A dynamic causal modeling of the second outbreak of COVID-19 in Italy.

While the vaccination campaign against COVID-19 is having its positive impact, we retrospectively analyze the causal impact of some decisions made by the Italian government on the second outbreak of the SARS-CoV-2 pandemic in Italy, when no vaccine was available. First, we analyze the causal impact of reopenings after the first lockdown in 2020. In addition, we also analyze the impact of reopening schools in September 2020. Our results provide an unprecedented opportunity to evaluate the causal relationship between the relaxation of restrictions and the transmission in the community of a highly contagious respiratory virus that causes severe illness in the absence of prophylactic vaccination programs. We present a purely data-analytic approach based on a Bayesian methodology and discuss possible interpretations of the results obtained and implications for policy makers.

Student perceptions of the virtualization of teaching by COVID-19: the case of the Master's Degree in Tourism at the University of Huelva

The virtualization of the Tourism Master's Degree at the University of Huelva during the COVID-19 pandemic was a success and this study analyzed the key factors in this successful virtualization, according to the perception of its students. For this, it was decided to carry out a causal analysis using the methodology of fuzzy cognitive maps and the results achieved glimpsed the role of the teacher in this success. © 2022 IEEE.

The use of online teaching tools in the Tourism Master of the University of Huelva after the COVID-19 pandemic

This study points out the factors to be taken into account so that the professors of the Master's Degree in Tourism at the University of Huelva do not stop using online teaching tools in their training process after the COVID-19 pandemic, since some of which can enrich face-to-face teaching. A causal analysis was carried out through fuzzy cognitive maps and the results obtained indicated that if it is intended that these teachers continue using these tools, from the direction of the master's degree they must be allowed to use them and facilitate their use, sending them training courses on these tools, giving them accessibility to this type of tools and allowing them to use them if they require it. © 2022 IEEE.

Assessing the Causal Impact of Online Instruction due to COVID-19 on Students' Grades and its aftermath on Grade Prediction Models

The COVID-19 pandemic forced many educational institutions to transition to online learning activities. This significantly impacted various aspects of students' lives. Many of the studies aimed at assessing the impact of the online instruction on students' wellbeing and performance have mainly focused on issues such as mental health. However, the impact on student grades-a key measure of student success-has been given little attention. The handful existing studies are either focused on primary schools-where the dynamics are different from higher education-or based on statistical correlations, which are usually not causally rigorous, therefore, prone to biased estimates due to various confounding variables. There are many variables associated with students' grades, thus, to assess the causal impact of the online instruction on students' grades, there is a need for a causally-grounded approach that can control for confounding variables. To that end, we use a causal tree to investigate the impact of online instruction on the grades of the general population as well as different demographic subgroups. Our analysis is based on the demographic and engagement data for the 2019 (offline/control) and 2020 (online/treatment) cohorts of 3 mandatory courses in an Australian university. For all 3 courses, our results show that for any given student in the population, the average grade they would have gotten, had they studied offline, reduced by 3.6%, 4.7%, and 14% respectively. Further analyses show that among students with similar level of (low) engagement with the virtual learning environment, the average grade international students would have gotten, had they studied face-to-face, reduced by 19.9%, 36.6%, and 46.9% more than their domestic counterparts despite having similar engagement for the 3 courses respectively. These subgroup disparities have the potential to exacerbate existing inequalities. Given the current concerns about algorithmic bias in learning analytics (LA), we trained grade prediction models with the data and investigated for algorithmic bias. Interestingly, we find that by simply changing citizenship status, a student gets a new predicted grade, entirely different from what was initially predicted given their actual citizenship status. This implies that researchers must be careful when building LA models on COVID-19 era data. © 2022 ACM.

