Climate change and the future of health and care service delivery in Wales: Building resilience to rising sea levels & flooding

The Art and Science of Uncertainty and the Future | DL4SG Summer School, Cardiff, July 2026

Amirhossein Ghadiri, PhD Student

Data Lab for Social Good, Cardiff Business School, Cardiff University

Prof. Bahman Rostami-Tabar, Lead supervisor

Data Lab for Social Good, Cardiff Business School, Cardiff University

Dr. Thomas Woolley, Co-supervisor

School of Mathematics, Cardiff University

Dr. William Bennett, Co-supervisor

School of Engineering, University of Swansea

Monmouth, November 2024

A community hospital takes on water. Staff move every patient up to the first floor and wait there. Their access is cut off until the water level drops.

On the city’s main street, a pharmacy, a dentist, and a GP surgery all flood at once.

The water never reaches the electrical substations, but they fail anyway. Power is gone for up to 48 hours, and people on home oxygen and dialysis machines, well outside the flooded streets, are suddenly at risk.

This is what flooding disruption to healthcare actually looks like. My PhD is about understanding it, measuring it, and planning for it.

About 75 seconds. Open slowly, tell it as a story. This is a real Welsh case, Storm Bert in Monmouth, from Public Health Wales. Useful follow-on fact for questions: the GP surgery, Castlegate, was displaced to another site for over six months. The point to land: the damage is rarely just the flooded building, it is the cascade. Hold the last line, then move to the agenda. This audience is here for uncertainty, so the story is the setup for the framing that follows.

What I will cover

1. The problem
2. Interconnected research directrions
3. WP1: mapping how floods disrupt care
4. WP2 and WP3: measure it, then plan for it
5. Key Takeaways

15 seconds. One line each, then move. The agenda comes back once, before part four, so the room always knows where we are.

1. The problem

The question behind everything

Floods and rising sea levels keep disrupting how healthcare is delivered in Wales. We map where the water goes, but we do not map what stops working.

• Roughly one in eight properties in Wales sits at flood risk, and the climate signal points to wetter winters and higher seas ¹
• There is no central record of how floods disrupt healthcare delivery, who it hits, or for how long ²

How do floods disrupt healthcare service delivery, and what can we do about it?

About 1 minute. Establish the gap clearly for a mixed audience: flood maps tell us which buildings get wet, and say nothing about which services fail and for whom. That blind spot is the service layer, and it is where the whole PhD sits. Read the research question out loud.

1. Natural Resources Wales

2. Public Health Wales

2. Interconnected research directrions

Three connected packages, one thread

Each package feeds the next. The wp1 defines what the models need to consider and measure, and the current vulnerability defines what the future planning adapts.

About 1 minute. This is the spine of the talk. WP1 establishes what actually happens, because right now the evidence itself is missing. WP2 turns those mechanisms into measurement: which facilities are vulnerable, and what a flood causally does to demand. WP3 takes today’s picture into futures we cannot assign probabilities to, and plans under that. The arrows are the argument: you cannot model vulnerability well until the qualitative work tells you what to model, and you cannot plan for the future without a defensible baseline. I will spend most time on WP1, since it is furthest along, then sketch the other two.

3. WP1: mapping how floods disrupt care

Disruption propagates, it does not stay put

Healthcare delivery is a system of interdependent parts. A flood is a shock that propagates through them.

A facility can stay completely dry and still stop delivering care. Monmouth proved it.

About 1 minute, and this is the conceptual spine of WP1, distilled from a structured synthesis of about 70 studies across more than 20 countries. Walk it left to right. Layer A is the flood. It knocks out the infrastructure care leans on, B: power, water, transport, IT. That hits the facility and its workforce, C: closures, and staff who cannot get in. That changes demand, D: who needs care and whether they can reach it. The band underneath, X, is the interesting part: failures cascade across layers, evolve over hours to months, and reach facilities that never flood, through a severed road or a lost substation. On current evidence the dominant pathways are access, power, and workforce, not the flooded building. That is exactly the Monmouth story from the opening, and the whole reason this needs more than a flood map.

