Data-Driven Modeling of Impact of Differential Efficacy of COVID-19 Vaccines in Two Socio-Economically Contrasting Cities: New York, USA and Bogotá, Colombia

Abstract

In an effort to curb the spread of COVID-19, various types of vaccines, including mRNA, viral vectors, and traditional ones, were globally approved and implemented. However, the distribution of vaccines in each country became a critical and determining factor in the disease's evolution. The present study aims to understand the differential impact of the different available vaccine types on disease burden. A proposed mathematical model considers multiple vaccines in a community to analyze the dynamics of COVID-19 transmission in two socioeconomically diverse regions. Secondary incidence data for the cities of Bogotá, Colombia, and New York, USA, from March 2020 to December 2021 were used to estimate vaccine-related parameters and actual transmission rates. The results suggest that although New York has more effective vaccines, higher vaccination rates, and lower poverty rates compared to Bogotá, its disease burden was significantly higher due to higher population density and, consequently, a greater number of contacts. This indicates that while more effective vaccines are crucial to flattening the curve, social distancing measures are equally important for quickly controlling the disease if the vaccination rate is not sufficiently high. Additionally, the model successfully captures the epidemiological behaviour of transmission through the use of vaccines, calculating the basic reproductive number in different scenarios and estimating the parameters of the proposed model.