Analysis of the impact of the COVID-19 pandemic on the public transport system by bus in Brazil

This article analyzes the impact of the COVID-19 pandemic on public transport by bus in two Brazilian metropolia, Belo Horizonte and Joao Pessoa. Spearman's correlation pointed out a strong relationship between the variation in the number of passengers transported and the restrictive measures to combat the COVID-19 pandemic showing that they probably dictated the use of public transport by the population. However, the correlation between the number of new confirmed cases of COVID-19 and the variation of transported users was weak in Belo Horizonte and insignificant in Joao Pessoa. Given the influence of the stringency measures, the significant differences in correlation values with the variation of passengers were identified and proven, being 60% in Belo Horizonte and 76% in Joao Pessoa. The causality test confirmed that the pandemic intensified the drop in demand for public transport. Therefore, the more severe the policy to combat the transmission of the virus, the greater the relationship with the decrease in demand for buses. Thus, the pandemic was responsible for a significant drop in the number of passengers than the estimated trend for the same period. Finally, results show a crisis in the public transport system by bus in Brazil and the urgent need to rethink strategies to attract users to this service.

Improving the Process of Early-Warning Detection and Identifying the Most Affected Markets: Evidence from Subprime Mortgage Crisis and COVID-19 Outbreak-Application to American Stock Markets.

Stock-market-crash predictability is of particular interest in the field of financial time-series analysis. Famous examples of major stock-market crashes are the real-estate bubble in 2008 and COVID-19 in 2020. Several studies have studied the prediction process without taking into consideration which markets might be falling into a crisis. To this end, a combination analysis is utilized in this manuscript. Firstly, the auto-regressive estimation (ARE) algorithm is successfully applied to electroencephalography (EEG) brain data for detecting diseases. The ARE algorithm is employed based on state-space modelling, which applies the expectation-maximization algorithm and Kalman filter. This manuscript introduces its application, for the first time, to stock-market data. For this purpose, a time-evolving interaction surface is constructed to observe the change in the surface topology. This enables tracking of the stock market's behavior over time and differentiates between different states. This provides a deep understanding of the underlying system behavior before, during, and after a crisis. Different patterns of the stock-market movements are recognized, providing novel information regarding detecting an early-warning sign. Secondly, a Granger-causality time-domain technique, called directed partial correlation, is employed to infer the underlying interconnectivity structure among markets. This information is crucial for investors and market players, enabling them to differentiate between those markets which will fall in a catastrophic loss, and those which will not. Consequently, they can make successful decisions towards selecting less risky portfolios, which guarantees lower losses. The results showed the effectiveness of the use of this methodology in the framework of the process of early-warning detection.

Interplay between Discrete Emotions and Preventive Behavior in Health Crises: Big Data Analysis of COVID-19.

Understanding the interplay between discrete emotions and COVID-19 prevention behaviors will help healthcare professionals and providers to implement effective risk communication and effective risk decision making. This study analyzes data related to COVID-19 posted by the American public on Twitter and identifies three discrete negative emotions (anger, anxiety, and sadness) of the public from massive text data. Next, econometric analyses (i.e., the Granger causality test and impulse response functions) are performed to evaluate the interplay between discrete emotions and preventive behavior based on emotional time series and Google Shopping Trends time series, representing public preventive behavior. Based on the textual analysis of tweets from the United States, the following conclusions are drawn: Anger is a Granger cause of preventive behavior and has a slightly negative effect on the public's preventive behavior. Anxiety is a Granger cause of preventive behavior and has a positive effect on preventive behavior. Furthermore, preventive behavior is a Granger cause of anxiety and has a negative and lagging effect on anxiety. Exploring how discrete emotions, such as anger and anxiety, affect preventive behaviors will effectively demonstrate how discrete emotions play qualitatively different roles in promoting preventive behaviors. Moreover, understanding the impact of preventive behaviors on discrete emotions is useful for better risk communication.

Causality Analysis for COVID-19 among Countries Using Effective Transfer Entropy.

In this study, causalities of COVID-19 across a group of seventy countries are analyzed with effective transfer entropy. To reveal the causalities, a weighted directed network is constructed. In this network, the weights of the links reveal the strength of the causality which is obtained by calculating effective transfer entropies. Transfer entropy has some advantages over other causality evaluation methods. Firstly, transfer entropy can quantify the strength of the causality and secondly it can detect nonlinear causal relationships. After the construction of the causality network, it is analyzed with well-known network analysis methods such as eigenvector centrality, PageRank, and community detection. Eigenvector centrality and PageRank metrics reveal the importance and the centrality of each node country in the network. In community detection, node countries in the network are divided into groups such that countries in each group are much more densely connected.