Two methods, one picture

WP1 combines computational reach with qualitative depth.

The text gives breadth and the what. The interviews give the why and the cascades. Together they triangulate.

About 1 minute. Newspapers, reports, and parliamentary records tell you what happened but rarely why, and cascading effects mostly surface when you talk to the people who ran the response. So the NLP is one input into a case-study design, not the work package on its own. Four contrasting Welsh cases anchor the qualitative side: Monmouth, Rhondda Cynon Taf, Newport and the Gwent Levels, and Tenby, each a different flood mechanism and population. The final step quantifies effect sizes, anchored where possible in records rather than expert opinion alone.

The pipeline reads the world’s flood record. Getting from raw text to quantified pathways

Every coded pathway traces back to the sentence it came from.

About 75 seconds. Walk the picture left to right, briefly: ingest the multilingual sources, clean out duplicates and off-topic bleed, chunk and embed everything in one shared space so English and Welsh retrieve each other, reconcile inductive topics against the taxonomy, then the language model codes each passage to the pathway typology, events get resolved across documents, and reported magnitudes are pooled. The dashed box is the one stage that needs serious compute, and it scales to the university supercomputer next. Then the three facts: the corpus, the learned filter, and the first complete run. Every coded pathway traces back to the sentence it came from, which is what makes the whole thing auditable.

What the engagement taught me

The most valuable insights came from people, not papers.

• The damage travels: a flooded GP surgery displaces a screening service, which then displaces something else
• A hospital can be unreachable without being flooded, when its access road or power goes
• Vulnerability is uneven: isolated people, and those on home medical equipment, carry the most risk

These are exactly the mechanisms a flood map cannot show, and the reason WP1 comes first.

About 1 minute. Over nine months I have engaged more than 18 Welsh organisations: Public Health Wales, health boards, Natural Resources Wales, councils and Public Services Boards. This slide is the payoff. The Monmouth cascade, the hospital-as-island examples, the power-cut risk to home-care patients. None of it shows up in a hazard overlay. It is why partners said they would not trust models that skipped this step, and why the qualitative package grounds the whole PhD.

Where we are

1. The problem
2. Interconnected research directrions
3. WP1: mapping how floods disrupt care
4. WP2 and WP3: measure it, then plan for it
5. Key Takeaways

10 seconds. WP1 defines the mechanisms. Now, briefly, what the next two packages do with them.

4. WP2 and WP3: measure it, then plan for it

WP2: which facilities fail, and how demand shifts

A spatio-temporal model that scores healthcare facilities and tracks how floods change demand.

• Inputs: flood hazard, road accessibility, facility characteristics, population context
• A vulnerability score for each facility that varies across space and time
• How demand moves during and after a flood, using interrupted time series
• Causal machine learning to estimate effect sizes rather than mere correlation

About 1 minute. WP2 is where WP1’s mechanisms become measurement. It is a prediction model, spatial and temporal, and the staff dimension came straight from the qualitative work: a facility is only as available as the people who can reach it. The uncertainty here is attribution: a demand spike after a flood is not proof the flood caused it, so the toolkit is causal, interrupted time series and double machine learning, to separate effect from coincidence. Partners repeatedly said the actionable output is which factors drive vulnerability, for example a single access road, more than any composite number.

WP3: planning resilience under uncertainty

How healthcare delivery stays functional as the climate shifts, and how scarce resources are planned for floods.

• Scenario-based exposure of Welsh healthcare assets under RCP 4.5, RCP 8.5, 2 and 4 degrees of warming
• Adaptation woven into routine investment, not separate adaptation investment
• Stochastic and robust optimisation, since the future is uncertain by nature

About 1 minute. WP3 is the future-facing package and the deepest uncertainty in the project. There is no way to validate a 30-year projection today, so this is closer to foresight than prediction: you plan across plausible futures rather than forecasting one. Stochastic programming handles the uncertainty you can put probabilities on; robust optimisation handles the uncertainty you cannot. The framing was reshaped by partners: a real-time resource model has no live data to run on in a real event, so the viable, useful version is offline scenario-based preparedness, which is also what the Civil Contingencies Act duty to plan actually asks for.

