Java-Based COVID-19 Epidemic Spread Simulation Using Normal Distribution

This article presents a simple COVID-19 epidemic spread simulation written in Java, using the normal (Gaussian) distribution to model population movement and infection rates, aiming to illustrate how staying at home can dramatically curb virus transmission.

Key constants are defined: an initial 50 infected individuals, zero hospital beds at the start, and a high movement intention value of 0.99, representing unrestricted travel.

When the movement intention is reduced to -0.99 (simulating strict self‑isolation), the simulation shows a rapid decline in infections, demonstrating the power of personal mobility control.

Introducing an isolation zone with 100 hospital beds further suppresses the outbreak, especially when combined with low movement intention.

If movement intention returns to 0.99 while hospital capacity remains limited, the model quickly fills all beds and the epidemic spirals out of control.

Expanding hospital capacity tenfold (to ten times the original beds) restores control over the spread, even under higher movement intention.

The simulation also includes a hospital_receive_time parameter for patient admission delay and a shadow_time parameter representing the virus’s 14‑day incubation period; longer delays worsen outcomes.

Overall, the model underscores that controlling population movement is the dominant factor in epidemic management, and that premature relaxation of precautions during the incubation period can lead to a dangerous resurgence.