How a Simple Java Simulation Shows Mobility’s Role in COVID‑19 Spread

Overview

A Java program models the spread of COVID‑19 using an object‑oriented approach. The total population and each individual's mobility intention are sampled from a normal (Gaussian) distribution, while the virus transmission probability is a fixed constant. The simulation tracks infection, incubation, diagnosis, and isolation over discrete time steps.

Key Parameters

public static int ORIGINAL_COUNT = 50;

– initial number of infected individuals. public static float BROAD_RATE = 0.8f; – probability that a contact transmits the virus. public static float SHADOW_TIME = 0; – incubation period (0 for immediate symptoms; set to 140 to represent a 14‑day latent period). public static int HOSPITAL_RECEIVE_TIME = 10; – number of time steps from diagnosis to placement in isolation. public static int BED_COUNT = 0; – number of hospital isolation beds available at the start of the outbreak. public static float u = 0.99f; – average mobility intention; values close to 1 allow free movement, negative values model strict movement restrictions.

Adjusting these constants enables exploration of different outbreak dynamics.

Simulation Scenarios

Scenario 1 – No Hospital Beds, High Mobility

Configuration: BED_COUNT = 0, u = 0.99f. The infection spreads gradually at first but quickly overwhelms the nonexistent isolation capacity, illustrating the need for emergency medical facilities.

Scenario 2 – Adding Hospital Beds

Configuration: BED_COUNT = 100. With the same high mobility ( u = 0.99f) the spread is slower, but if mobility is reduced (e.g., u = 0.2f) the epidemic can be contained or even eliminated.

Scenario 3 – Extending Incubation Period

Configuration: SHADOW_TIME = 140 (14 days). The model shows a delayed but explosive increase in cases once the latent period ends, again exceeding hospital capacity.

Scenario 4 – Negative Mobility Intent

Configuration: u = -0.99f (strict movement restriction). Even with a long incubation period, the simulation demonstrates that severe mobility limits eventually bring the outbreak under control.

Insights

The experiments highlight that population mobility (parameter u) is the dominant factor shaping epidemic dynamics. Hospital capacity ( BED_COUNT) and response time ( HOSPITAL_RECEIVE_TIME) influence outcomes, but without controlling movement the virus spreads rapidly, especially after the incubation period ends.

Source Code

The full source code is publicly available on GitHub:

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