def date_formatter(x, pos):
return (cv.date('2021-01-12') + dt.timedelta(days=x * 7)).strftime('%d-%b')
def test_misc():
sc.heading('Testing miscellaneous functions')
sim_path = 'test_misc.sim'
json_path = 'test_misc.json'
gitinfo_path = 'test_misc.gitinfo'
fig_path = 'test_misc.png'
fig_comments = 'Test comment'
# Data loading
cv.load_data(csv_file)
cv.load_data(xlsx_file)
with pytest.raises(NotImplementedError):
cv.load_data('example_data.unsupported_extension')
with pytest.raises(ValueError):
cv.load_data(xlsx_file, columns=['missing_column'])
# Dates
d1 = cv.date('2020-04-04')
d2 = cv.date(sc.readdate('2020-04-04'))
ds = cv.date('2020-04-04', d2)
assert d1 == d2
assert d2 == ds[0]
with pytest.raises(ValueError):
cv.date([(2020, 4, 4)]) # Raises a TypeError which raises a ValueError
with pytest.raises(ValueError):
cv.date('Not a date')
cv.daydiff('2020-04-04')
# Run sim for more investigations
sim = cv.Sim(pop_size=500, verbose=0)
sim.run()
sim.plot(do_show=False)
# Saving and loading
cv.savefig(fig_path, comments=fig_comments)
cv.save(filename=sim_path, obj=sim)
cv.load(filename=sim_path)
# Version checks
cv.check_version('0.0.0') # Nonsense version
print('↑ Should complain about version')
with pytest.raises(ValueError):
cv.check_version('0.0.0', die=True)
# Git checks
cv.git_info(json_path)
cv.git_info(json_path, check=True)
# Poisson tests
c1 = 5
c2 = 8
for alternative in ['two-sided', 'larger', 'smaller']:
cv.poisson_test(c1, c2, alternative=alternative)
for method in ['score', 'wald', 'sqrt', 'exact-cond']:
cv.poisson_test(c1, c2, method=method)
with pytest.raises(ValueError):
cv.poisson_test(c1, c2, method='not a method')
# Test locations
for location in [None, 'viet-nam']:
cv.data.show_locations(location)
# Test versions
with pytest.raises(ValueError):
cv.check_save_version('1.3.2', die=True)
cv.check_save_version(cv.__version__,
filename=gitinfo_path,
comments='Test')
# Test PNG
try:
metadata = cv.get_png_metadata(fig_path, output=True)
assert metadata['Covasim version'] == cv.__version__
assert metadata['Covasim comments'] == fig_comments
except ImportError as E:
print(
f'Cannot test PNG function since pillow not installed ({str(E)}), skipping'
)
# Tidy up
remove_files(sim_path, json_path, fig_path, gitinfo_path)
return
def test_misc():
sc.heading('Testing miscellaneous functions')
sim_path = 'test_misc.sim'
json_path = 'test_misc.json'
# Data loading
cv.load_data(csv_file)
cv.load_data(xlsx_file)
with pytest.raises(NotImplementedError):
cv.load_data('example_data.unsupported_extension')
with pytest.raises(ValueError):
cv.load_data(xlsx_file, columns=['missing_column'])
# Dates
d1 = cv.date('2020-04-04')
d2 = cv.date(sc.readdate('2020-04-04'))
ds = cv.date('2020-04-04', d2)
assert d1 == d2
assert d2 == ds[0]
with pytest.raises(ValueError):
cv.date([(2020, 4, 4)]) # Raises a TypeError which raises a ValueError
with pytest.raises(ValueError):
cv.date('Not a date')
cv.daydiff('2020-04-04')
# Saving and loading
sim = cv.Sim()
cv.save(filename=sim_path, obj=sim)
cv.load(filename=sim_path)
# Version checks
cv.check_version('0.0.0') # Nonsense version
