Mathematical Modelling of Virus Spreading in COVID-19.

Area of Science:

• Epidemiology
• Mathematical Biology
• Infectious Disease Modeling

Background:

• COVID-19 pandemic highlighted the need for robust mathematical models to understand and predict infectious disease dynamics.
• Existing models often require complex parameterization, limiting their application in real-time outbreak management.

Purpose of the Study:

• To develop a simplified mathematical model for analyzing COVID-19 spreading dynamics.
• To utilize the model for predicting daily and cumulative infections and understanding key epidemiological parameters.
• To apply the model to address critical issues such as virus extinction, emergence of second waves, and competitive pathogen spread.

Main Methods:

• A mathematical framework was developed incorporating the basic reproduction number (R0) and an attenuation constant (k).
• The model was used to deduce the daily number of infections (DNI) and cumulative number of infections (CNI) over time (m).
• Model predictions were validated against experimental data, demonstrating good agreement.

Main Results:

• The model successfully predicted DNI and CNI, aligning well with empirical observations.
• Key conditions for virus extinction based on R0 were inferred.
• The model provided explanations for the occurrence of second infection waves and suggested potential preventive measures.
• Insights into the competitive spread of two viruses were generated, informing control strategies.

Conclusions:

• The proposed mathematical model offers a simple yet effective framework for analyzing complex epidemic scenarios.
• Theoretical insights derived from the model can guide the assessment of infection wave severity.
• The model facilitates the formulation of effective control and mitigation strategies for infectious disease outbreaks.