The following specification particularly describes the invention and the manner in which it is to be
performed.

Field of the Invention The present invention relates to the field of epidemiological modeling and predictive healthcare analytics, and more specifically to a graph theory-enhanced artificial intelligence system for forecasting the spread of infectious diseases. This invention combines concepts from graph theory, machine learning, and network science to build dynamic models that simulate and predict disease transmission within a population. The system is particularly applicable in public health monitoring, early outbreak detection, and intervention planning. It leverages real-time data to provide accurate, adaptive, and scalable insights into how diseases propagate through social and geographical networks.
Background of the Invention The accurate modeling and prediction of infectious disease spread have long been central challenges in the field of epidemiology. Traditional mathematical models, such as the SIR (Susceptible-Infectious-Recovered) and SEIR (Susceptible-Exposed-Infectious- Recovered) models, offer theoretical frameworks to understand disease dynamics.
However, these models typically rely on assumptions of population homogeneity, static transmission rates, and limited interaction complexity. As a result, they often fall short in capturing the nuanced, real-world behaviors and networked interactions through which diseases actually spread.
The recent global outbreaks of infectious diseases such as COVID-19, SARS, and Ebola have exposed significant limitations in conventional epidemiological models.
These limitations include the inability to model dynamic human mobility patterns, changes in social behavior, super-spreader events, and regional disparities in disease transmission. Additionally, the rising availability of large-scale data from mobile

devices, health records, IoT sensors, and social media has presented a new opportunity for developing more accurate and responsive predictive tools.
Graph theory offers a robust framework for modeling the complex web of interactions in a population. By representing individuals as nodes and their interactions or movements as edges, it becomes possible to simulate how diseases might propagate through communities, workplaces, or transportation networks. However, graph models alone lack predictive intelligence unless coupled with advanced learning algorithms.
Recent advancements in Artificial Intelligence (Al) particularly Graph Neural Networks (GNNs) and temporal deep learning models have demonstrated strong potential in learning patterns from structured and dynamic graph data. These models can analyze changes over time, learn latent features of transmission, and provide real-time
forecasts.
Despite these advancements, there remains a gap in the integration of graph theory and Al for practical disease spread prediction. Existing approaches are often either too simplistic (pure graph analysis) or too data-intensive and opaque (black-box Al models without interpretability). There is a need for a hybrid, interpretable, scalable system that can utilize graph-based representations enriched by Al to offer accurate, transparent, and real-time epidemiological forecasts.
This invention addresses that gap by introducing a novel framework that seamlessly integrates dynamic graph construction with Al-powered prediction engines.
The invention not only improves forecast accuracy but also supports public health decision-making through visual insights, early warning capabilities, and scenario simulation for disease mitigation strategies.
Summary of the Invention The present invention provides a novel and intelligent system for predicting the spread of infectious diseases using a graph theory-enhanced artificial intelligence (Al) model. The system leverages the structural advantages of graph-based representations of populations and interactions and the learning capabilities of Al algorithms to model,

analyze, and forecast disease transmission with higher precision and adaptability.
In this invention, the population is modeled as a dynamic graph, where: • Nodes represent individuals, groups, or locations (such as households, hospitals,
cities),
• Edges represent interactions or movements (such as physical contact, shared space, or travel),
• Edge weights indicate interaction frequency, duration, or likelihood of
transmission.
The graph is dynamically updated in real-time using various data sources such as mobile tracking, GPS logs, healthcare databases, wearable devices, and public health reports.
This allows the graph to accurately reflect evolving patterns of human behavior and
interaction.
A core feature of the invention is the Al Prediction Engine, which uses Graph Neural Networks (GNNs), Temporal Convolutional Networks, or Recurrent Neural Networks (RNNs) like LSTM to analyze the structure and evolution of the graph. These models are
trained to:
• Predict future infection nodes and possible outbreak zones, • Classify nodes based on infection risk, • Estimate the basic reproduction number (Ro) over time, • Simulate intervention outcomes such as lockdowns or vaccination drives.
The system also includes a decision support and visualization layer, enabling public health officials, governments, and healthcare providers to interpret the model’s predictions and take proactive actions. Alerts and heatmaps are generated for high-risk zones or clusters.
Key benefits of the invention include: • Improved prediction accuracy by using real-world interaction data, • Scalability across geographic regions and disease types, • Flexibility in adapting to different infection models or variants,

• Interpretable insights for non-technical users, • Real-time feedback loop for model refinement and continuous learning.

