Post COVID-19 Vaccination: Infection Rate Analysis Using Time Series Modeling

The COVID-19 pandemic has drastically altered the course of human life and economic stability, with a death toll nearing seven million. Three years in, infections persist, affecting even the vaccinated populace, with no global data to clarify the infection rates post-vaccination or the relationship between vaccine hesitancy and subsequent infection. Addressing this gap, our research introduces a big data-driven analytical model employing TBATS, Prophet, ARIMA, and LSTM forecasting techniques to evaluate vaccination's impact on case trends and potential variant resistance. This paper utilizes WHO-provided datasets of daily cases, deaths, and vaccination rates, including booster shots since 2020, to gauge vaccine efficacy and the possibility of vaccines not entirely preventing new strains. Our analysis aims to shed light on vaccination outcomes and inform public health strategies, offering a clearer understanding of COVID-19's trajectory in the vaccinated demographic.