Abstract
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.
•A panel data of 127 countries over two subperiods between 2020 and 2021 is used.•The extent that fiscal responses contributed to the spread and containment of the disease is explored.•case fatality ...ratios of the study data set countries are modelled with big data analytics technologies.•Country's economic development, its fiscal expenditures, and its initial fatality ratio almost entirely predict its fatality ratio over the next period.•A counterfactual exercise shows that if the less developed economies had implemented the same fiscal responses as the rich, then their fatality ratios would have declined.
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.
Given the growing importance of organizations’ environmental, social, and governance (ESG) performance, studies employing AI-based techniques to generate insights from ESG data for investors and ...managers are limited. To bridge this gap, this study proposes an AI-based multi-stage ESG performance prediction system consolidating clustering for identifying patterns within ESG data, association rule mining for uncovering meaningful relationships, deep learning for predictive accuracy, and prescriptive analytics for actionable insights. This study is grounded in the big data analytics capability view that has emerged from the dynamic capabilities theory. The model is validated using an ESG dataset of 470 Fortune listed 500 companies obtained from the Refinitiv database. The model offers practical guidance for decision-makers to maintain or enhance their ESG scores, crucial in a business landscape where ESG metrics significantly affect investor choices and public image.
Cardiovascular disease (CVD) risk prediction plays a significant role in clinical research since it is the key to primary prevention. As family health units follow up on a specific group of patients, ...particularly in the middle-aged and elderly groups, CVD risk prediction has additional importance for them. In a retrospectively collected data set from a family health unit in Turkey in 2018, we evaluated the CVD risk levels of patients based on SCORE-Turkey. By identifying additional CVD risk factors for SCORE-Turkey and grouping the study patients into 3-classes "low risk," "moderate risk," and "high risk" patients, we proposed a machine learning implemented early warning system for CVD risk prediction in family health units. Body mass index, diastolic blood pressures, serum glucose, creatinine, urea, uric acid levels, and HbA1c were significant additional CVD risk factors to SCORE-Turkey. All of the five implemented algorithms, k-nearest neighbour (KNN), random forest (RF), decision tree (DT), logistic regression (LR), and support vector machines (SVM), had high prediction performances for both the K4 and K5 partitioning protocols. With 89.7% and 92.1% accuracies for K4 and K5 protocols, KNN outperformed the other algorithms. For the five ML algorithms, while for the "low risk" category, precision and recall measures varied between 95% to 100%, "moderate risk," and "high risk" categories, these measures varied between 60% to 92%. Machine learning-based algorithms can be used in CVD risk prediction by enhancing prediction performances and combining various risk factors having complex relationships.
·Public transportation usage prediction is valuable for sustainable development.·Machine learning - XAI integrated model is developed for estimating daily public transport usage rates.·The usage ...rates of different modes of public transportation (ferry, rail) are predicted holistically.·Rule-based interpretations are presented to further explain the model.·The research findings are confirmed using Google trend online dataset.
Public transportation usage prediction is valuable for the sustainable development of transportation systems, particularly in crowded megacities. Machine learning technologies are of great interest for predicting public transportation usage. While these technologies outperform many other techniques, they suffer from limited interpretability. Explainable artificial intelligence (XAI) tools and techniques that offer post-hoc explanations of the obtained predictions are gaining popularity. This paper proposes an advanced tree-based ensemble algorithm for public transportation usage rate prediction. We aim to explain the predictions both with the most widely used technique of XAI, Shapley additive explanation (SHAP) and in the light of the rules presented. To predict the total public transportation usage, the proposed model combines all types of public transportation, categorized as ferry, railway, and bus, unlike most existing studies focusing on a single kind of public transport. Besides the sort of transportation, the day of the week, whether the day is special, and the daily ratio of passenger types were identified as model features for predicting the daily usage of each type of public transportation. We tested the proposed model using an open data set from Izmir City, Turkey. While the model had superior prediction performance, the explanations showed that the type of public transportation, weekday, and the ratio of full-fare passengers have the highest SHAP values, and the model features have many interactions. We also validated our results using an online data set showing Google search trends.
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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