The Role of e-Leadership on the Productivity of Virtual Work in Higher Education

COVID-19 pandemic may further decline productivity of the workforce in the future especially in higher education. This article aims to verify the significance of e-leadership as organizational factor, digital collaboration as job factor, and digital mastery as personal factor on the productivity of virtual work in higher education. Online survey and causal analysis were conducted for supporting this article. It's about 847 academic and non-academic staffs who were participating as the respondents. PLS based Structural Equation Modelling were utilized for structuring and calculating the collected data. The result of statistical analysis reveals that e-leadership affects positively and significantly but indirectly on the productivity of virtual work. Digital mastery and digital collaboration play moderating role in determining effect of e-leadership on work productivity. For maintaining and leveraging the productivity of employee in doing virtual work, the organization should direct leadership of academic managers as transformational leadership approach for developing digital mastery and encouraging digital collaboration. © 2021 IEEE.

Fusing XGBoost and SHAP Models for Maritime Accident Prediction and Causality Interpretability Analysis

In order to prevent safety risks, control marine accidents and improve the overall safety of marine navigation, this study established a marine accident prediction model. The influences of management characteristics, environmental characteristics, personnel characteristics, ship characteristics, pilotage characteristics, wharf characteristics and other factors on the safety risk of maritime navigation are discussed. Based on the official data of Zhejiang Maritime Bureau, the extreme gradient boosting (XGBoost) algorithm was used to construct a maritime accident classification prediction model, and the explainable machine learning framework SHAP was used to analyze the causal factors of accident risk and the contribution of each feature to the occurrence of maritime accidents. The results show that the XGBoost algorithm can accurately predict the accident types of maritime accidents with an accuracy, precision and recall rate of 97.14%. The crew factor is an important factor affecting the safety risk of maritime navigation, whereas maintaining the equipment and facilities in good condition and improving the management level of shipping companies have positive effects on improving maritime safety. By explaining the correlation between maritime accident characteristics and maritime accidents, this study can provide scientific guidance for maritime management departments and ship companies regarding the control or management of maritime accident prevention.

Tourism Development During the Pandemic of Coronavirus (COVID-19): Evidence From Iran.

The coronavirus (COVID-19) epidemic has created a great deal of fear and uncertainty about health, economy, and social life. Therefore, the health, social, and economic impacts of COVID-19 are of great importance. In prone rural communities, tourism industry can contribute to the sustainable economy and social development of the villagers, and as a dynamic economic sector, cause economic, social, cultural, and environmental changes. In this regard, the purpose of this inquiry was to develop tourism during the coronavirus pandemic using the social exchange theory (SET). The present study is a descriptive, correlational and causal inquiry that is conducted using survey technique. The statistical population included tourists visiting Sistan region around Hamoun Wetland in eastern Iran (N = 850). In the sampling process, 266 tourists were selected as a sample using random sampling strategy. The study instrument was a researcher-made questionnaire, whose validity was confirmed by a panel of subjectivists and its reliability was approved by a pilot study and Cronbach's alpha coefficients (0.87≥ α ≥ 0.71). Based on SET, the proposed causal model was able to explain about 56% ( RAdj2 = 0.562) of the variance changes in tourism development during the COVID-19 epidemic.

Causal inference for the impact of economic policy on financial and labour markets amid the COVID-19 pandemic

The COVID-19 pandemic has turned the world upside down since the beginning of 2020, leaving most nations worldwide in both health crises and economic recession. Governments have been continually responding with multiple support policies to help people and businesses overcoming the current situation, from “Containment”, “Health” to “Economic” policies, and from local and national supports to international aids. Although the pandemic damage is still not under control, it is essential to have an early investigation to analyze whether these measures have taken effects on the early economic recovery in each nation, and which kinds of measures have made bigger impacts on reducing such negative downturn. Therefore, we conducted a time series based causal inference analysis to measure the effectiveness of these policies, specifically focusing on the “Economic support” policy on the financial markets for 80 countries and on the United States and Australia labour markets. Our results identified initial positive causal relationships between these policies and the market, providing a perspective for policymakers and other stakeholders.

Determining containment policy impacts on public sentiment during the pandemic using social media data.

