Population whose homes are in flooded areas, Population of the raster cells the hazard footprint touches (grid: 100 m), counted cell by cell inside the area of analysis, each cell by its own journey to care. A cell only GRAZED by the flood contributes its whole population, so this is an upper bound at any resolution, and the coarser the grid, the larger the overstatement. Two tiles: the flooded cells that still have a road route to a facility, and the ones with no facility reachable at all; a cell that never had a facility, or that no cells file answers for, is a third figure, never dropped and never called cut off. Being in the water is the limiting case of losing access, so on the map the second keeps the class red; the first is painted by its travel time with a red ring.
No facility reachable on the mapped road network, outside the flooded area, Cells on dry land with NO health facility reachable on the road network after the hazard cut it. Not 'lost the nearest one', none at all. A cell in the water with lost access is in the row above, not here: every cell is counted once.
Must redirect to a different facility, The nearest reachable facility is a DIFFERENT one than before the event. The travel time may be longer, and the receiving facility may be busier.
Pushed past a care threshold, Could reach a facility within a care threshold (30 minutes, 1 hour or 2 hours) before the event and cannot within that threshold now. The tiles count 100 m cells; the settlement classes in the analysis below use the 30-minute line only.
Facilities serving more people, Their access is affected: the facility they kept takes a patient surge from a closed or unreachable neighbour, so expect longer waits and stretched services. This is a care-quality signal, not a capacity one: the test is that the facility gained ANY patients, so a surge of ten and a surge of thirty thousand read the same here, and nothing in OpenStreetMap gives the beds or staffing to say whether either exceeds what the place can handle. It is also the lowest level, so it counts only those for whom crowding is the ONLY harm, people who are additionally rerouted or delayed are counted above, many of them at these same facilities, which makes this figure a floor. Outside the headline count since the owner's ruling of 2026-11-06: stated on its own line.
"Facilities likely experiencing surge" counts the whole prior catchment and is the one figure on this page that deliberately overlaps the others. The surge line under the tiles is not this chart: it counts, once each, only the people who kept their facility and whose only harm is that it gained patients, and like this chart it is outside the headline (owner ruling, 2026-11-06). Every other number follows people whose own travel time changed; that one follows the people whose travel time did not, same facility, same trip, a waiting room now serving a catchment it did not have last week. It counts the WHOLE prior catchment, so anyone in it who was also rerouted or delayed is already counted above. Two cautions: catchment here means nearest-facility assignment, not registration, so it is where people would go rather than where they are enrolled; and there is no capacity in it, so the same percentage means different things at a referral hospital and at a health post. NOR IS THERE CAPABILITY: facilities come from OpenStreetMap amenity=hospital/clinic, which records no specialism, so a 100 m cell is assigned its nearest facility whether or not that facility treats what it needs. Rows whose name suggests a specialism are marked; none is reassigned. A facility showing NO prior catchment is not a facility that served nobody: catchments are assigned cell by cell to a single nearest facility, so a facility that is the nearest one for no populated cell carries a zero baseline either way. Those are reported separately, with no percentage, and the effect on their existing patients is UNKNOWN rather than zero.
Each person is counted once at their worst level, so the figures add up. A cell is in one level only: one in the flooded area is in the first level whatever happened to its access.
Children and women are counted with each settlement's OWN share. Estimated for each settlement from the Nepal DHS 2022. 26,879 cells of the 26,883 affected cells have an estimate; the other 4 cells are counted at the share of those that do. Shares, not a headcount of identified people.
Settlement population is the population raster summed over the settlement's own polygon, from the GHS-POP grid (2025) at 100 m resolution.
Travel times assume FREE-FLOW speeds, there is no congestion model Every surviving road is routed at its normal speed, even where it now carries traffic diverted from a road that was cut. Nothing slows down: routing runs before the caseload surge is known and the surge never feeds back. The travel times here are therefore CONSERVATIVE. Real journeys on a diverted route will take longer, by an amount this page does not estimate, so the counts of delayed and cut-off people are the fewest the event can account for, not the most. The surge figures show the demand these figures do not carry: a facility with a patient surge of tens of thousands implies the roads to it are carrying them.
A road inside the flood footprint is assumed impassable That is the primary rule: the water extent cuts the network directly. Reported damage to bridges and roads COMPLEMENTS it, it can only add cuts the footprint does not already make, for structures damaged outside the observed water. It never replaces the footprint rule and never removes a cut.