print('↑ Should complain about version')
with pytest.raises(ValueError):
cv.check_version('0.0.0', die=True)
# Git checks
cv.git_info(json_path)
cv.git_info(json_path, check=True)
# Poisson tests
c1 = 5
c2 = 8
for alternative in ['two-sided', 'larger', 'smaller']:
cv.poisson_test(c1, c2, alternative=alternative)
for method in ['score', 'wald', 'sqrt', 'exact-cond']:
cv.poisson_test(c1, c2, method=method)
with pytest.raises(ValueError):
cv.poisson_test(c1, c2, method='not a method')
# Tidy up
remove_files(sim_path, json_path)
return
pl.figure(figsize=(24, 16))
# Import files
filepaths = [f'{resfolder}/uk_sim_{scen}.obj' for scen in scenarios]
sims = sc.odict()
msims = sc.odict()
for scen in scenarios:
filepath = f'{resfolder}/uk_sim_{scen}.obj'
msims[scen] = sc.loadobj(filepath)
sims[scen] = msims[scen].sims
msims[scen].reduce()
sim = sims[0][0] # Extract a sim to refer to
# Extract weekly infection data
w0, w1, w2, w3, w4, w5, w6, w7 = cv.date('2021-01-10'), cv.date('2021-01-17'), cv.date('2021-01-24'), cv.date('2021-01-31'), \
cv.date('2021-02-07'), cv.date('2021-02-14'), cv.date('2021-02-21'), cv.date('2021-02-28')
wd = [
sim.day(w0),
sim.day(w1),
sim.day(w2),
sim.day(w3),
sim.day(w4),
sim.day(w5),
sim.day(w6),
sim.day(w7)
]
inf_med = []
inf_low = []
inf_high = []
for scen in scenarios:
pl.figure(figsize=(24, 16))
# Import files
filepaths = [f'{resfolder}/uk_sim_{scen}.obj' for scen in scenarios]
sims = sc.odict()
msims = sc.odict()
for scen in scenarios:
filepath = f'{resfolder}/uk_sim_{scen}.obj'
msims[scen] = sc.loadobj(filepath)
sims[scen] = msims[scen].sims
msims[scen].reduce()
sim = sims[0][0] # Extract a sim to refer to
# Extract weekly infection data
w0, w1, w2, w3, w4, w5, w6, w7 = cv.date('2021-03-08'), cv.date('2021-03-15'), cv.date('2021-03-22'), cv.date('2021-03-29'), \
cv.date('2021-04-05'), cv.date('2021-04-12'), cv.date('2021-04-19'), cv.date('2021-04-26')
wd = [
sim.day(w0),
sim.day(w1),
sim.day(w2),
sim.day(w3),
sim.day(w4),
sim.day(w5),
sim.day(w6),
sim.day(w7)
]
inf_med = []
inf_low = []
inf_high = []
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
def date_formatter(x, pos):
return (cv.date('2020-11-15') +
dt.timedelta(days=x)).strftime('%b-%y')
def date_formatter(x, pos):
return (cv.date('2020-09-30') + dt.timedelta(days=x)).strftime('%d-%b')
import numpy as np
import datetime as dt
do_multi = True
do_plot = False
do_save = True
# good fit (nearly) with
total_pop = 20e6
pop_size = 100e3
pop_scale = int(total_pop / pop_size) #2.0
pop_infected = 150
beta = 0.0111
rel_death_prob = 2.
symp_dates = [
cv.date('2020-03-01'),
cv.date('2020-04-01'),
cv.date('2020-05-01'),
cv.date('2020-06-01'),
cv.date('2020-07-01'),
cv.date('2020-08-01'),
cv.date('2020-09-01')
]
#symp_probs = [0.01, 0.02, 0.03, 0.04, 0.06, 0.06, 0.035]#[0.01, 0.015, 0.03, 0.04, 0.04, 0.04, 0.03]
#daily_tests = np.linspace(0,5000,182)
daily_tests = np.concatenate([
np.linspace(0, 30, 31), # March
np.linspace(30, 200, 30), # April
np.linspace(200, 600, 31), # May
np.linspace(600, 1200, 30), # June
np.linspace(1200, 1000, 31), # July