Phone: +905347477978ORCID ID: https://orcid.org/0000-0001- Journal Pre-proof J o u r n a l P r e -p r o o f AbstractThe coronavirus pandemic, which has numerous global implications, has led people to believe that nothing will be the same as before. The present day is dominated by studies on determining the factors that affect, taking preventive actions, and trying to find an effective treatment on top priority. Meteorological parameters are among the crucial factors affecting infectious diseases. The present study examines the correlation between weather and coronavirus disease 2019 (COVID-19) by considering nine cities in Turkey. In this regard, temperature (°C), dew point (°C), humidity (%), and wind speed (mph) are considered as parameters of weather.Research states that the incubation period of COVID-19 varies from 1 day to 14 days. Therefore, the effects of each parameter within 1, 3, 7, and 14 days are examined. In addition, the population is included as an effective parameter for evaluation. The analyses are conducted based on Spearman's correlation coefficients. The results showed that the highest correlations were observed for population, wind speed 14 days ago, and temperature on the day, respectively. The study results may guide authorities and decision-makers on taking specific measures for the cities.

Abstract:The main purpose of this study was to develop and apply a neural network (NN) approach and an adaptive neuro-fuzzy inference system (ANFIS) model for forecasting the attendance rates at soccer games. The models were designed based on the characteristics of the problem. Past real data was used. Training data was used for training the models, and the testing data was used for evaluating the performance of the forecasting models. The obtained forecasting results were compared to the actual data and to each other. To evaluate the performance of the models, two statistical indicators, Mean Absolute Deviation (MAD) and mean absolute percent error (MAPE), were used. Based on the results, the proposed neural network approach and the ANFIS model were shown to be effective in forecasting attendance at soccer games. The neural network approach performed better than the ANFIS model. The main contribution of this study is to introduce two effective techniques for estimating attendance at sports games. This is the first attempt to use an ANFIS model for that purpose.

This study presents a comprehensive and comparative analysis of weighting and multiple attribute decision-making (MADM) methods in the context of sustainable energy. As the selection problems of energy involve various conflicting attributes, MADM methods have been widely applied in addressing these issues. In this study, six weighting and seven MADM methods that constitute a total of 42 models are implemented to evaluate different weighting and multicriteria decision-making methods and determine the most efficient and sustainable energy option. To determine the weights of economic, environmental, socioeconomic, and technical attributes, two subjective methods-the analytic hierarchy process and best-worst method-and four objective methods-the criteria importance through intercriteria correlation, Shannon's entropy, standard deviation, and mean weight-are used. Thus, both expert evaluations and data-based assessments are considered. Using each attribute weight provided by the six methods, the ranking of electricity generation options for Turkey is obtained through seven MADM methods: the elimination and choice expressing the reality method, the weighted sum method, the weighted product method, the organization, rangement et synthese de donnes relationnelles (ORESTE) method, the technique for order performance by similarity to the ideal solution, the preference ranking organization method for the enrichment of evaluations, and the multiple criteria optimization compromise solution. Rankings obtained from all models are integrated through the Borda, Copeland, and grade average methods. The results indicate that hydro is the optimal electricity generation option, followed by onshore wind, solar PV, geothermal, natural gas, and coal.

An artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS) models, and fuzzy rule-based system (FRBS) models are developed to predict the attendance demand in European football games, in this paper. To determine the most successful method, each of the methods is analyzed under different situations. The Elman backpropagation, feed-forward backpropagation, and cascade-forward backpropagation network types are developed to determine the outperforming ANN model. The backpropagation and hybrid optimization methods are used for training fuzzy inference system (FIS) to determine the outperforming ANFIS model. The fuzzy logic model is developed after experimenting different forms of membership functions. To this end, the data of 236 soccer games are used to train the ANN and ANFIS models, and 2017/2018 season's data of these clubs are used to test all of the models. The results of all models are compared with each other and real past data. To assess the performance of each model, two error measures that are Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD) are implemented. These measures reveal that the ANN model that has Elman network type outperforms the other models. Finally, the results emphasize that the proposed ANN model can be effectively used for prediction purposes.

