Clinical Impact of SARS-CoV2 Pandemic in the Diagnosis of Early-Stage Thoracic Tumours

Introduction: Cancer healthcare has been affected by Coronavirus disease 2019 (COVID-19) pandemic, interfering the normal function of oncology units and increasing diagnostic delay. Nevertheless, the rising incidence of respiratory infections led to an increase in medical consultations and chest imaging explorations. The aim of the study was to assess whether the increase in medical evaluations in the context of the pandemic led to an increase in the detection of early-stage thoracic tumours. Methods: We performed a retrospective single-institution study, collecting data from patients diagnosed with thoracic tumours between March, 1, 2020 and December, 31, 2021. We analysed their demographic and clinical data, symptoms at diagnosis and those who were diagnosed due to SARS-CoV-2 infection. Results: A total of 378 patients were analysed. Main results are shown in Table-1. Only 5.3% of newly diagnosed thoracic tumours were related to a suspected or confirmed SARS-CoV-2 infection. However, these patients were not diagnosed at earlier stages (p = 0.414). When we evaluated symptoms at diagnosis, we found that asymptomatic patients presented in earlier stages (p <0.000, Figure-1), being the majority incidental findings during the follow-up of oncological and non-oncological pathologies. Regarding symptomatic patients, most presented as locally advanced or metastatic diseases and no changes have been observed in the pattern of presentation compared to studies prior to the pandemic. [Formula presented] Conclusions: COVID-19 pandemic did not seem to increase thoracic tumours diagnosis in our study. Lung cancer diagnosed in patients due to SARS-CoV-2 infection was not detected in earlier stages. Clinical presentation was similar to previous reported outside COVID-19 pandemic. Nevertheless, we find that asymptomatic patients diagnosed incidentally presented more frequently in localized stages in comparison with symptomatic patients. [Formula presented] Keywords: COVID19, Lung Cancer, Diagnosis

Estimation of Classroom Capacity Based on Its Size and Ventilation Capacity during the SARS-CoV-2 Coronavirus Pandemic

An analysis was conducted based on a model of CO2 mass balance inside a classroom of a certain volume (V-classroom). The model considered the parameters of CO2 production rate per person and indoor air renewal capacity (air changes per hour, or ACH), an equation being obtained that can be used to estimate the number of students that can use (at the same time) a classroom of a defined size (V-classroom) without the maximum allowable CO2 concentration level being reached, which would minimize the risk of airborne transmission of the SARS-CoV-2 virus.

Web 2.0 Education Tools as Support in Digital Marketing: Tungurahua Case Study

This research aims to assess the impact that information and communication technologies have as support in tourism against the COVID-19 pandemic, in this way a study is developed within Digital Marketing in institutions of the tourism industry of the province of Tungurahua and how they have contributed to the dissemination of information in the field of tourism. This research was based on the implementation of a structured questionnaire of 45 Likert-scale questions, which measure the use of digital, technological and communication tools and strategies that tourism companies have used with the aim of improving their promotion with tourists based on a new normality that has been applied in the face of the COVID-19 pandemic, the instrument designed and applied, obtained a level of trust of 0.831 by means of the Cronbach Alpha statistic taken from a sample of 320 institutions on the field of tourism. The statistic test for this research was Kolmogorov Smirnov, which allowed knowing the concordance between the data and the specific theoretical distribution, which was to check whether Digital Marketing is considered a supporting tool in tourism in the province of Tungurahua;with the final result obtained, the alternative hypothesis proposed in the research was accepted. The result of the project admitted to using auxiliary technological tools to boost current generations’ interest and knowledge of Digital Marketing through the distribution of information in the field of tourism. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Logistic Regression Model and Decision Trees to Analyze Changes in Tourist Behavior: Tungurahua Case Study

This research was designed to identify the factors that influence the behavior changes of tourists visiting the province of Tungurahua and build the new visitor profile. The analytical method applied in this study was through the logistic regression model. Through the use of the CHAID logarithm, the decision trees were constructed. For this research, a total of 323 questionnaires were collected and validated by 4 experts. Additionally, the Cronbach’s Alpha statistic was used with a confidence level of 0.914. In this way, some questions were accepted for this research. Among the findings, it is identified that the behavior of tourists is determined by attitudes, decision conditions, travel destination choices, risk perceptions, and marketing factors. Decision tree models revealed that the choice of tourist destination depends on the effective application of biosafety regulations by service providers. Millennials are the potential market, coming from some cities of Ecuador, like Ambato, Quito, and Latacunga. They have a university or higher level of education and belong to a middle and upper-middle-income social stratum. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.