Disease Outbreak Prevention

Experience level: Intermediate
Reasoning types: Graph
Industry: Healthcare
Tags: graph-analyticsdegree-centralitypublic-health

What this template is for

During a disease outbreak, public health officials must quickly decide where to deploy limited resources like vaccines, testing equipment, and emergency response teams. This template demonstrates how to use weighted degree centrality — a graph algorithm that combines connectivity with transmission risk metrics — to identify the most strategically important healthcare facilities.

By analyzing a network of hospitals, clinics, testing centers, and community organizations with weighted connections based on patient transfer volumes and contact intensity, this template helps you prioritize facilities that pose the greatest cumulative risk. These high-risk facilities act as critical hubs in the health network, making them ideal locations for maximum resource reach and rapid outbreak containment during an outbreak response.

Who this is for

Intermediate users who want to learn weighted degree centrality with a real-world epidemiological use case
Data scientists new to RelationalAI looking for a simple graph analytics example
Public health analysts planning outbreak response strategies
Healthcare network planners optimizing resource allocation

What you’ll build

Load a public health network with 10 facilities and 15 directed connections with transfer volume and contact intensity metrics
Use RelationalAI’s Graph API to model the healthcare network with weighted edges
Calculate risk-weighted degree centrality for each facility based on connection weights (transfer_volume × contact_intensity)
Track incoming and outgoing connections (indegree and outdegree)
Rank facilities by their cumulative risk (weighted degree centrality scores)
Identify top priority facilities for resource deployment
Generate a detailed prioritized list to guide outbreak response decisions

This template uses RelationalAI’s graph modeling capabilities with the Graph class to represent the network, and built-in weighted graph algorithms to compute degree centrality that accounts for transmission risk factors.

What’s included

Shared model setup: model_setup.py - Common model configuration and graph creation (used by both scripts)
Command-line script: disease_outbreak_prevention_network.py - CLI analysis script with detailed output
Interactive app: app.py - Streamlit web application with visualizations and interactive analysis
Data: data/facilities.csv and data/connections.csv

Prerequisites

Python >= 3.10
A Snowflake account that has the RAI Native App installed.
A Snowflake user with permissions to access the RAI Native App.

Quickstart

Follow these steps to run the template with the included sample data. You can customize the data and model as needed after you have it running end-to-end.

Download the ZIP file for this template and extract it:
Terminal window
```
curl -O https://docs.relational.ai/templates/zips/v1/disease-outbreak-prevention.zip
unzip disease_outbreak_prevention_network.zip
cd disease_outbreak_prevention_network
```
You can also download the template ZIP using the “Download ZIP” button at the top of this page.

Create and activate a virtual environment

python -m venv .venv
source .venv/bin/activate
python -m pip install -U pip

Install dependencies

From this folder:
Terminal window
```
python -m pip install .
```
Configure Snowflake connection and RAI profile
Terminal window
```
rai init
```
Run the template

Option A: Command-line script
Terminal window
```
python disease_outbreak_prevention_network.py
```
Option B: Interactive Streamlit app
Terminal window
```
# Install additional dependencies for visualization
python -m pip install .[visualization]

# Launch the interactive app
streamlit run app.py
```
The Streamlit app provides:
- Interactive network visualization with directional arrows
- Filterable facility rankings table
- Detailed priority facility analysis
- CSV export functionality

How it works

The template follows this flow:

CSV files → model_setup.create_model() → Calculate metrics → Analyze strategic priorities → Display results

1. Shared Model Setup

Both the CLI script and Streamlit app use the same model setup from model_setup.py:

from model_setup import create_model

# Create the model, concepts, relationships, and graph (all in one call)
model, graph, Facility = create_model()

The create_model() function handles:

Creating the RelationalAI model container
Defining the Facility concept with all properties
Loading facilities from CSV
Defining the FacilityConnection concept for edges with transfer_volume, contact_intensity, and risk_weight properties
Loading connections from CSV with their risk metrics
Calculating risk_weight as transfer_volume × contact_intensity for each connection
Creating the directed, weighted graph using risk_weight as edge weights
Returning all components for use in analysis

2. Calculate Graph Metrics

Use RelationalAI’s Graph API to define weighted centrality metrics:

# Weighted degree centrality (sum of risk-weighted edge weights)
degree_centrality = graph.degree_centrality()

# Incoming edges (indegree count)
incoming_edges = graph.indegree()

# Outgoing edges (outdegree count)
outgoing_edges = graph.outdegree()

The weighted degree centrality incorporates the risk weights (transfer_volume × contact_intensity) from each edge, providing a measure of cumulative transmission risk rather than just connectivity count.

3. Query and Rank Facilities

Query the graph to retrieve all metrics and rank facilities:

from relationalai.semantics import where, Float, Integer

# Create variable references
facility = graph.Node.ref("facility")
centr_score = Float.ref("centr_score")
in_edges = Integer.ref("in_edges")
out_edges = Integer.ref("out_edges")

# Query the graph
results = where(
    degree_centrality(facility, centr_score),
    incoming_edges(facility, in_edges),
    outgoing_edges(facility, out_edges)
).select(
    facility.id,
    facility.name,
    facility.type,
    facility.region,
    centr_score.alias("degree_centrality"),
    in_edges.alias("incoming_connections"),
    out_edges.alias("outgoing_connections")
).to_df()

# Sort by degree centrality (descending)
results = results.sort_values("degree_centrality", ascending=False)
results.insert(0, "rank", range(1, len(results) + 1))

4. CLI Script Analysis

The disease_outbreak_prevention_network.py script displays:

A ranked table of all facilities with their metrics
Detailed breakdown of the top 3 priority facilities
Network-wide summary statistics
Actionable recommendations for outbreak response

5. Interactive Streamlit App

The included app.py provides an interactive web interface using the same shared model:

import streamlit as st
from model_setup import create_model

# Load the same model and query results
model, graph, Facility = create_model()
results = get_results(model, graph, Facility)

The Streamlit app features:

Interactive network graph: Directed edges with arrows, hover for facility details, region-based layout
Filterable rankings table: Filter by facility type and region, download as CSV
Priority facility analysis: Expandable sections with detailed metrics and role analysis
Summary statistics: Sidebar with key network metrics

Customize this template

Use your own data:

Replace the CSV files in the data/ directory with your own network, keeping the same column names (or update the logic in disease_outbreak_prevention_network.py).
Make sure that the facilities in connections.csv only references valid facility IDs.

Extend the model:

Add more risk factors: The template already uses weighted connections (transfer_volume × contact_intensity). You could extend this by:

Adding additional risk metrics to connections (e.g., disease prevalence, facility bed capacity)
Creating a more sophisticated risk formula (e.g., weighted average of multiple factors)
Adding temporal aspects (e.g., seasonal variation in transmission rates)

Troubleshooting

Why does authentication/configuration fail?

Run rai init to create/update raiconfig.toml.
If you have multiple profiles, set RAI_PROFILE or switch profiles in your config.

Why does the script fail to connect to the RAI Native App?

Verify the Snowflake account/role/warehouse and rai_app_name are correct in raiconfig.toml.
Ensure the RAI Native App is installed and you have access.