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Due to the high necessity of medical face masks and face shields during the COVID-19 pandemic, healthcare centers dealing with infected patients have faced serious challenges due to the high consumption rate face masks and face shields. In this regard, the supply chain of healthcare centers should put all of their efforts into avoiding any shortages of masks and shields as these products are considered as primary ways to prevent the spread of the virus. Since, any shortages in these products would lead to irrecoverable and costly consequences in terms of the mortality rate of patients and medical staff. Therefore, healthcare centers should decide on best supplier to supply required products, considering technical, and sustainability measures. Dynamicity and uncertainty of the pandemic are other factors that add up to the complexity of the supplier selection problem. Therefore, this paper develops a novel decision-making approach using Measuring attractiveness through a categorical-based evaluation technique (MACBETH) and a new combinative distance-based assessment method to address the supplier selection problem during the COVID-19 pandemic. Due to high uncertainty, vague and incomplete information for decision-making problems during the COVID-19 pandemic, the developed decision-making approach is implemented under fuzzy rough numbers as a superior uncertainty set of the traditional fuzzy set and rough numbers. Extensive sensitivity analysis tests are performed based on parameters of the decision-making approach, impacts of weight coefficients, and consistency of results in comparison to other MCDM methods. A real-life case study is investigated for a hospital in Istanbul, Turkey to show the applicability of the developed approach. Based on the results of MACBETH method, job creation and occupational health and safety systems are two top criteria. Results of the case study for five suppliers indicate that supplier (A1) is the best supplier with a distance score of 3.308. [ABSTRACT FROM AUTHOR]

The purpose of the article/hypothesis: The measures taken by the governments to fight Covid-19 such as social distancing and lockdowns not only crippled the social life, but also the economies of their countries. The purpose of this paper is to analyze the impact of the Covid-19 pandemic on the Turkish economy. The authors used the Istanbul Stock Exchange Index which is a sort of barometer to measure the health of the economic and financial system in Turkey. The aim of the paper is to show the impact of Covid-19 together with the risk in the global market, exchange rates and government responses to the pandemic on the Istanbul Stock Exchange. Methodology: The authors analyzed the impact of the pandemic studying the relations between the Istanbul Exchange and a number of deaths due to Covid-19 together with VIX index, the Government Response Index and exchange rate of USA dollar/Turkish Lira with Toda-Yamamoto Causality test. Results of the research: The results suggest that the number of deaths due to Covid-19 and exchange rates cause the loss in value in the Istanbul Stock Exchange while VIX index and the Government Response Index are found to be ineffective in explaining the volatility in the ISE. [ABSTRACT FROM AUTHOR]

Multi-Criteria Decision Making (MCDM) methods help researchers in solving many problems in terms of numerical analysis. However, MCDM methods have not been very popular in the health sector. In this study, five ones of Turkey's most intense and highly populated cities were selected and the risk of the spread of Covid-19 disease was evaluated on the basis of seven criteria. The PROMETHEE and the ELECTRE methods were conducted to rank the cities in terms of the spread of Covid-19. The PROMETHEE method correctly ranked the most risky city as Istanbul, but ELECTRE ranked Istanbul the second most risky. The results of the methods are compared with real data. PROMETHEE gave more convenient results than ELECTRE. Also, this paper offers a new field of study to the literature. [ABSTRACT FROM AUTHOR]

Artificial impermeable surfaces are becoming more prevalent, especially in urban areas, as a result of shifting land use and cover, roads, roofs, etc. The modification of land surface temperature (LST) can also be accomplished through artificially impermeable surfaces. Large artificial impermeable surfaces, such as rooftops, parking lots, and other areas of use, can be found in industrial zones, shopping malls, industrial airports, and other locations. For the Anatolian side of Istanbul, 14 Landsat 8 OLI/TIRS imagery images over the years 2016–2022 were investigated. To evaluate how well the study's images could be utilized, correlation and cosine similarity approaches were employed. A total of 12 images may be employed for research LST studies, it was discovered. We looked at closure dates during the COVID-19 epidemic to find out how human migration affected the LST. In addition, the LST value was estimated using the ordinary least squares (OLS) method employing LST and other biophysical indices. A decrease in LST values was seen as a result of the investigation. High levels of similarity and correlation were found between the images used. Results from the Google Mobility Index also provide support to the study. All of these facts provide support to Istanbul's Anatolian side experiencing lower surface temperature values, which consequently affects the city's massive structures. [ABSTRACT FROM AUTHOR]

