K-SEIR-Sim: A simple customized software for simulating the spread of infectious diseases

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• COVID-19 poses a significant global threat, necessitating advanced tools for pandemic analysis.
• Existing susceptible-exposed-infected-recovered (SEIR) models are often too technical for public and governmental use.
• Effective pandemic management requires accessible methods for trend prediction and intervention assessment.

Purpose of the Study:

• To develop a user-friendly software, the kernel SEIR simulator (K-SEIR-Sim), based on an improved K-SEIR model.
• To create a tool that simplifies the analysis of infectious disease dynamics, including natural spread and containment measures.
• To provide a practical solution for understanding and managing pandemics like COVID-19.

Main Methods:

• Developed the kernel SEIR simulator (K-SEIR-Sim) incorporating natural propagation, containment, and characteristic parameters.
• Utilized an improved K-SEIR model for enhanced simulation accuracy.
• Applied the software to COVID-19 outbreak data from the United States and Wuhan, China.

Main Results:

• K-SEIR-Sim demonstrated effectiveness in analyzing epidemic situations and human interventions during COVID-19.
• The software successfully evaluated the impact of containment measures in real-world scenarios.
• Validation using US and Wuhan COVID-19 data confirmed the software's practical utility.

Conclusions:

• K-SEIR-Sim is the first simple, customized operating software for infectious disease analysis.
• The software offers valuable real-time, backward-looking, and forward-looking analysis capabilities.
• K-SEIR-Sim is highly beneficial for global efforts against COVID-19 and future infectious disease threats.