COVID-19: booster(s) vs. hospitalization and Intensive Care Unit admission.

OBJECTIVE: As the pandemic continues, different vaccine protocols have been implemented to maintain the protection of vaccines and to provide protection against new variants. The aim of this study was to assess hospitalized patients' vaccination status and document the efficacy of boosters. PATIENTS AND METHODS: The patients that were hospitalized due to COVID-19 were enrolled from 28 hospitals in Turkey for five months from September 2021. 5,331 confirmed COVID-19 patients from collaborating centers were randomly enrolled to understand/estimate the distribution of vaccination status in hospitalized patients and to compare the efficacy of vaccination/booster protocols. RESULTS: 2,779 men and 2,552 women of which 2,408 (45.2%) were admitted to Intensive Care Units participated in this study. It was found that the highest risk reduction for all age groups was found in groups that received 4 doses. Four doses of vaccination for every 3.7 people under 50 years of age, for every 5.7 people in the 50-64 age group, and for every 4.3 people over 65 years of age will prevent 1 patient from being admitted to intensive care. Regardless of the type of vaccine, it was found that the risk of ICU hospitalization decreased in those who were vaccinated compared to those who were not vaccinated. Regardless of the type of vaccine, the ICU risk was found to decrease 1.25-fold in those who received 1 or 2 doses of vaccine, 1.18-fold in those who received 3 doses, and 3.26-fold in those who received 4 doses. CONCLUSIONS: The results suggested that the addition of a fourth dose is more effective in preventing intensive unit care even in disadvantaged groups.

Impact of Machine Learning in Digital Marketing Applications

The devastating Covid 19 pandemic has shifted priorities in the business world to accommodate the new normal that the pandemic has caused. Digital marketing has become a necessity for corporations to keep up with the evolving needs of consumers. With the help of the emerging technologies, corporations have begun embracing tailored digital marketing applications that aim to attract and retain more consumers. The techniques or applications used by businesses to improve their marketing performance are consolidated and supported by artificial intelligence applications. In this study, we examine the significance of the marketing field and machine learning, as well as marketing techniques, artificial intelligence, and machine learning algorithms. As a result of the examinations, it has been shown how machine learning techniques and algorithms facilitate marketing processes, increase the profitability of businesses and it is very difficult to realize digital marketing without machine learning algorithms. © 2022 IEEE.

Understanding COVID-19 News Coverage using Medical NLP

Being a global pandemic, the COVID-19 outbreak received global media attention. In this study, we analyze news publications from CNN and The Guardian - two of the world's most influential media organizations. The dataset includes more than 36,000 articles, analyzed using the clinical and biomedical Natural Language Processing (NLP) models from the Spark NLP for Healthcare library, which enables a deeper analysis of medical concepts than previously achieved. The analysis covers key entities and phrases, observed biases, and change over time in news coverage by correlating mined medical symptoms, procedures, drugs, and guidance with commonly mentioned demographic and occupational groups. Another analysis is of extracted Adverse Drug Events about drug and vaccine manufacturers, which when reported by major news outlets has an impact on vaccine hesitancy. © 2021 Copyright for this paper by its authors

ANN-polynomial-Fourier series modeling and Monte Carlo forecasting of tourism data

Modeling and forecasting of tourism data have received attention in the past decades. Turkey is one of the countries that benefit significantly from the tourism industry. Several time-series models have been recommended to best describe tourist arrivals to Turkey. However, in the 21st century, the world experiences great uncertainty in most possible event outcomes. These uncertainties are very difficult to account for. We proposed a hybrid artificial neural network (ANN)-polynomial-Fourier method to model the number of foreign visitors to Turkey from January 2004 to December 2020. The proposed model performance before and during the COVID-19 pandemic is evaluated separately. We evaluate the model performance by comparing with results from Danbatta and Varol (2021, ), Fourier series, and ARIMA models. To account for prediction uncertainties, we ran 300 Monte Carlo simulations within +/- 2 sigma from the model regression curve. According to the result outcomes, the proposed ANN-polynomial-Fourier has proven worthy to be considered a candidate model for the Turkish tourism data. The multistep ahead forecast suggests a 10.22% increase in the monthly foreign visitors' arrivals to Turkey in the year 2021.

Modeling and Forecasting of Tourism Time Series Data using ANN-Fourier Series Model and Monte Carlo Simulation

Tourism is counted as one of the most sensitive sectors to crises such as the COVID-19 pandemic. By the first quarter of 2020, it brought the foreign visitors' travels to a sudden and unexpected halt. This has negatively affected the tourism sector. Due to the perishable nature of the tourism industry products, many researchers are calling for urgent development and implementation of a rescue plan that will help in predicting the future number of foreign visitors. In this paper, we proposed an approach to modeling and forecasting a tourism time-series data that have both trend and seasonality. This approach is a combination of the Fourier series and artificial neural network methods to capture the seasonality and trend components in data. We applied this method to the monthly foreign visitors to Turkey dataset. We studied the data for the periods before, and during the COVID-19 pandemic. To account for uncertainties in the model prediction during the COVID-19 pandemic, we employed the Monte Carlo simulation method. We run 100 Monte Carlo simulations within ±2σ from the model curve. The mean of these 100 Monte Carlo simulation paths is computed and used for presenting the Monte Carlo forecast result values of the data. To test the feasibility of this approach, we compared the model predictions with some other existing models in the literature. In each case, the model has demonstrated a decent prediction and outperformed the benchmarked models. The proposed model produces a statistically good fit and acceptable result that can be used to forecast other tourism-related attributes. © 2021 IEEE.

Monte Carlo forecasting of time series data using Polynomial-Fourier series model

The perishable nature of tourism products and services makes forecasting an important tool for tourism planning, especially in the current COVID-19 pandemic time. The forecast assists tourism organizations in decision-making regarding resource allocations to avoid shortcomings. This study is motivated by the need to model periodic time series with linear and nonlinear trends. A hybrid Polynomial-Fourier series model that uses the combination of polynomial and Fourier fittings to capture and forecast time series data was proposed. The proposed model is applied to monthly foreign visitors to Turkey from January 2014 to August 2020 dataset and diagnostic checks show that the proposed model produces a statistically good fit. To improve the model forecast, a Monte Carlo simulation scheme with 100 simulation paths is applied to the model residue. The mean of the 100 simulation paths within +/- 2 sigma bounds from the model curve was taken and found to give statistically acceptable results.