Live Repositories
1 repositoryOne Health Risk & Policy Scenario Modelling
A structured framework for analysing zoonotic and biosecurity risk within a One Health context. Translates biological, environmental, and agricultural variables into a composite risk signal, and evaluates how policy interventions modify that risk — supporting structured, reproducible decision-making under conditions of uncertainty.
This is a conceptual and analytical framework rather than a production-ready system. It focuses on interpretability and structured reasoning, prioritising transparent methodology over predictive complexity. Designed to connect with biological data infrastructure provided by The BioChain.
Model Variables
Livestock density · Wildlife contact · Water quality · Vaccination coverage · Veterinary access · Human exposure · Farm biosecurity
Policy Scenarios
Baseline · Improved biosecurity · Vaccination expansion · Increased veterinary access · Integrated One Health intervention
Outputs
Regional risk scores · Comparative scenario outputs · Visual representations of risk distribution
Methodology
Deterministic scenario-based model · Variables normalised and combined into weighted composite · Interpretability-first design
Related Analysis
Questions & Answers
Is this a predictive model?
No. It is a deterministic, scenario-based framework prioritising interpretability and transparent methodology over predictive complexity — a conceptual and analytical tool rather than a production-ready system.
What variables does the model use?
Seven: livestock density, wildlife contact, water quality, vaccination coverage, veterinary access, human exposure and farm biosecurity, normalised and combined into a weighted composite risk score.
What policy scenarios can it compare?
Five: baseline, improved biosecurity, vaccination expansion, increased veterinary access, and an integrated One Health intervention.
How does this connect to The BioChain?
The framework is designed to connect with biological data infrastructure provided by The BioChain, though the two are separate, independently developed pieces of work.
In Development
1 repositoryOutbreak Simulation Under Uncertainty
A stochastic simulation framework for modelling zoonotic outbreak dynamics under conditions of epidemiological uncertainty. Where the risk modelling framework asks what is the current risk?, this model asks what happens if an outbreak begins? — simulating transmission trajectories, intervention timing, and the effect of incomplete surveillance data on outbreak detection and response.
Designed to complement the R risk modelling framework, taking composite risk scores as inputs and simulating what happens downstream. Stochastic variability, surveillance gaps, and delayed reporting are modelled explicitly — reflecting the conditions under which real-world outbreak response actually operates.
Model Approach
Stochastic simulation · Monte Carlo methods · Branching process models · Uncertainty quantification
Key Questions
Detection lag under incomplete surveillance · Intervention threshold analysis · Cross-species transmission modelling
Integration
Accepts risk scores from the R modelling framework · Links to spatial risk mapping outputs · Compatible with The BioChain data infrastructure
Planned Outputs
Outbreak trajectory distributions · Detection probability curves · Intervention effectiveness comparisons
Related Analysis
Questions & Answers
What question does this model answer that the risk model doesn't?
Where the risk modelling framework asks "what is the current risk?", this model asks "what happens if an outbreak begins?" — simulating transmission trajectories, intervention timing and the effect of incomplete surveillance data on detection and response.
What modelling approach does it use?
Stochastic simulation using Monte Carlo methods and branching process models, with explicit uncertainty quantification rather than single-point estimates.
Is it available yet?
Not yet. It is in active development and the repository will be published on GitHub once the framework reaches a stable state.
How does it relate to the R risk modelling framework?
It is designed to complement it, taking composite risk scores as inputs and simulating what happens downstream, with stochastic variability, surveillance gaps and delayed reporting modelled explicitly.
Planned
1 repositorySpatial Risk Mapping
Geospatial visualisation of composite One Health risk scores across regions, integrating outputs from the risk modelling and outbreak simulation frameworks. Designed to support spatial decision-making, resource allocation, and surveillance prioritisation.
Planned Scope
Geospatial risk visualisation · Regional comparative analysis · Integration with risk scoring outputs
Intended Use
Surveillance prioritisation · Resource allocation · Cross-border risk communication
Questions & Answers
What will this component do?
Geospatial visualisation of composite One Health risk scores across regions, integrating outputs from the risk modelling and outbreak simulation frameworks, to support spatial decision-making, resource allocation and surveillance prioritisation.
When is it starting?
Development begins following the release of the outbreak simulation module — no fixed date has been set.
How does it fit with the other two components?
It is the third and final planned component of the modelling framework: risk modelling establishes current risk, outbreak simulation models what happens if an outbreak begins, and spatial risk mapping would visualise both across regions.