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1 repository
R ● Live Open Source

One 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

In Development

1 repository
In active development — repository coming soon
Python ◐ In Development

Outbreak 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

Planned

1 repository
Planned — development begins following outbreak simulation release
TBC ○ Planned

Spatial 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

01

Risk Modelling

Multi-domain composite risk scoring across livestock, wildlife, environment and human exposure — deterministic, interpretable, policy-facing.

02

Outbreak Simulation

Stochastic transmission modelling under surveillance uncertainty — what happens if an outbreak begins, and how quickly can it be detected.

03

Spatial Risk Mapping

Geospatial visualisation of risk outputs — supporting regional surveillance prioritisation and cross-border risk communication.

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