Knowledge, attitudes, perceptions, and COVID-19 hesitancy in a large public university in Mexico city during the early vaccination rollout.

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 preventing the disease. Studies conducted in Mexico have revealed that vaccination intention in Mexico among the general population ranges from 62 to 82%. 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.

DISPERSION OF A NEW CORONAVIRUS SARS-COV-2 BY AIRLINES IN 2020: TEMPORAL ESTIMATES OF THE OUTBREAK IN MEXICO.

BACKGROUND: 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. OBJECTIVE: We developed a method to study the dissemination of this infection by airline routes and provide estimations of the time of arrival of the outbreak to different cities. METHODS: Using the Kermack and McKendrick model complemented with diffusion on a graph composed of nodes and edges, we made an analysis of COVID-19 dispersion to other cities by air travel. RESULTS: The estimation was accurate in that it was possible to predict in the middle of February 2020 the arrival of the first outbreak in Mexico, which eventually occurred between March 20 and 30. This estimation was robust with respect to small changes in epidemiological parameters at the other nodes. CONCLUSIONS: The estimation of the time of arrival of the outbreak from its epicenter, allows for 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 the present study, this estimation was accurate, as observed from the real data of the beginning of the outbreak in Mexico City up to April 6, 2020.

Dispersion of a new coronavirus SARS-COV-2 by airlines in 2020: temporal estimates of the outbreak in Mexico

ABSTRACT Background: 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. Objective: We developed a method to study the dissemination of this infection by airline routes and provide estimations of the time of arrival of the outbreak to different cities. Methods: Using the Kermack and McKendrick model complemented with diffusion on a graph composed of nodes and edges, we made an analysis of COVID-19 dispersion to other cities by air travel. Results: The estimation was accurate in that it was possible to predict in the middle of February 2020 the arrival of the first outbreak in Mexico, which eventually occurred between March 20 and 30. This estimation was robust with respect to small changes in epidemiological parameters at the other nodes. Conclusions: The estimation of the time of arrival of the outbreak from its epicenter, allows for 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 the present study, this estimation was accurate, as observed from the real data of the beginning of the outbreak in Mexico City up to April 6, 2020.

Control measures for Chagas disease.

Chagas disease, also known as American trypanosomiasis, is a potentially life-threatening illness caused by the protozoan parasite, Trypanosoma cruzi. The main mode of transmission of this disease in endemic areas is through an insect vector called triatomine bug. Triatomines become infected with T. cruzi by feeding blood of an infected person or animal. Chagas disease is considered the most important vector borne infection in Latin America. It is estimated that between 16 and 18 millions of persons are infected with T. cruzi, and at least 20,000 deaths each year. In this work we formulate a model for the transmission of this infection among humans, vectors and domestic mammals. Our main objective is to assess the effectiveness of Chagas disease control measures. For this, we do sensitivity analysis of the basic reproductive number R0 and the endemic proportions with respect to epidemiological and demographic parameters.

Seasonality and outbreaks in West Nile virus infection.

In this paper we analyze the impact of seasonal variations on the dynamics of West Nile Virus infection. We are interested in the generation of new epidemic peaks starting from an endemic state. In many cases, the oscillations generated by seasonality in the dynamics of the infection are too small to be observable. The interplay of this seasonality with the epidemic oscillations can generate new outbreaks starting from the endemic state through a mechanism of parametric resonance. Using experimental data we present specific cases where this phenomenon is numerically observed.

Modelling the dynamics of West Nile Virus.

In this work we formulate and analyze a mathematical model for the transmission of West Nile Virus (WNV) infection between vector (mosquito) and avian population. We find the Basic Reproductive Number R0 in terms of measurable epidemiological and demographic parameters. R0 is the threshold condition that determines the dynamics of WNV infection: if R0< or =1 the disease fades out, and for R0 >1 the disease remains endemic. Using experimental and field data we estimate R0 for several species of birds. Numerical simulations of the temporal course of the infected bird proportion show damped oscillations approaching the endemic value.