Detailed Description of the Invention The present invention discloses an intelligent, adaptive system for predicting disease spread using a hybrid model that integrates graph theory with artificial intelligence (Al). The system captures real-world social interactions through graph-based structures and applies learning algorithms to simulate and forecast disease transmission patterns with high accuracy and timeliness.
1. System Overview The system comprises the following major components: o Data Collection Module o Graph Construction Engine o Al Prediction Engine o Visualization and Alert Interface o Feedback and Model Update Module
1.1 Data Collection Module This module gathers data from multiple heterogeneous sources, including but not
limited to:
• Mobile location tracking and GPS logs • Healthcare records and testing reports • Social interaction logs (e.g., Bluetooth-based contact tracing) • Demographic and environmental data • Transportation and mobility statistics The collected data is cleaned, pre-processed, anonymized, and formatted for use in graph construction. 1.2 Graph Construction Engine This engine constructs a dynamic graph where: • Nodes represent individuals, places, or entities relevant to disease spread.

• Edges denote interactions or connections (e.g., physical contact, shared environment, travel history). • Edge weights are calculated based on frequency, duration, and proximity of interactions, and optionally infection probability.
The graph is time-evolving, meaning it is updated periodically (e.g., hourly or daily) based on incoming data. Additional graph properties include: • Node features: health status, age, immunity level, mobility score • Edge features: duration of contact, type of interaction, mask compliance, vaccination status
1.3 Al Prediction Engine This engine consists of machine learning models that operate directly on the
constructed graph.
Core Algorithms Include: • Graph Neural Networks (GNN): Learn node-level and graph-level embeddings to identify high-risk individuals and clusters. • Temporal Graph Networks or Spatio-Temporal Models: Predict how the graph will evolve over time, capturing the progression of disease. • LSTM or GRU networks: Learn temporal trends in infection rates and health
parameters.
• Reinforcement Learning: Suggest intervention strategies (e.g., lockdowns or resource allocations) and simulate their impact.
Functionality:
• Predict likely future infections based on current graph structure. • Classify nodes into risk categories: Susceptible, Exposed, Infectious, Recovered. • Simulate interventions (e.g., isolating a node) and their effect on future spread.

Identify super-spreaders using graph metrics like centrality, degree, and
betweenness.
1.4 Visualization and Alert Interface The interface displays disease spread forecasts and risk maps to stakeholders such
as:
• Public health officials • Municipal authorities • Hospital administrators • Features include: • Heatmaps of predicted outbreaks • Dashboards with infection curves • Early warning alerts • Interactive scenario simulations
02-Ju1-2025/66102/202541063069/Form 2(Title Page)
The system can be integrated into web-based platforms or mobile apps and may support API access for integration with government portals.

1.5 Feedback and Model Update Module A key aspect of the invention is the continuous improvement of the prediction system through a feedback loop: • Real-world outcomes (actual case reports) are compared with predictions. • Errors are used to update model weights via re-training. • The graph structure is refined with newly available data. • Edge weights and transmission probabilities are adjusted based on emerging knowledge (e.g., new variants, behavior shifts).

This enables the system to learn and adapt in near real-time, increasing its reliability
over time.
2. Technical Implementation Notes • The system may be deployed on cloud platforms, enabling large-scale processing
and scalability.
• Data security and privacy are enforced through encryption, access control, and anonymization techniques. • The graph database may use technologies such as Neo4j or networkX, and Al models may be implemented in PyTorch Geometric, TensorFlow, or DGL (Deep Graph Library).
3. Example Use Case In a metropolitan city, the system receives data from hospitals, public transit systems, and smartphone-based contact tracing apps. It constructs a population interaction graph and predicts that a certain neighborhood is at high risk due to a concentration of central nodes with increasing contact frequency. Authorities receive alerts and enforce localized containment. Meanwhile, healthcare facilities are warned in advance to prepare for a potential surge in cases.
Advantages of the Invention • Provides fine-grained predictions based on real interaction data. • Supports adaptive learning from new outbreaks or mutations. • Can be applied at local, regional, or national scales. • Offers visual simulation tools for policy makers. • More accurate than traditional SIR/SEIR models due to real-world data
integration.

Industrial Applications • Public health agencies for outbreak prediction and planning. • Smart city infrastructures for emergency preparedness. • Healthcare providers for resource forecasting. • Research institutions modeling epidemic dynamics. • Government and disaster management units for containment strate

1: A system for predicting disease spread using a graph-based Al model
comprising:
• A data ingestion module that collects mobility, health, and interaction data; • A graph construction engine that builds and updates dynamic contact networks; • An Al module employing graph neural networks to analyze and forecast disease
spread;
• A user interface to display predictions and alerts.
Claim 2: The system of claim 1, wherein the graph is dynamically updated based on real-time data streams.
Claim 3: The system of claim 1, wherein the Al module is trained using supervised or reinforcement learning techniques.
Claim 4: The system of claim 1, wherein the graph edges are weighted based on proximity, interaction duration, and infection probability.
Claim 5: The system of claim 1, wherein the prediction module identifies super­spreaders based on centrality and influence metrics within the graph.