Hazardous healthcare waste (HCW) management system is one of the most critical urban systems affected by the COVID-19 pandemic due to the increase in waste generation rate in hospitals and medical centers dealing with infected patients as well as the degree of hazardousness of generated waste due to exposure to the virus. In this regard, waste network flow would face severe problems without taking care of hazardous waste through disinfection facilities. For this purpose, this study aims to develop an advanced decision support system based on a multi-stage model that was combined with the random forest recursive feature elimination (RF-RFE) algorithm, the indifference threshold-based attribute ratio analysis (ITARA), and measurement of alternatives and ranking according to compromise solution (MARCOS) methods into a unique framework under the Fermatean fuzzy environment. In the first stage, the innovative Fermatean fuzzy RF-RFE algorithm extracts core criteria from a finite set of initial criteria. In the second stage, the novel Fermatean fuzzy ITARA determines the semi-objective importance of the core criteria. In the third stage, the new Fermatean fuzzy MARCOS method ranks alternatives. A real-life case study in Istanbul, Turkey, illustrates the applicability of the introduced methodology. Our empirical findings indicate that "Pendik" is the best among five candidate locations for sitting a new disinfection facility for hazardous HCW in Istanbul. The sensitivity and comparative analyses confirmed that our approach is highly robust and reliable. This approach could be used to tackle other critical multi-dimensional problems related to COVID-19 and support sustainability and circular economy. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to evaluate infant mortality in 2019 and 2020 years at the local level, considering the beginning of the pandemic period. Methods: The population of this cross-sectional study are infants registered in a district of Istanbul in 2019 and 2020. Infant mortality rates before and during the COVID-19 pandemic were calculated. Antenatal healthcare and delivery practices in addition to the underlying risk factors for infant deaths were identified and compared by the year of mortality. Results: Infant mortality rates were calculated as 4.8 and 5.1; neonatal mortality rates were 3.9 and 2.7; postneonatal mortality rates were 0.9 and 2.4 per thousand live births, respectively by the years. No statistically significant difference was found between maternal and infant characteristics of the two years. The number of pregnancy follow-up records was significantly higher for the infants that died in 2020 compared to 2019. Yet, there wasn't any difference in number of prenatal physician visits. Conclusion: Increase in the infant mortality rate during the pandemic compared to the pre-pandemic period is due to postneonatal mortality. The increase in postneonatal mortality is related to deaths caused by infections. This should be investigated with the characteristics of infants and healthcare accessibility features. No disruption was identified in access to antenatal care in cases of infant mortality during the research period. This continuity in health services must be preserved. Our experience during the study revealed a room for improvement in data access on always-important public health indicators for evidence-based decision-making. [ABSTRACT FROM AUTHOR]

The lockdowns and curfews during the COVID-19 pandemic have halted economic and transportation activities across the world. This study aims to investigate air pollution levels in the Marmara region, particularly in Istanbul, before and during the COVID-19 pandemic. The study used real data provided by the General Directorate of Meteorology and applied three machine learning algorithms (ANN, RBFreg, and SMOreg) to analyze air pollution data. In addition, a one-sample t-test was performed to compare air pollution levels before and during the COVID-19 pandemic in the Marmara region and Istanbul. The results of the study showed a significant reduction in the particulate matter (PM) value, which indicates the degree of air pollution, in both the Marmara region and Istanbul during the COVID-19 pandemic. The one-sample t-test results showed that the reduction in air pollution levels was statistically significant in both areas (t = 11.45, p <.001 for the Marmara region, and t = 3.188, p <.001 for Istanbul). These findings have important practical implications for decision-makers planning for a more sustainable environment. Overall, the study provides valuable insights into the impact of the COVID-19 pandemic on air pollution levels in the Marmara region, particularly in Istanbul. The application of machine learning algorithms and statistical analysis provides a rigorous approach to the investigation of this important issue by comparing before and during the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

Background/aim: Intercity travel is one of the most important parameters for combating a pandemic. The ongoing COVID-19 pandemic has resulted in different computational studies involving intercity connections. In this study, the effects of intercity connections during an epidemic such as COVID-19 are evaluated using a new network model. Materials and methods: This model considers the actual geographic neighborhood and population density data. This new model is applied to actual Turkish data by means of provincial connections and populations. A Monte Carlo algorithm with a hybrid lattice model is applied to a lattice with 8802 data points. Results: Around Monte Carlo step 70, the number of active cases in Türkiye reaches up to 8.0% of the total population, which is followed by a second wave at around Monte Carlo step 100. The number of active cases vanishes around Monte Carlo step 160. Starting with İstanbul, the epidemic quickly expands between steps 60 and 100. Simulation results fit the actual mortality data in Türkiye. Conclusion: This model is quantitatively very efficient in modeling real-world COVID-19 epidemic data based on populations and geographical intercity connections, by means of estimating the number of deaths, disease spread, and epidemic termination. [ABSTRACT FROM AUTHOR]