Stringent containment and closure policies have been widely implemented by governments to prevent the transmission of COVID-19. Yet, such policies have significant impacts on people's emotions and mental well-being. Here, we study the effects of pandemic containment policies on public sentiment in Singapore. We computed daily sentiment values scaled from −1 to 1, using high-frequency data of ∼240,000 posts from highly followed public Facebook groups during January to November 2020. The lockdown in April saw a 0.1 unit rise in daily average sentiment, followed by a 0.2 unit increase with partially lifting of lockdown in June, and a 0.15 unit fall after further easing of restrictions in August. Regarding the impacts of specific containment measures, a 0.13 unit fall in sentiment was associated with travel restrictions, whereas a 0.18 unit rise was related to introducing a facial covering policy at the start of the pandemic. A 0.15 unit fall in sentiment was linked to restrictions on public events, post lock-down. Virus infection, wearing masks, salary, and jobs were the chief concerns found in the posts. A 2 unit increase in these concerns occurred even when some restrictions were eased in August 2020. During pandemics, monitoring public sentiment and concerns through social media supports policymakers in multiple ways. First, the method given here is a near real-time scalable solution to study policy impacts. Second, it aids in data-driven and evidence-based revision of existing policies and implementation of similar policies in the future. Third, it identifies public concerns following policy changes, addressing which can increase trust in governments and improve public sentiment.

Causal Analysis of Impact Factors of COVID-19 in China.

Mobility, group awareness, and temperature are considered as the important factors that may impact the increase in confirmed cases of the COVID-19[1]. This paper aims to verify the above factors on the COVID-19 and show the possible confounding factors of each research variable in reality. Based on this, we collected data about the epidemic from January 20, 2020 to February 24, 2021, including the relevant data of 31 provinces and regions in China. Plus, we use the directed acyclic graph (DAG)[2] to show the causal relationship between the above influencing factors and the confirmed daily epidemic cases, and the confounding is estimated based on DAG. The effective adjustment set of factors are used to perform the regression of the total causal effect among the explanatory variables and the confirmed cases of the epidemic using negative binomial regression. Through the comprehensive causal analysis of the decisive factors for the COVID-19, we provide strong evidence for population mobility, group awareness and the impact of weather on the epidemic, and estimates the possible confounding factors in all aspects of society. Incorporating the above factors, we provide suggestions for future decisions on the prevention of large-scale epidemics.

CAST Applied to Covid-19 Pandemic in Brazil

This work presents the results of CAST (Causal Analysis using Systems Theory) application, technique initially created to understand the aerospace accidents root causes, to identify the main reasons that may have contributed to the considerable amount of human deaths caused by the Coronavirus pandemic in Brazil. Through the CAST process, it was possible to identify the dangers involved in safety control mechanisms related to the pandemic and its health implications, understanding the events that allowed this occurrence, identifying why the components related to the system's safety were not effective and, suggest mechanisms that reinforce safety controls at national and global levels, in order to avoid or mitigate similar losses related to pandemics in future events the steps established in the CAST structured approach, allowed to analyze the factors that contributed to the high number of human deaths, obtaining a greater amount of information and details, used in the elaboration of improvement proposals to face the problem, mainly with regard to the health system. © 2021 IEEE.

R.Graph: A new risk-based causal reasoning and its application to COVID-19 risk analysis.

Various unexpected, low-probability events can have short or long-term effects on organizations and the global economy. Hence there is a need for appropriate risk management practices within organizations to increase their readiness and resiliency, especially if an event may lead to a series of irreversible consequences. One of the main aspects of risk management is to analyze the levels of change and risk in critical variables which the organization's survival depends on. In these cases, an awareness of risks provides a practical plan for organizational managers to reduce/avoid them. Various risk analysis methods aim at analyzing the interactions of multiple risk factors within a specific problem. This paper develops a new method of variability and risk analysis, termed R.Graph, to examine the effects of a chain of possible risk factors on multiple variables. Additionally, different configurations of risk analysis are modeled, including acceptable risk, analysis of maximum and minimum risks, factor importance, and sensitivity analysis. This new method's effectiveness is evaluated via a practical analysis of the economic consequences of new Coronavirus in the electricity industry.