ABSTRACT
Background: Vaccination against COVID-19 is a primary tool for controlling the pandemic. However, the spread of vaccine hesitancy constitutes a significant threat to reverse progress in controlling transmissible diseases such as COVID-19. A previous Mexican survey conducted in 2012 showed that the adult population with more years of formal education and from higher socioeconomic levels was more likely to have incomplete vaccination schedules. Objective: To know the prevalence of COVID-19 vaccine hesitancy and associated factors among academics, students, and administrative personnel of a public university in Mexico City. Methods: We administered an online survey investigating sociodemographic aspects, knowledge, attitudes, practices, and acceptance/hesitancy regarding the COVID-19 vaccine. Using generalized linear Poisson models, we analyzed factors associated with vaccine hesitancy, defined as not intending to be vaccinated within the following six months or refusing vaccination. Results: During May and June 2021, we studied 840 people, prevalence of vaccine hesitancy was 6%. Hesitancy was significantly associated with fear of adverse effects, distrust of physician’s recommendations, lack of knowledge regarding handwashing, age younger than 40 years, refusal to use face masks, and not having received influenza vaccination during the two previous seasons.Conclusions. Vaccine hesitancy in this population is low. Furthermore, our results allowed us the identification of characteristics that can improve vaccine promotion.
Subject(s)
COVID-19ABSTRACT
On January 23, 2020, China imposed a quarantine on the city of Wuhan to contain the SARS-CoV-2 outbreak. Regardless of this measure the new infection has spread to several countries around the world. Here, we developed a method to study the dissemination of this infection by the airline routes and we give estimations of the time of arrival of the outbreaks to the different cities. In this work we show an analysis of the dispersion of this infection to other cities by airlines based on the classic model the Kermack and McKendrick complemented with diffusion on a graph composed of nodes which represent the cities and edges which represent the airline routes. We do several numerical simulations to estimate the date of arrival to different cities starting the infection at Wuhan, China and to show the robustness of the estimation respect to changes in the epidemiological parameters and to changes on the graph. We use Mexico City as an example. In this case, our estimate of the arrival time is between March 20 and March 30, 2020. This analysis is limited to the analysis of dispersion by airlines, so this estimate should be taken as an overestimate since the infection can arrive by other means. This model estimates the arrival of the infectious outbreak to Mexico between March 20 and March 30. This estimation gives a time period to implement and strengthen preventive measures aimed at the general population, as well as to strengthen hospital infrastructure and training of human resources in health.