60, 0.03, 0.30
ax5 = pl.axes([x0, y0c, dx, dyc])
format_ax(ax5, sim, key='r_eff')
fn = safegraph_file
df = pd.read_csv(fn)
week = df['week']
days = sim.day(week.values.tolist())
s = df['p.tot.schools'].values * 100
w = df['p.tot.no.schools'].values * 100
# From Fig. 2
colors = sc.gridcolors(5)
wcolor = colors[3] # Work color/community
scolor = colors[1] # School color
pl.plot(days,
w,
'd-',
c=wcolor,
markersize=15,
lw=3,
alpha=0.9,
label='Workplace and\ncommunity mobility data')
pl.plot(days,
s,
'd-',
c=scolor,
markersize=15,
lw=3,
alpha=0.9,
label='School mobility data')
sc.setylim()
xmin, xmax = ax5.get_xlim()
ax5.set_xticks(np.arange(xmin, xmax, day_stride))
pl.ylabel('Relative mobility (%)')
pl.legend(loc='upper right', frameon=False)
plot_intervs(sim)
return fig
