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    Big data analytics and COVID-19: investigating the relationship between government policies and cases in Poland, Turkey and South Korea
    (Oxford Univ Press, 2022) Sozen, Mert Erkan; Sariyer, Gorkem; Ataman, Mustafa Gökalp
    We used big data analytics for exploring the relationship between government response policies, human mobility trends and numbers of coronavirus disease 2019 (COVID-19) cases comparatively in Poland, Turkey and South Korea. We collected daily mobility data of retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential areas. For quantifying the actions taken by governments and making a fairness comparison between these countries, we used stringency index values measured with the `Oxford COVID-19 government response tracker'. For the Turkey case, we also developed a model by implementing the multilayer perceptron algorithm for predicting numbers of cases based on the mobility data. We finally created scenarios based on the descriptive statistics of the mobility data of these countries and generated predictions on the numbers of cases by using the developed model. Based on the descriptive analysis, we pointed out that while Poland and Turkey had relatively closer values and distributions on the study variables, South Korea had more stable data compared to Poland and Turkey. We mainly showed that while the stringency index of the current day was associated with mobility data of the same day, the current day's mobility was associated with the numbers of cases 1 month later. By obtaining 89.3% prediction accuracy, we also concluded that the use of mobility data and implementation of big data analytics technique may enable decision-making in managing uncertain environments created by outbreak situations. We finally proposed implications for policymakers for deciding on the targeted levels of mobility to maintain numbers of cases in a manageable range based on the results of created scenarios.
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    Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations
    (Springer, 2022) Sariyer, Gorkem; Ataman, Mustafa Gökalp; Mangla, Sachin Kumar; Kazancoglu, Yigit; Dora, Manoj
    Grounded in dynamic capabilities, this study mainly aims to model emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics (BDA) technologies. Since government may impose some restrictions and prohibitions in coping with emergencies to protect the functioning of EDs, it also aims to investigate how such policies affect ED operations. The proposed model is designed by collecting big data from multiple sources and implementing BDA to transform it into action for providing efficient responses to emergencies. The model is validated in modeling the daily number of patients, the average daily length of stay (LOS), and daily numbers of laboratory tests and radiologic imaging tests ordered. It is applied in a case study representing a large-scale ED. The data set covers a seven-month period which collectively means the periods before COVID-19 and during COVID-19, and includes data from 238,152 patients. Comparing statistics on daily patient volumes, average LOS, and resource usage, both before and during the COVID-19 pandemic, we found that patient characteristics and demographics changed in COVID-19. While 18.92% and 27.22% of the patients required laboratory and radiologic imaging tests before-COVID-19 study period, these percentages were increased to 31.52% and 39.46% during-COVID-19 study period. By analyzing the effects of policy-based variables in the model, we concluded that policies might cause sharp decreases in patient volumes. While the total number of patients arriving before-COVID-19 was 158,347, it decreased to 79,805 during-COVID-19. On the other hand, while the average daily LOS was 117.53 min before-COVID-19, this value was calculated to be 165,03 min during-COVID-19 study period. We finally showed that the model had a prediction accuracy of between 80 to 95%. While proposing an efficient model for sustainable operations management in EDs for dynamically changing environments caused by emergencies, it empirically investigates the impact of different policies on ED operations.
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    Fiscal responses to COVID-19 outbreak for healthy economies: Modelling with big data analytics
    (Elsevier, 2023) Sarıyer, Görkem; Kahraman, Serpil; Sözen, Mert Erkan; Ataman, Mustafa Gökalp
    Fiscal responses to the COVID-19 crisis have varied a lot across countries. Using a panel of 127 countries over two separate subperiods between 2020 and 2021, this paper seeks to determine the extent that fiscal responses contributed to the spread and containment of the disease. The study first documents that rich countries, which had the largest total and health-related fiscal responses, achieved the lowest fatality rates, defined as the ratio of COVID-related deaths to cases, despite having the largest recorded numbers of cases and fatalities. The next most successful were less developed economies, whose smaller total fiscal responses included a larger health-related component than emerging market economies. The study used a promising big data analytics technology, the random forest algorithm, to determine which factors explained a country's fatality rate. The findings indicate that a country's fatality ratio over the next period can be almost entirely predicted by its economic development level, fiscal expenditure (both total and health-related), and initial fatality ratio. Finally, the study conducted a counterfactual exercise to show that, had less developed economies implemented the same fiscal responses as the rich (as a share of GDP), then their fatality ratios would have declined by 20.47% over the first period and 2.59% over the second one.