5. Key Takeaways

If you take three things away

1. WP1 is the foundation, and it is being carried out
2. The work is grounded in real Welsh cases, not in the literature alone, with interconnected research directrions: evidence, cause, and the future
3. Built with a live network of Welsh partners, so the outputs are plans and tools health practitioners can actually use

About 40 seconds. Slow down for these three. The gap, the intellectual structure, and the route to practice. Then hand over for questions. The backup slides cover the coding scheme, WP2 and WP3 in depth, the partner network, and the Welsh data landscape.

Thank you 💬

Questions and discussion welcome.

Amirhossein Ghadiri

Data Lab for Social Good, Cardiff Business School

Supervised by Bahman Rostami-Tabar, Thomas Woolley, and William Bennett | Funded by WGSSS (ESRC)

GhadiriA@cardiff.ac.uk

amirhosseinghdv | amirhossein-ghadiri | 🌐 amirhosseinghdv.github.io

Slides available at: amirhosseinghdv.github.io

Finish under time. Backups: the coding scheme, WP2 depth, WP3 depth, the full partner network, and the Welsh data landscape.

The chain becomes a coding scheme

	Family	What it covers
A	Hazard trigger	fluvial, surface water, coastal, storm surge, sea-level rise
B	Infrastructure failure	power, water, transport, ICT lifelines
C	Facility and workforce disruption	closures, evacuation, staff unable to work
D	Demand-side disruption	access barriers, displaced and surging demand
X	Cross-cutting cascades	failures that jump between families

WP2 in more depth

A spatio-temporal vulnerability score for Welsh healthcare facilities.

• Hazard and exposure drivers from NRW flood extents and the Communities at Risk Register
• A staff dimension: staff home location and transport access, not just facility characteristics
• Outcome data (closures, cancellations, attendance drops) is interview and record derived, since it is not held centrally
• Attribution by interrupted time series: a baseline, then the drop that coincides with the flood
• Section 19 flood investigation reports as structured post-event inputs

WP3 in more depth

Scenario-based planning where the future is uncertain by nature.

• Asset exposure of Welsh healthcare facilities under RCP 4.5, RCP 8.5, and 2 and 4 degrees of warming
• Adaptation framed as resilience woven into routine investment, rather than relocation
• Resource and preparedness planning run offline across scenarios, aligned with the Civil Contingencies Act duty to plan ahead
• Two-stage and multi-stage stochastic programming, plus robust optimisation
• Communicating uncertainty to decision-makers as a first-class output

Full partner network

Organisation	Contacts	Contribution
Public Health Wales	Huw Williams, Tracy Evans, Behrooz Behbood, Jessica Stone	Cases, risk data, EPRR introductions
Cardiff and Vale UHB	Arjun Padmavathy, Tom Porter	Staff flood survey, scenario data, attribution advice
Aneurin Bevan UHB, Gwent	Wendy Warren, Mo, Shireen	Monmouth response access and interviews
Natural Resources Wales	Luke	Flood layers, lived-experience route
Cardiff PSB and Council	Abigail Streeter, Simon Dooley	Climate risk assessment, flood strategy, Section 19 reports
Monmouthshire and Newport	Catrin Maby, Ian Martin	Castlegate case detail, civil contingencies interviews
Other health boards and LRFs	Haley Barrow, Gemma Hobson, LA emergency planning leads	EPRR links, scope guidance, local response

Welsh data: what we can and cannot get

Why proxies and text mining, not a single clean dataset.

• No central record of healthcare disruption from flooding: closures and cancellations sit behind fragmented FOI requests
• Detailed but restricted health data in SAIL, costly and slow to access
• Strong open data for flood hazard (NRW), population (ONS), and transport (OS, OSM)
• The Senedd record and council transcripts add an unusual public, bilingual text source
• Activity drops and prescribing data are practical proxies for disruption