Introduction and aim. The fear experienced by nurses during the COVID-19 pandemic may threaten patient safety. This study was conducted with the aim of examining nurses' COVID-19 fears and attitudes to patient safety in the pandemic. Material and methods. The research had a descriptive and cross-sectional design. The research sample consisted of 245 nurses working at a teaching and research hospital in Istanbul, Turkey's most populous province. Research data were collected in June--July 2021. A Nurse Characteristics Form, the Fear of COVID-19 Scale (FCV-19S) and the Patient Safety Attitude Questionnaire were used to collect data. The data evaluation was performed using descriptive statistics, Student t test, one-way variance (ANOVA), and Pearson correlation analysis. Results. The nurses' mean score for fear of COVID-19 was 16.67±6.88, and their mean score for patient safety attitude was 141.70±27.78. Their COVID-19 fear levels and mean patient safety attitude scores were found to differ according to their intention to leave the job, their education on COVID-19 and their age. Conclusion. Nurses' experiencing of physical, social and psychological problems relating to the COVID-19 pandemic should be followed up in the long term. [ABSTRACT FROM AUTHOR]

Objectives: The clinical course of COVID-19 ranges from mild to severe. The predictability of clinical outcomes gains importance in managing the disease. In this retrospective cohort study, we aimed to investigate the relationship between biomarker levels and the clinical severity of COVID-19. Methods: COVID-19 patients (n=618) admitted to a tertiary care hospital in Istanbul, Turkey were classified according to their clinical status using a scoring system designed by WHO. Laboratory parameters such as D-dimer, ferritin, and lymphocyte count levels were evaluated. In order to find out the relation between laboratory biomarkers and the severity of COVID-19, univariable and multivariable logistic regression analyses were used. Results: A positive correlation was found when WHO Score was compared with D-dimer levels (r=.508, p<0.01), and ferritin levels (r=.391, p<0.01), whereas a negative correlation was observed between WHO Score and lymphocyte count levels (r=-.381, p<0.01). The cut-off values for D-dimer and ferritin were found as 0.86 ng/mL (70.9% sensitivity and 87.1% specificity) and 92.74 ng/mL (78.5% sensitivity and 52.2% specificity) respectively. According to the multivariable logistic regression model, lymphocyte count (ß=- 0.305, p<0.001) and D-dimer levels (β=1.326, p<0.001) are the statistically significant regressors for hospitalization need. Conclusion: Patients with higher D-dimer and ferritin levels were likely to have more severe disease, whereas patients with higher lymphocyte counts overcame the disease mildly. Unlike other studies, evaluating D-dimer and lymphocyte count will likely give more detailed information on COVID-19 clinical outcomes. [ABSTRACT FROM AUTHOR]

The world is currently struggling with a new type of coronavirus (2019-nCoV) pandemic that first appeared in Wuhan, China, and then spread to almost all countries. As in other countries of the world, public authorities in Turkey are implementing many preventive and mitigating partial lockdown (PL) actions against the virus's effects. Some decisions and policies implemented before and after March 11, 2020, when the first virus case has been identified, have reduced people and traffic circulation, which has also turned into some improvements in air quality. At this point, this study aims to investigate how this pandemic affects the air quality of a metropolis. A case study of the city of Istanbul, the most affected city with more than half of Turkey's cases, is performed. In our analysis, we observe, compare, and discuss the impact of the COVID-19 pandemic and PL decisions on Istanbul city's air quality. We consider the particulate matter (PM10), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen oxide (NO), nitrogen oxides (NOx), and ozone (O3) concentrations. We used data from 19 air monitoring stations (AMSs) and obtained improvements in the air quality for the pandemic period. In summary, the concentration levels in PM10, NO2, NO, and NOx result in a clear decline in pandemic times compared to the normal times in Istanbul. On the other hand, a non-homogenous trend for SO2 and CO concentrations is observed for different AMSs. A partial increase in O2 concentration is obtained in the comparison of before and during the PL period. [ABSTRACT FROM AUTHOR]

