How a Simple Java Simulation Reveals COVID‑19 Spread Dynamics

Simulation Model

A Java program models virus spread in a closed population. Individuals and their movement intentions are sampled from normal (Gaussian) distributions, while the virus transmission rate is a fixed constant. The model tracks infection status, incubation delay, hospital isolation, and resource limits.

Key Parameters

Initial infected count public static int ORIGINAL_COUNT = 50; Transmission rate (BROAD_RATE) public static float BROAD_RATE = 0.8f; Incubation period (SHADOW_TIME) – number of simulation steps before an infected individual becomes contagious. 0 means no delay; 140 represents 14 days. public static float SHADOW_TIME = 0; Hospital response time (HOSPITAL_RECEIVE_TIME) – steps from diagnosis to isolation. public static int HOSPITAL_RECEIVE_TIME = 10; Hospital bed capacity (BED_COUNT) – number of isolation beds available at the start of the outbreak. public static int BED_COUNT = 0; Mobility intention (u) – average tendency to move. Positive values allow free movement; negative values simulate strict movement restrictions.

public static float u = 0.99f;

Scenario Experiments

1. No Hospital Capacity, High Mobility

Configuration: ORIGINAL_COUNT = 50, BED_COUNT = 0, u = 0.99f. The simulation shows rapid exponential growth because infected individuals cannot be isolated.

2. Adding Hospital Beds

Increasing BED_COUNT to 100 provides isolation capacity, which flattens the infection curve.

public static int BED_COUNT = 100;

When mobility is also reduced (lower u), the outbreak can be quickly contained.

3. Long Incubation Period

Setting SHADOW_TIME = 140 (14 days) mimics COVID‑19’s latent period. Early spread appears hidden; once the incubation ends, cases surge and overwhelm hospital capacity.

public static float SHADOW_TIME = 140;

4. Reducing Mobility

Assigning a negative mobility value ( u = -0.99f) simulates strict movement restrictions, dramatically curtailing transmission.

public static float u = -0.99f;

Insights

The experiments demonstrate that the mobility intention parameter u dominates epidemic dynamics. Adequate hospital capacity and a short HOSPITAL_RECEIVE_TIME reduce the peak, but a long incubation period ( SHADOW_TIME) hides early cases, making early detection essential. Controlling population movement is the most effective lever to prevent explosive growth.

Source Code

The full source code is available on GitHub:

https://github.com/KikiLetGo/VirusBroadcast/tree/master/src