Systematic Review of Mathematical Models for Viral Transmission Dynamics: Insights from COVID-19 Studies

Abstract

The research aims to measure the systematic review related to the mathematical models for viral transmission dynamics. For measuring the research, the study used E views software because the research was based on secondary data analysis. The mathematical models present viral transmission dynamics related to COVID-19 studies. Furthermore, the geographic and social dimensions of viral transmission have been clarified using models that incorporate spatial and network dynamics. The prediction of trends and the assessment of the efficacy of interventions have been made easier by time series analysis. Models evaluating the effect of vaccination on the dynamics of transmission and the development of herd immunity are explored, since vaccination programs have taken center stage in the pandemic response. Behavioral models have investigated how human behavior affects how the epidemic spreads. Sensitivity and uncertainty assessments highlight the importance of recognizing and dealing with model parameter uncertainties. Refinement of the model and continuous research is required due to the dynamic nature of the pandemic and the emergence of new variations. The study summarizes research findings until January 2022. It emphasizes the use of mathematical models in determining crucial factors like the fundamental reproduction number (R₀) and clarifying the effects of treatments like lockdowns and social distance measures. This comprehensive study concludes by highlighting the vital role that mathematical models play in improving our comprehension of the dynamics of COVID-19 transmission. The distribution of resources, policy choices, and public health policies have all benefited from these discoveries. Collaboration between researchers and public health practitioners is crucial to adjusting models and tactics to successfully reduce the effects of the virus as the pandemic progresses.