Transmission dynamics
SEIR, agent-based, and metapopulation models for outbreak response and scenario planning.
Medilytics helps public-health teams, pharma, and research make the calls that hinge on population data — from how many vaccine doses a region needs to how an outbreak will spread. We don't rely on one model: we bring together a family of them, validated against your own data, with every assumption shown.
The point of a model isn't the model — it's the call you make because of it. Here's what a Medilytics model puts within reach.
Forecast vaccine demand by cohort and region before the season starts — not a round number, a defensible one.
Project transmission weeks ahead, with the uncertainty made explicit, so you can plan for the curve before it bends.
Model immunity and waning over time, so timing and boosters are set by evidence rather than the calendar.
Analysis prepared to stand up to scrutiny — reproducible, documented, and ready for peer or regulatory review.
Vaccines are where we start — but the same modelling engine reaches across public health, pharma, and payer decisions.
SEIR, agent-based, and metapopulation models for outbreak response and scenario planning.
Forecast demand and coverage across age cohorts, regions, and rollout schedules.
Model how protection rises and decays over time from trial and real-world data.
Survival, efficacy, and subgroup analysis prepared to stand up in front of a regulator.
Turn routine and registry health data into population-level signal you can act on.
Compare interventions side by side, with costs and uncertainty, before committing budget.
The strongest forecasts don't come from a single model — they come from several, compared. We bring the best of them together and apply them, validated, to the decision in front of you.
We run a family of models — our own and the best from research and public health — and show where they agree, and where they don't.
Models run inside your secure environment, on your own data. Only aggregate results leave — aligned to UK GDPR and information governance.
We work alongside academic and public-health modellers, bringing rigorous, defensible methods to the decisions regional teams actually face.
We start from the decision the model has to feed — not the data we happen to have.
Ingest, clean, and document every source, so the inputs are traceable later.
Fit, calibrate, and stress-test against held-out data and alternative structures.
Quantify uncertainty and show where the model breaks — not only where it works.
Reproducible code, documented assumptions, and a readout your board can actually read.
Most models are black boxes. Ours are built to be questioned — because a number you can't defend is a number you can't use.
Every assumption is documented and handed over with the model — nothing hidden.
Every projection ships with its confidence band. You see the range, not just a point.
Code and data lineage travel with the result, so anyone can re-run it.
Tested against held-out data and prior seasons before anyone relies on it.
We model only on anonymised data supplied by health bodies — identifiers never reach us. Aligned to UK GDPR and information governance.
The clearest view of what happens next.
We're building the modelling partner that public health, pharma, and research reach for when the answer has to be right — and has to hold up when someone checks.
We're building Medilytics with a small group of founding partners — health teams with a decision to make, and modellers who want their work put to use. Tell us which you are, and what you're trying to answer.