Coronavirus disease 2019 is the most crucial issue of all countries worldwide as it poses a threat and risk to people in many aspects such as health and economy.Since each country's development level, economy, and infrastructure differ, countries' struggle against COVID-19 varies. Therefore, understanding the COVID-19 risk levels of countries can be crucial in determining possible strategies to take specific measures for those at the highest risk. Also, determining the risk level of countries can be more critical than estimates, such as the number of cases and deaths, as the level of risk alone can be an informative indicator for all such issues. Unlike most studies, this study concentrates on evaluating and estimating the COVID-19 risk level of countries. This study proposes two families of multivariate exponential estimators using two auxiliary attributes. Theoretically, the mean square error (MSE) equations of all proposed exponential estimators are obtained and compared with existing estimators. Some exceptional cases of the multivariate exponential estimators are regarded and compared with MSE values of proposed multivariate exponential estimators. As a result, the multivariate exponential estimators provide more efficient results than all other estimators. These theoretical findings are supported by a numerical illustration using real dataset. K E Y W O R D SCOVID-19 risk, exponential estimator, mean square error, risk assessment, two auxiliary attributes INTRODUCTIONThe World Health Organization (WHO) declared on March 11, 2020, that coronavirus disease 2019 (COVID-19) could be considered a pandemic. 1Since then, COVID-19 has caused a global crisis affecting people's health, well-being and lifestyle, and the world economy. As of January 16, 2021, there have been 92,506,811 confirmed COVID-19 cases and 2,001,773 reported deaths globally. 2 However, the number of cases and deaths varies from country to country. The main reason for this may be that each country differs in population density, cultural habits, health services, protective measures, and infrastructure. 3Management and control of COVID-19 depend primarily on the health system of a country. 4 A robust health system plays a determining role in countries' preparedness and responses to pandemics. 5 In addition, socioeconomic factors are crucial in the spread of COVID-19. [6][7][8][9] Such parameters, health system, and socioeconomic vulnerability affect the risk level of countries. It is usual for COVID-19 to spread brutally in the least developed and most vulnerable countries (countries with the highest risk). 10 In this regard, evaluating and determining the COVID-19 risk level of countries can be crucial as it is an informative indicator for most issues, including the number of COVID-19 cases and mortality rates.

Background: Coronavirus disease 2019 (COVID-19) is an invisible enemy that has made people observe issues such as eating habits, personal hygiene, and environmental factors that may affect their immune systems. Objectives: Because air pollution can affect the immune system, it is necessary to examine the relationship between air quality parameters and COVID-19.  Methods: his study examines the correlation between air quality and COVID-19 considering 7 air pollutants: PM10, SO2, CO, NO2, NOx, NO, and O3. The confirmed COVID-19 cases were considered from 9 provinces, accounting for 78% of the total cases in Turkey. The required data were collected from the websites of the country’s relevant official institutions. Two statistical tests, the Pearson correlation, and Spearman Rho were conducted to determine any potential linear and monotonic relationships.  Results: Based on both test results, a significant positive correlation was observed between air SO2 content and the number of COVID-19 cases in Turkey. Conclusion: The outcomes could help identify provincial actions or measures.

This study proposes a mathematical model of dynamic pricing for soccer game tickets. The logic behind the dynamic ticket pricing model is price change based on multipliers which reflect the effects of time and inventory. Functions are formed for the time and inventory multipliers. The optimization algorithm attempts to find optimal values of these multipliers in order to maximize revenue. By multiplying the mean season ticket price (used as the reference price) by the multipliers, dynamic ticket prices are obtained. Demand rates at different prices are needed for the model, and they are provided by a unique fuzzy logic model. The results of this model are compared with real data to test the model's effectiveness. According to the results of the dynamic pricing model, the total revenue generated is increased by 8.95% and 0.76% compared with the static pricing strategy in the first and second cases, respectively. The results of the fuzzy logic model are also found to be competitive and effective. This is the first time a fuzzy logic model has been designed to forecast the attendance of soccer games. It is also the first time this type of mathematical model of dynamic pricing for soccer game tickets has been designed.

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