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ba0fd6b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | #!/usr/bin/env python3
"""Validation & Diagnostic Visualization for iCCM CHW Triage Dataset."""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
SCENARIOS = ['low_burden', 'moderate_burden', 'high_burden']
def load_scenarios(data_dir='data'):
dfs = {}
for sc in SCENARIOS:
path = os.path.join(data_dir, f'iccm_{sc}.csv')
if os.path.exists(path):
dfs[sc] = pd.read_csv(path)
return dfs
def make_report(dfs, output='validation_report.png'):
fig, axes = plt.subplots(4, 2, figsize=(16, 22))
fig.suptitle('iCCM Community Health Worker Triage — Validation Report',
fontsize=16, fontweight='bold', y=0.98)
df = dfs.get('moderate_burden', list(dfs.values())[0])
# Panel 1: True diagnosis distribution
ax = axes[0, 0]
diags = ['malaria', 'pneumonia', 'diarrhoea', 'mixed', 'other_febrile']
colors_d = ['#e74c3c', '#3498db', '#f39c12', '#9b59b6', '#95a5a6']
counts = [((df['true_diagnosis'] == d).sum()) for d in diags]
ax.bar(range(5), counts, color=colors_d)
ax.set_xticks(range(5))
ax.set_xticklabels([d.replace('_', ' ').title() for d in diags], fontsize=8)
for i, v in enumerate(counts):
ax.text(i, v + 30, f'{v/len(df)*100:.1f}%', ha='center', fontsize=9)
ax.set_ylabel('Count')
ax.set_title('True Diagnosis (Moderate Burden)')
# Panel 2: CHW action distribution
ax = axes[0, 1]
actions = df['chw_action'].value_counts()
colors_a = {'treat_at_community': '#2ecc71', 'refer_urgently': '#e74c3c',
'treat_and_refer': '#f39c12', 'refer': '#e67e22'}
ax.bar(range(len(actions)), actions.values,
color=[colors_a.get(a, '#95a5a6') for a in actions.index])
ax.set_xticks(range(len(actions)))
ax.set_xticklabels([a.replace('_', '\n') for a in actions.index], fontsize=7)
for i, v in enumerate(actions.values):
ax.text(i, v + 30, f'{v/len(df)*100:.1f}%', ha='center', fontsize=9)
ax.set_ylabel('Count')
ax.set_title('CHW Action')
# Panel 3: Symptom prevalence
ax = axes[1, 0]
syms = ['fever', 'cough', 'fast_breathing', 'diarrhoea', 'any_danger_sign']
vals = [df[s].mean() * 100 for s in syms]
ax.barh(range(5), vals, color='#3498db', alpha=0.8)
ax.set_yticks(range(5))
ax.set_yticklabels([s.replace('_', ' ').title() for s in syms])
for i, v in enumerate(vals):
ax.text(v + 0.5, i, f'{v:.1f}%', va='center', fontsize=10)
ax.set_xlabel('Prevalence (%)')
ax.set_title('Symptom & Sign Prevalence')
# Panel 4: Treatment given
ax = axes[1, 1]
treatments = ['act_given', 'amoxicillin_given', 'ors_given', 'zinc_given']
t_vals = [df[t].mean() * 100 for t in treatments]
colors_t = ['#e74c3c', '#3498db', '#f39c12', '#2ecc71']
ax.bar(range(4), t_vals, color=colors_t, alpha=0.8)
ax.set_xticks(range(4))
ax.set_xticklabels(['ACT', 'Amoxicillin', 'ORS', 'Zinc'])
for i, v in enumerate(t_vals):
ax.text(i, v + 0.5, f'{v:.1f}%', ha='center', fontsize=10)
ax.set_ylabel('% of all cases')
ax.set_title('Treatment Given')
# Panel 5: Cross-scenario diagnosis rates
ax = axes[2, 0]
x = np.arange(len(diags))
width = 0.25
for i, sc in enumerate(SCENARIOS):
if sc not in dfs:
continue
d = dfs[sc]
rates = [(d['true_diagnosis'] == diag).mean() * 100 for diag in diags]
ax.bar(x + i * width, rates, width, label=sc.replace('_', ' ').title(), alpha=0.8)
ax.set_xticks(x + width)
ax.set_xticklabels([d.replace('_', ' ').title() for d in diags], fontsize=7)
ax.set_ylabel('Prevalence (%)')
ax.set_title('Diagnosis Rates Across Scenarios')
ax.legend(fontsize=8)
# Panel 6: MUAC distribution
ax = axes[2, 1]
ax.hist(df['muac_cm'], bins=50, color='#9b59b6', alpha=0.7, edgecolor='white')
ax.axvline(11.5, color='red', ls='--', lw=1.5, label='SAM <11.5')
ax.axvline(12.5, color='orange', ls='--', lw=1.5, label='MAM <12.5')
ax.set_xlabel('MUAC (cm)')
ax.set_title(f'MUAC Distribution (SAM={( df["nutrition_status"]=="SAM").mean()*100:.1f}%)')
ax.legend(fontsize=8)
# Panel 7: Age distribution by diagnosis
ax = axes[3, 0]
for diag, color in [('malaria', '#e74c3c'), ('pneumonia', '#3498db'), ('diarrhoea', '#f39c12')]:
sub = df[df['true_diagnosis'] == diag]['age_months']
ax.hist(sub, bins=30, alpha=0.5, color=color, label=diag.title(), edgecolor='white')
ax.set_xlabel('Age (months)')
ax.set_title('Age Distribution by Diagnosis')
ax.legend(fontsize=9)
# Panel 8: Referral rate across scenarios
ax = axes[3, 1]
ref_rates = []
ds_rates = []
sam_rates = []
for sc in SCENARIOS:
if sc not in dfs:
continue
d = dfs[sc]
ref_rates.append(np.mean(['refer' in str(a) for a in d['chw_action']]) * 100)
ds_rates.append(d['any_danger_sign'].mean() * 100)
sam_rates.append((d['nutrition_status'] == 'SAM').mean() * 100)
x = np.arange(len(SCENARIOS))
ax.bar(x - 0.2, ref_rates, 0.2, label='Referral Rate', color='#e74c3c', alpha=0.8)
ax.bar(x, ds_rates, 0.2, label='Danger Signs', color='#f39c12', alpha=0.8)
ax.bar(x + 0.2, sam_rates, 0.2, label='SAM', color='#9b59b6', alpha=0.8)
ax.set_xticks(x)
ax.set_xticklabels([s.replace('_', '\n').title() for s in SCENARIOS], fontsize=8)
ax.set_ylabel('%')
ax.set_title('Referral, Danger Signs, SAM Across Scenarios')
ax.legend(fontsize=8)
plt.tight_layout(rect=[0, 0, 1, 0.97])
plt.savefig(output, dpi=150, bbox_inches='tight')
print(f'Saved validation report to {output}')
plt.close()
if __name__ == '__main__':
dfs = load_scenarios()
if dfs:
make_report(dfs)
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