A study on the spread of COVID 19 outbreak by using mathematical modeling

Area of Science:

• Epidemiology
• Mathematical Biology
• Computational Science

Background:

• Mathematical models are crucial for understanding real-world problems, but converting observations into accurate formulations remains a challenge.
• The COVID-19 pandemic highlighted the critical need for reliable predictive models, despite existing models having limitations.
• Existing mathematical models for disease spread, including COVID-19, often present deficiencies due to formulation challenges.

Purpose of the Study:

• To propose and examine an alternative mathematical model for depicting the spread of COVID-19.
• To explore the utility of different differential and integral operators in modeling disease dynamics.
• To provide a novel approach for predicting the future behavior of the COVID-19 pandemic.

Main Methods:

• Development of an alternative mathematical model for COVID-19 spread.
• Application of various differential operators to the model.
• Utilization of integral operators to generate diverse simulation scenarios.

Main Results:

• The alternative model provides a new perspective on COVID-19 transmission dynamics.
• Different mathematical operators yield distinct predictive scenarios for disease spread.
• The study demonstrates the potential for improved predictions through novel modeling approaches.

Conclusions:

• Mathematical modeling of COVID-19 spread can be enhanced by exploring alternative formulations.
• The proposed model and its variations offer valuable insights into pandemic behavior.
• Further research into advanced mathematical operators can refine epidemiological predictions.