Virus Spread Simulation

Project Report: COVID-19 Spread Simulation in a Realistic City Environment

1. Problem Statement

The COVID-19 pandemic revealed the critical importance of understanding how infectious diseases propagate through populations in urban environments. Traditional mathematical models offer high-level insights but fail to visualize or adapt dynamically to behavioral or environmental changes. To address this gap, there is a need for an interactive simulation tool that visualizes how the virus spreads among individuals moving across a realistic cityscape, while considering age-based vulnerability, vaccination, quarantine strategies, and social distancing.

1. Our Solution

We present a C++-based simulation that visually models the spread of a virus (inspired by COVID-19) within a city-like environment. Individuals are modeled as agents who move along streets and intersections generated as a grid-based map with additional randomness. Each individual possesses attributes like age, vaccination status, and health state (susceptible, infected, quarantined, recovered). The simulation offers real-time statistical feedback, age-based infection probability, and configurable intervention strategies such as vaccination rate, quarantine policy, and social distancing.

1. Tech Stack

Programming Language: C++

Graphics and UI:

SFML (Simple and Fast Multimedia Library) for rendering

ImGui + ImGui-SFML for GUI controls and real-time data display

Build System: g++ with appropriate linking flags for SFML and OpenGL

1. Functionalities Explained

4.1 Realistic City Map Generation

The map consists of grid-based intersections representing streets and avenues.

Random nodes are added to simulate parks or crowd zones.

Nodes are interconnected to form a navigable graph with realistic movement paths.

4.2 Agent (Person) Modeling

Each person is modeled with the following key properties:

Position, Velocity, and Movement Path

Age: Influences infection risk

Health State:

Susceptible (at risk of infection)

Infected (can transmit virus)

Quarantined (isolated and non-contagious)

Recovered (immune)

Vaccination Status: Includes effectiveness probability

4.3 Age-Based Infection Logic

Individuals under 10 or over 60 are more likely to be infected due to weaker immunity.

Others have reduced infection probabilities.

Color-coded visualization:

Cyan: Children (<10 years)

Pink: Elderly (>60 years)

Light Blue: Other susceptible individuals

4.4 Infection Spread Dynamics

Infection is proximity-based: closer individuals have higher transmission chance.

Vaccination reduces infection probability by a randomized effectiveness factor.

Social distancing reduces speed and proximity, mitigating spread.

4.5 Quarantine Strategies

Configurable via UI:

Symptomatic Detection (based on probability)

Random Testing

Contact Tracing (infected quarantines traced contacts)

Quarantined individuals cannot infect others and recover after a set duration.

4.6 User Interactions

Control Panel (via ImGui):

Modify simulation parameters (infection rate, population, vaccine rate)

Toggle strategies and behaviors

Add individuals manually

Person Tracking: Click an individual to follow their stats and nearest infected distance

Real-Time Graphs: Display population health distribution over time

1. Algorithms and Data Structures Used

5.1 Algorithms

Floyd–Warshall Algorithm: Used for precomputing all-pairs shortest paths between nodes in the city grid. Enables fast pathfinding and dynamic rerouting.

Randomized Monte Carlo Logic: Used for probabilistic infection transmission and strategy simulation (e.g., symptom detection).

5.2 Data Structures

Graph (Adjacency List): Represents the city layout with intersections (nodes) and roads (edges).

2D Vectors (Matrix): Used for distance and next-node matrices in the Floyd–Warshall algorithm.

Dynamic Arrays (std::vector): Store lists of people, paths, and time-series statistics.

Structs and Enums: Represent complex agent states and behaviors efficiently.

1. Conclusion

This simulation provides a dynamic and educational tool to understand how virus spreads in a city, highlighting the effects of individual behavior, public health strategies, and demographic vulnerability. It merges agent-based modeling with interactive visualization, making it ideal for both academic demonstration and public awareness.

1. Future Enhancements

Implement stacked area chart for epidemic progression

Introduce behavior-based decision making for agents

Add support for masks and varying infection strains

Export statistics to CSV for external analysis

Built With

• c++
• imgui
• sfml