Post-Vaccination Analytics at Scale: Measuring Breakthrough Infection Trends Using Nationwide Pharmacy Data

Vijitha Uppuluri

Abstract: Surveillance and data analysis after vaccination are paramount in assessing the effectiveness of COVID-19 vaccines and if the vaccinated people can get infected. This study will analyze a large-scale breakthrough infection and compare the trends according to the pharmacy business data all over Korea, with special attention to the vaccinated populations. In turn, breakthrough infection patterns are evaluated using data analysis techniques such as statistical analysis and Artificial Intelligence models. Some factors used include the type of vaccine used, age and other health complications of the patients. Such findings present an understanding of the effectiveness of vaccines at the given period and factors leading to a breakthrough illness. The paper aims to identify obstacles to using large-scale data and generalization in pharmacological epidemiological investigations. It aimed to identify the needs of diabetic patients and fine-tune the approaches to improve the population's health.

Keywords: Breakthrough infections, post-vaccination analytics, COVID-19, pharmacy data, vaccine efficacy, epidemiological study, machine learning