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While the whole world struggles with the COVID-19 pandemic, there are many different measures taken by countries. In this sense, the distribution of free masks to citizens between the ages of 20–65 in Turkey is one of the important measures taken against to spread of the pandemic. This distribution process is carried out through pharmacies and people can obtain their masks from any pharmacy in their area of residence. However, this situation may cause some pharmacies to be very busy, and thus social distance cannot be maintained and health and safety of the people may be threatened. In this paper, we aim to prioritize pharmacies so that only determined pharmacies in certain regions perform mask distribution process to prevent virus transmission. For this purpose, Esenler district is taken into consideration for a pilot study which is one of the risky regions in terms of virus spread in Istanbul, Turkey. Multi-criteria decision-making approach (MCDM) is used because of the necessity of handling many factors in decision-making process and the contradiction of evaluation factors in the prioritization of pharmacies. In order to best model the uncertainty in the decision process, the MCDM approach is applied in a fuzzy environment. In addition, spherical fuzzy AHP and VIKOR MCDM approaches are used as novel hybrid method in this paper. As a result of spherical fuzzy multi-criteria analysis, the pharmacies that need to provide free mask distribution in the Esenler region have been successfully identified. [ABSTRACT FROM AUTHOR]

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]

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]

We study a home healthcare routing and scheduling problem, where multiple healthcare service provider teams should visit a given set of patients at their homes. The problem involves assigning each patient to a team and generating the routes of the teams such that each patient is visited once. When patients are prioritized according to the severity of their condition or their service urgency, the problem minimizes the total weighted waiting time of the patients, where the weights represent the triage levels. In this form, the problem generalizes the multiple traveling repairman problem. To obtain optimal solutions for small to moderate-size instances, we propose a level-based integer programming (IP) model on a transformed input network. To solve larger instances, we develop a metaheuristic algorithm that relies on a customized saving procedure and a general variable neighborhood search algorithm. We evaluate the IP model and the metaheuristic on various small-, medium- and large-sized instances coming from the vehicle routing literature. While the IP model finds the optimal solutions to all the small- and medium-sized instances within three hours of run time, the metaheuristic algorithm achieves the optimal solutions to all instances within merely a few seconds. We also provide a case study involving Covid-19 patients in a district of Istanbul and derive insights for the planners by means of several analyses. [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]

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]

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]

COVID-19 poses many challenges for hospitals around the world. Each country attempts to solve the problems in its hospitals using different methods. In Turkey, two pandemic hospitals were built in İstanbul, the most crowded province. In addition, some hospitals were designated as pandemic hospitals. This study focuses on the methods used for site selection for a pandemic hospital in Atakum, a district of Samsun City, Turkey. As a solution to the problem, initially, spatial analysis was performed using GIS to produce maps based on seven criteria obtained from the insight of an expert team. Analytic hierarchy process (AHP) augmented by interval-valued Pythagorean fuzzy numbers (PFNs) was then used to determine weights for the criteria. Distance to transportation network was the most important criterion influencing the selection process and the least significant one was the distance to fire stations. Based on the criteria weights, and five rules specified by the expert team, 13 suitable locations for a pandemic hospital were determined using GIS. The technique for order preference by similarity to ideal solution (TOPSIS) method was used to determine the final ranking of 13 alternative locations (A1–A13). A10 was identified as the most appropriate site and A11 as the least appropriate site for a pandemic hospital. Finally, sensitivity analysis was performed to investigate how changes in weight values of the criteria affect the ranking of the alternatives. [ABSTRACT FROM AUTHOR]

Background: We aimed to explore the impact of the preventive measures and partial lockdown to the psychiatric emergency department (PED) visits during COVID-19 pandemic in a mental health epicenter in Istanbul.Methods: A total of 5839 patients admitted to PED during the lockdown period (LP) between March 30 and May 31, 2020, were enrolled in this retrospective cohort study. Data of these patients were compared to those of patients in the same period in 2019 between April 1 and June 2, 2019 (non-LP). We also investigated the monthly number of PED visits and hospitalizations between March 1 and December 31, 2020, and compared it to the same period in 2019.Results: The volume of PED visits and hospitalizations in LP decreased by 12% and 41.6%, respectively. The rates of patients presenting anxiety and depressive disorders and bipolar disorders were found to significantly increase in LP than non-LP (p < 0.001; p < 0.001; p < 0.01, respectively). Depressive disorders, prior history of mental illness, and aggressive behavior were found to predict frequent PED visits while decrease in age and male gender found to predict hospitalizations. Regarding suicide attempt, younger patients and those with new-onset mental disorders were found to be at high risk in LP. Patients diagnosed with COVID-19 in PED visits were mostly with psychotic and bipolar disorders.Conclusion: Policy-makers should focus on studies on mental health services to reorganize and enhance such services, which are crucial to prevent and manage adverse mental health consequences of the pandemic and congestion in PEDs. [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]