[Display omitted] • Types detection between COVID-19 findings. • Application of Fourier Transform and Grad-CAM techniques in the preprocessing step. • Obtaining as many types-based activation sets as the number of classes for each CNN. • Applying the local interpretable model-agnostic explanations to the activation sets. • Reclassification of selected types-based efficient sets using the Softmax method. Covid-19 is a disease that affects the upper and lower respiratory tract and has fatal consequences in individuals. Early diagnosis of COVID-19 disease is important. Datasets used in this study were collected from hospitals in Istanbul. The first dataset consists of COVID-19, viral pneumonia, and bacterial pneumonia types. The second dataset consists of the following findings of COVID-19: ground glass opacity, ground glass opacity, and nodule, crazy paving pattern, consolidation, consolidation, and ground glass. The approach suggested in this paper is based on artificial intelligence. The proposed approach consists of three steps. As a first step, preprocessing was applied and, in this step, the Fourier Transform and Gradient-weighted Class Activation Mapping methods were applied to the input images together. In the second step, type-based activation sets were created with three different ResNet models before the Softmax method. In the third step, the best type-based activations were selected among the CNN models using the local interpretable model-agnostic explanations method and re-classified with the Softmax method. An overall accuracy success of 99.15% was achieved with the proposed approach in the dataset containing three types of image sets. In the dataset consisting of COVID-19 findings, an overall accuracy success of 99.62% was achieved with the recommended approach. [ABSTRACT FROM AUTHOR]

OBJECTIVE: This study aims to calculate the medical costs of Covid-19 patients for hospitals based on the severity of clinical care. DESIGN: The study was conducted in a hospital in Istanbul/Turkey. A micro-costing approach was performed using historical cost data for one year. All direct and indirect medical inputs were determined in quantities and monetary values for four types of Covid-19 patients in the hospital. RESULTS: The analysis calculated the unit cost of an outpatient to be 459,99 ..., while the cost per day for inpatient to be 1.184,63 ..., for non-intubated in intensive care unit to be 1.938,11, for intubated in the intensive unit to be 2.393,99 .... The study also indicates that the total cost of a non-intubated patient in intensive care units is 1,54 times higher than the total cost per inpatient. An intubated patient's cost is 2,08 times higher than an inpatient's cost. CONCLUSION: This study indicates that Covid-19 patients incur significantly high costs for hospitals. The findings of the study provide empirical data for different types of patients which can be used in clinical management and can help all related governing bodies to plan their actions and make the decisions. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic, health care systems are under extreme pressure. This study analyzed health care resource use (HCRU) and costs in patients admitted to the hospital for COVID-19 and aimed to estimate the one-year direct medical cost of the disease in Turkey. Methods: This retrospective cohort study was conducted between March and July 2020 in a tertiary hospital (n = 1056) in Istanbul. Patient demographics, clinical and treatment characteristics at admission, comorbidities, disease severity, and costs from a payer perspective were evaluated using the microcosting method. The results include LOS, hospital costs, and univariate and generalized linear models to investigate influencing factors. The data were extrapolated to provide a country-level estimate. Results: The mean length of stay was 9.1 days (SD 6.9). The mean length of stay was 8.0 days (4.7) for patients hospitalized in wards versus 14.8 days (SD 12.0) for patients hospitalized in the ICU. In univariate analysis, several factors, including O2 therapy (+ 3.7 days), high CRP > 41.8 mg/L (+ 3.8 days), and elevated ferritin (+ 3.5), were found to be associated with a longer LOS (p < 0.05). The direct annual medical cost of COVID-19 was estimated at PPP$ 2.1 billion. The COVID-19 pandemic resulted in a direct medical burden that corresponds to 2.0% of the government health expenditures and 0.8 per thousand of Turkey's gross domestic product (GDP). Conclusions: Estimating the impact of this pandemic in terms of HCRU and costs to the health care system can help design strategies to manage the pandemic. [ABSTRACT FROM AUTHOR]

The most important protective measure in the pandemic process is a vaccine. The logistics and administration of the vaccine are as important as its production. The increasing diffusion of electronic devices containing geo-referenced information generates a large production of spatial data that are essential for risk management and impact mitigation, especially in the case of disasters and pandemics. Given that vaccines will be administered to the majority of people, it is inevitable to establish vaccination centres outside hospitals. Site selection of vaccination centres is a major challenge for the health sector in metropolitan cities due to the dense population and high number of daily cases. A poor site selection process can cause many problems for the health sector, workforce, health workers, and patients. To overcome this, a three-step solution approach is proposed: (i) determining eight criteria using from the experience of the advisory committee, (ii) calculating criterion weights using Analytic Hierarchy Process (AHP), and performing spatial analysis of criteria using Geographic Information System (GIS), (iii) assigning potential vaccination centres by obtaining a suitability map and determining service areas. A case study is performed for Bağcılar, Istanbul district, using the proposed methodology. The results show that the suitable areas are grouped in three different areas of the district. The proposed methodology provides an opportunity to execute a scientific and strategic vaccination programme and to create a map of suitable vaccination centres for the countries. [ABSTRACT FROM AUTHOR]