WatchActive

Disruption index anomaly in France

🇫🇷 France

Onset: 2026-07 · First detected: 2026-07-06T14:02:21.900813+00:00 · Last observed: 2026-07-12T00:00:51.613701+00:00

Generated at: 2026-07-12T00:00:51.613701+00:00 · The radar runs every 6 hours; data may lag by up to 6 hours.

Plain-language brief

What happened, who is affected, what similar past episodes looked like and what to do — every number links to its source.

What happened

ObservedIn 2026-07, the combined disruption picture for France moved well outside its usual range — rated watch — the lowest of our three alert levels.[Composite Disruption Index (monthly methodology)]
ObservedThe reading behind this alert: index at 49 (the full technical detail is in the sections below).[Composite Disruption Index (monthly methodology)]

Who is affected

ObservedHotels, travel managers, OTAs and tour operators with guests in France during the event window are the most directly affected, together with travellers holding bookings there.[Composite Disruption Index (monthly methodology)]

What similar past episodes looked like

Historical associationIn similar past high-disruption months, overnight stays in France averaged 19.4% below normal calm-month levels — a historical association from our published research panel, not a prediction.[estimates.historical_correlation]
Historical associationLooking at the 3 most similar months in our 2014-2026 research panel, overnight stays one month later differed from a year earlier by a median of +7.2% — again a historical association, not a prediction of this event.[estimates.analogues]

What to do now

RecommendationFor now, keep an eye on the situation in France; no change to bookings is needed yet.[detection.observed]

Impact

index	49.01
components	{"aviation":0,"disaster":63.24,"health":0,"rail":0,"weather":0}

Historical monthly context — not current conditionsAs of: 2026-07 · Disruption index: 49.01 · 0 months older than this event

How this was detected

Rule: monthly_index_anomaly

Method: deterministic-rule

Thresholds

index_watch	25
index_severe	50
z_min	2
z_floor_index	10

Observed

month	2026-07
disruption_index	49.01
components	{"aviation":0,"disaster":63.24,"health":0,"rail":0,"weather":0}

Baseline

n_points	139
window_hours	—
mean	49.83
std	23.25

Insufficient baseline — absolute threshold only.

Sources

Composite Disruption Index (monthly methodology)snapshotcollected: 2026-07-06

Corroborating evidence

Corroboration not applicable: live snapshots describe current conditions, not the period of this event.

Estimates

Estimates are labeled model outputs derived from published historical artifacts — they are context, not forecasts.

Model output — historical correlation estimatehistorical-correlation

r_nights	0.38
r_arrivals	0.36
best_lag_months	6
best_lag_r	-0.39
baseline_deviation_pct	-19.38
n_months	136

Caveats:

correlation is association, not causation
baseline_deviation_pct is the observed average nights deviation during historical high-disruption months (index > 50) vs calm months
coefficients are dominated by the 2020-2021 pandemic period and may overstate the impact of smaller disruptions
computed on monthly national aggregates; sub-national and short-lived events are diluted

Observed history — analogue episodes, not a predictionanalogue-episodesindex_equiv = 25

Episode	score	index	h0	h1	h2	h3
🇫🇷 France · 2023-03	1.461	82.35	4.48	7.20	9.43	-0.13
🇫🇷 France · 2016-03	1.457	79	6.37	-5.80	-7.11	-4.05
🇫🇷 France · 2023-04	1.449	83.11	7.20	9.43	-0.13	-2.01

median_h1	7.20
median_h2	-0.13
median_h3	-2.01

Caveats:

analogues are historically similar months, not a forecast of this event
similarity is computed on a 10-feature panel embedding (tourism volumes, disruption/stringency indices, seasonality); it ignores event semantics
the index_equiv mapping from severity to disruption-index points is a disclosed modelling assumption, not an observation
observed_nights_yoy_pct mixes the event effect with everything else that happened in those months

Recommendations

Monitor the situation in France and re-check this report before confirming new bookings.
based on: detection.observed

For AI agents

Machine-readable JSON for this page:

/data/api/v1/events/ev-monthly-anomaly-fr-20260701-d91c2e.json

Add as an MCP server (from the repo root):

claude mcp add travel-trends -- .venv/bin/python -m etl.mcp_server

API documentation→