Modeling for COVID-19 with the contacting distance

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• COVID-19 poses significant global public health and socioeconomic challenges.
• Effective control strategies require accurate epidemic modeling.

Purpose of the Study:

• To propose and analyze an SEIR-type epidemic model incorporating contacting distance and immigration rate.
• To estimate model parameters using Wuhan city data (Jan-Mar 2020).
• To conduct sensitivity analysis and numerical studies for COVID-19 control.

Main Methods:

• Developed an SEIR (Susceptible-Exposed-Infectious-Recovered) model.
• Incorporated contacting distance and healthy individual immigration rate as key factors.
• Estimated parameters using real-world COVID-19 data and performed sensitivity analysis.

Main Results:

• Contacting distance and immigration rate are critical factors influencing COVID-19 transmission.
• Decreasing contacting distance and/or immigration rate reduces the epidemic's extinction lag.
• Model simulations provide insights into effective control measures.

Conclusions:

• The study highlights the importance of physical distancing and controlling population movement for pandemic management.
• Findings offer theoretical support for public health interventions and policy-making.
• The model provides a framework for understanding and mitigating infectious disease spread.