Facilities inside the footprint are treated as not accessible.
Times are DRIVING times on the Overture Maps / OpenStreetMap road network as mapped, with walking where there is no road; public transport, congestion, night driving and weather are not represented.
Routes stay inside Nepal destinations and transit both, except where the only road briefly weaves across the border, which stays open. Care across the border is the separate purple what-if scenario, never part of these numbers.
A destination is any mapped hospital or clinic that could admit an acute casualty animal hospitals and facilities that do not admit an acute general case (eye, dental, skin, physiotherapy, dialysis, fertility, psychiatry, psychology and psychosocial care, ayurvedic, homeopathic, acupuncture and other complementary practice, nutrition, foot care, osteopathy and other non-physician therapy, pharmacies and dispensaries, hospices, diagnostics-only labs and imaging) are excluded by rule, and so are places that treat no one: wellness, beauty, fitness and education businesses, shops, and health premises that receive no patients (ambulance bases, health offices, morgues, blood and milk banks, home-care firms); maternity, children's, polyclinics, rehabilitation and general hospitals and clinics stay in, and a name that suggests a specialism is marked, not reassigned.
People are counted at home GHS population (2025) in its 100 m cells; each populated cell travels from its own position, and displacement is not represented.
Everyone is routed to their NEAREST open facility by network travel time, the standard geographic-access assumption. Real patients bypass for quality, cost or referral, and nothing here represents capacity, so who-goes-where is indicative rather than predictive; the 2nd- and 3rd-nearest views show the alternatives, and a cell counts as having lost access only when no facility is reachable on the mapped network.
The cells are the same method at 100 m Every populated 100 m cell of the population grid is routed on its own to its nearest, 2nd- and 3rd-nearest open facility, before and after the event. The map's hexagons aggregate those cells: each goes to the facility most of its people go to, and carries the population-weighted mean of their travel times, so a hexagon and the tiles can differ: the tiles count each cell on its own. A cell is in the flooded area when its centre falls on mapped water; a hexagon is highlighted when any of its people are.
The timeline shows each date as it was observed Each stop routes on the footprints current at that date, a later version of the same product replacing the earlier one, so the page reports the state at the last observation of each area, not the union of every observation. Cells, settlement classes and travel times follow the stop; routes, facility marks and the tiles describe the latest state. A date that appears in a product's name is the day the satellite looked only when the source says so; a release date is labelled as one.
Cuts combine across footprints A cell that every footprint on its own still leaves a road to can have none once all the cuts apply together. That check runs on the union of cuts and is what the cut-off count uses; where a dated stop has not had it computed, a note under the dial says so.
Three travel-time thresholds 30 minutes, 1 hour and 2 hours. Two hours is the threshold at which WHO's Global Health Observatory reports geographic access to emergency obstetric care. A 2026 systematic review (Roder-DeWan et al., J Glob Health 16:04136) concludes that 30 minutes or one hour is preferable for perinatal survival. All three are shown, so that a reader can use the one their programme works to.
What a facility can do is not mapped The facilities come from OpenStreetMap, which records where a facility is and not what care it can give. Every facility is counted as able to give care, so the facility counts are an upper bound on the care available, emergency obstetric care included.
Health access only This page does not account for the death toll, displacement, access to clean water, power or communications, so it will read LOWER than a humanitarian caseload for the same event. That is a difference of definition, not a disagreement.
WHERE EACH FIGURE COMES FROM.
The flood's extent is OBSERVED: as delineated by the services named in the sources below, and used as published.
Roads and facilities are MAPPED: Overture Maps and OpenStreetMap, as they stood on the dates of the map vintage line.
People per cell come from a PUBLISHED GRID: the GHS-POP grid (2025) at 100 m resolution, as published.
Travel time is COMPUTED: the fastest journey over the mapped network. It was not timed on the ground.
Children, women and the survey's indicators are ESTIMATED FROM THE SURVEY: Estimated for each settlement from the Nepal DHS 2022. 26,879 cells of the 26,883 affected cells have an estimate; the other 4 cells are counted at the share of those that do. Shares, not a headcount of identified people.
NOT on this page: whether a facility is open, staffed or supplied; where people sought care; journeys to anything but a health facility.