Learning Disease Causality Knowledge From the Web of Health Data

Health information becomes importantly valuable for protecting public health in the current coronavirus situation. Knowledge-based information systems can play a crucial role in helping individuals to practice risk assessment and remote diagnosis. The authors introduce a novel approach that will develop causality-focused knowledge learning in a robust and transparent manner. Then, the machine gains the causality and probability knowledge for inference (thinking) and accurate prediction later. In addition, the hidden knowledge can be discovered beyond the existing understanding of the diseases. The whole approach is built on a causal probability description logic framework that combines natural language processing (NLP), causality analysis, and extended knowledge graph (KG) technologies. The experimental work has processed 801 diseases in total (from the UK NHS website linking with DBpedia datasets). As a result, the machine learnt comprehensive health causal knowledge and relations among the diseases, symptoms, and other facts efficiently.

Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition and causal decomposition.

The global spread of the coronavirus disease 2019 (COVID-19) pandemic has affected the world in many ways. Due to the communicable nature of the disease, it is difficult to investigate the causal reason for the epidemic's spread sufficiently. This study comprehensively investigates the causal relationship between the spread of COVID-19 and mobility level on a multi time-scale and its influencing factors, by using ensemble empirical mode decomposition (EEMD) and the causal decomposition approach. Linear regression analysis investigates the significance and importance of the influential factors on the intrastate and interstate causal strength. The results of an EEMD analysis indicate that the mid-term and long-term domain portrays the macroscopic component of the states' mobility level and COVID-19 cases, which represents overall intrinsic characteristics. In particular, the mobility level is highly associated with the long-term variations of COVID-19 cases rather than short-term variations. Intrastate causality analysis identifies the significant effects of median age and political orientation on the causal strength at a specific time-scale, and some of them cannot be identified from the existing method. Interstate causality results show a negative association with the interstate distance and the positive one with the airline traffic in the long-term domain. Clustering analysis confirms that the states with the higher the gross domestic product and the more politically democratic tend to more adhere to social distancing. The findings of this study can provide practical implications to the policymakers that whether the social distancing policies are effectively working or not should be monitored by long-term trends of COVID-19 cases rather than short-term.

Capturing the Interplay between Risk Perception and Social Media Posting to Support Risk Response and Decision Making.

This research aims to capture the interplay between risk perception and social media posting through a case study of COVID-19 in Wuhan to support risk response and decision making. Dividing users on Sina Weibo into the government, the media, the public, and other users, we address two main research questions: Whose posting affects risk perception and vice versa? How do different categories of social media users' posts affect risk perception and vice versa? We use Granger causality analysis and impulse response functions to answer the research questions. The results show that from one perspective, the government and the media on Sina Weibo play critical roles in forming and affecting risk perceptions. From another perspective, risk perception promotes the posting of the media and the public on Sina Weibo. Since government's posting and media's posting can significantly enhance the public's perceptions of risk issues, the government and the media must remain vigilant to provide credible risk-related information.

A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19.

Since the start of the pandemic caused by the novel coronavirus, COVID-19, more than 106 million people have been infected and global deaths have surpassed 2.4 million. In Chile, the government restricted the activities and movement of people, organizations, and companies, under the concept of dynamic quarantine across municipalities for a predefined period of time. Chile is an interesting context to study because reports to have a higher quantity of infections per million people as well as a higher number of polymerize chain reaction (PCR) tests per million people. The higher testing rate means that Chile has good measurement of the contagious compared to other countries. Further, the heterogeneity of the social, economic, and demographic variables collected of each Chilean municipality provides a robust set of control data to better explain the contagious rate for each city. In this paper, we propose a framework to determine the effectiveness of the dynamic quarantine policy by analyzing different causal models (meta-learners and causal forest) including a time series pattern related to effective reproductive number. Additionally, we test the ability of the proposed framework to understand and explain the spread over benchmark traditional models and to interpret the Shapley Additive Explanations (SHAP) plots. The conclusions derived from the proposed framework provide important scientific information for government policymakers in disease control strategies, not only to analyze COVID-19 but to have a better model to determine social interventions for future outbreaks.