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    Mode of arrival aware models for forecasting flow of patient and length of stay in emergency departments
    (Galenos Publ House, 2022) Ataman, Mustafa Gökalp; Sariyer, Gorkem
    Aim: Flow of patients to emergency departments (EDs) and their stays in EDs (ED-LOS) depend significantly on their arrival modes. In this study, developing effective models for forecasting patient flow and length of stay (LOS) in EDs by considering arrival modes led better planning of ED operations. Materials and Methods: In this study, by categorizing the mode of arrival into two, self-arrived in and by ambulance, autoregressive integrative moving average (ARIMA) models are applied for forecasting four time series: daily number of patients self arrived/arrived by an ambulance and average LOS of patients self-arrived/arrived by an ambulance. The models are validated with real-life data received from a large-scaled urban ED in Izmir, Turkey. Results: While seasonal ARIMA is proper for forecasting the daily number of patients on both modes, non-seasonal models are proper for forecasting the average LOS. The mean absolute percentage errors (MAPE) for the models of four time series are 5,432%, 13,085%, 9,955% and 10.984%, respectively. Thus, daily arrivals to the EDs show seasonality patterns. Conclusion: By emphasizing the impact of mode of arrival in ED context, this study can be used to aid the strategic decision making in the EDs for capacity planning to enable efficient use of the ED resources.
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    The power of governments in fight against covıd-19 - high-performing health systems or government response policies?
    (Walter De Gruyter Gmbh, 2023) Sariyer, Gorkem; Sozen, Mert Erkan; Ataman, Mustafa Gökalp
    Due to the pandemic situation caused by COVID-19 disease, there have been tremendous efforts worldwide to keep the spread of the virus under control and protect the functioning of health systems. Although governments take many actions in fighting this pandemic, it is well known that health systems play an undeniable role in this fight. This study aimed to investigate the role of health systems and government responses in fighting COVID-19. By purposively sampling Finland, Denmark, the UK, and Italy and analyzing their health systems' performances, governments' stringency indexes, and COVID-19 spread variables, this study showed that high-performing health systems were the main power of states in managing pandemic environments. This study also measured relations between short and medium-term measures and COVID-19 case and death numbers in all study countries. It showed that medium-term measures had significant effects on death numbers.
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    Predicting waiting and treatment times in emergency departments using ordinal logistic regression models
    (W B Saunders Co-Elsevier Inc, 2021) Ataman, Mustafa Gökalp; Sarıyer, Görkem
    Background: Since providing timely care is the primary concern of emergency departments (EDs), long waiting times increase patient dissatisfaction and adverse outcomes. Especially in overcrowded ED environments, emergency care quality can be significantly improved by developing predictive models of patients' waiting and treatment times to use in ED operations planning. Methods: Retrospective data on 37,711 patients arriving at the ED of a large urban hospital were examined. Ordinal logistic regression models were proposed to identify factors causing increased waiting and treatment times and classify patients with longer waiting and treatment times. Results: According to the proposed ordinal logistic regression model for waiting time prediction, age, arrival mode, and ICD-10 encoded diagnoses are all significant predictors. The model had 52.247% accuracy. The model for treatment time showed that in addition to age, arrival mode, and diagnosis, triage level was also a significant predictor. The model had 66.365% accuracy. The model coefficients had negative signs in the corresponding models, indicating that waiting times are negatively related to treatment times. Conclusion: By predicting patients' waiting and treatment times, ED workloads can be assessed instantly. This enables ED personnel to be scheduled to better manage demand supply deficiencies, increase patient satisfaction by informing patients and relatives about expected waiting times, and evaluate performances to improve ED operations and emergency care quality. (c) 2021 Elsevier Inc. All rights reserved.