+
+
+
+
+
+
+ {message}
+{message}
+ {onRetry && ( + + )} +{subtitle}
+ )} ++ Parametric insurance triggers for urban workers in East Africa +
++ NASA POWER grid temperatures corrected with ML urban heat island model per zone +
++ 7-day trigger probability forecasts with model confidence scoring per zone +
++ Actuarial pricing and budget allocation across zones with priority-ranked coverage +
+| Run ID | +Date | +Status | +Duration | +Zones | +Triggers | +Notifications | +Cost (USD) | +
|---|---|---|---|---|---|---|---|
| {run.run_id} | +{new Date(run.started_at).toLocaleDateString()} | +{run.duration_s.toFixed(0)}s | +{run.zones_processed} | +{run.triggers_found} | +{run.notifications_sent} | +${run.total_cost_usd.toFixed(4)} | +
+ Temperature, WBGT, and heat index tracking across all zones +
+| Zone | +Temp (°C) | +WBGT (°C) | +Heat Index | +Consecutive Hot Days | +7-Day Trigger | +Confidence | +Model | +Risk Level | +
|---|---|---|---|---|---|---|---|---|
|
+ {idx.zone_name}
+ {idx.city}
+ |
+ + + {idx.temp_current.toFixed(1)} + + | ++ + {idx.wbgt_current.toFixed(1)} + + | +{idx.heat_index_current.toFixed(1)} | +{idx.consecutive_hot_days} | +
+
+
+
+
+
+
+ {pct?.toFixed(0)}%
+
+ |
+ {((idx.prediction_confidence ?? 0) * 100).toFixed(0)}% | ++ + {tierLabel} + + | +
+ Current:{' '} + + {selectedData?.temp_current.toFixed(1)}°C + + {' / WBGT: '} + + {selectedData?.wbgt_current.toFixed(1)}°C + +
++ Worker heat alerts sent via SMS and WhatsApp in English and Swahili +
+{n.city}
++ {n.message_preview} +
+ +{s.table}
+ + This pipeline is designed to be forked and adapted for any region. The architecture separates + globally portable components (NASA POWER ingestion, WBGT calculation, Claude healing, notification delivery) + from region-specific configuration (zone definitions, temperature thresholds, payout tiers, knowledge base). +
++ To adapt for a new region, you need to customize these files: +
+{item.file}
+ {item.desc}
+{item.file}
+ {item.desc}
++ 6-step automated pipeline: ingest, heal, index, calibrate, explain, notify +
++ Total Pipeline Cost +
++ ${s?.total_cost_usd?.toFixed(2)} +
++ Average per Run +
++ ${s?.avg_cost_per_run_usd?.toFixed(4)} +
++ Last Run +
++ {s?.last_run ? new Date(s.last_run).toLocaleDateString() : '--'} +
+| + | Run ID | +Date | +Status | +Duration | +Zones | +Triggers | +Notifications | +Cost (USD) | +
|---|---|---|---|---|---|---|---|---|
|
+ {expandedRun === run.run_id ? (
+ |
+ {run.run_id} | +{new Date(run.started_at).toLocaleDateString()} | +{run.duration_s.toFixed(0)}s | +{run.zones_processed} | +{run.triggers_found} | +{run.notifications_sent} | +${run.total_cost_usd.toFixed(4)} | +|
|
+
+
+ + Pipeline Steps + +
+ {run.steps.map((step, i) => (
+
+
+
+ ))}
+
+ Step {i + 1}
+
+
+ {STEP_LABELS[step.step] ?? step.step}
+
+
+ {step.duration_s.toFixed(1)}s
+
+ |
+
+ Set budget, payout, and thresholds. The model prices each zone actuarially and allocates budget by priority. +
+| Rank | +Zone | +City | +Actuarial Cost/Worker | +Allocated Budget | +Workers Covered | +Coverage % | +Events/Year | +Basis Risk | +
|---|---|---|---|---|---|---|---|---|
| + + {z.priority_rank ?? '--'} + + | +{z.zone_name} | +{z.city} | +${z.actuarial_cost_per_worker?.toFixed(2) ?? '--'} | +{formatUsd(z.allocated_budget)} | +{z.workers_covered?.toLocaleString() ?? '0'} | ++ = 80 ? '#2a9d8f' : (z.coverage_pct ?? 0) >= 40 ? '#d4a019' : '#e63946', + }} + > + {z.coverage_pct?.toFixed(1) ?? '0'}% + + | +{z.events_per_year?.toFixed(1)} | ++ 0.3 ? '#e63946' : z.basis_risk_score > 0.2 ? '#e67e22' : '#2a9d8f', + }} + > + {z.basis_risk_score?.toFixed(3)} + + | +
+ 20 heat-vulnerable urban zones across East African cities +
+| Zone | +City | +Settlement | +Heat Vulnerability | +Workers | +Grid Temp | +UHI | +Corrected | +WBGT (°C) | +7d Trigger | +Risk Level | +
|---|---|---|---|---|---|---|---|---|---|---|
| {z.name} | +{z.city} | +{z.settlement_type} | +{z.worker_population_est.toLocaleString()} | ++ + {z.grid_temp_c?.toFixed(1)}°C + + | ++ 4 ? '#e63946' : (z.uhi_delta_c ?? 0) > 2 ? '#e67e22' : '#d4a019' }} + > + +{z.uhi_delta_c?.toFixed(1)}°C + + | ++ + {z.corrected_temp_c?.toFixed(1)}°C + + | ++ + {z.current_wbgt_c.toFixed(1)} + + | +
+ {(() => {
+ const pct = (z.trigger_probability_7d ?? 0) * 100
+ const color = pct > 70 ? '#e63946' : pct > 50 ? '#e67e22' : pct > 20 ? '#d4a019' : '#2a9d8f'
+ return (
+
+
+ )
+ })()}
+
+
+
+
+ {pct?.toFixed(0)}%
+
+ |
+
| Zone | +City | +Worker Population | +Outdoor Exposure | +Heat Vulnerability | +Enrolled Workers | +Enrollment Rate | +
|---|---|---|---|---|---|---|
| {z.name} | +{z.city} | +{z.worker_population_est.toLocaleString()} | +{z.outdoor_exposure_pct}% | +{enrolledCount.toLocaleString()} | ++ + {enrollmentRate}% + + | +