Impact of COVID-19 Pandemic on Cultural Products Interests

The COVID-19 pandemic has had a significant impact on human behaviors and how it influenced peoples' interests in cultural products is an unsolved problem. While prior studies mostly adopt subjective surveys to find an answer, these methods are always suffering from high cost, limited size, and subjective bias. Inspired by the rich user-oriented data over the Internet, this work explores the possibility to leverage users' search logs to reflect humans' underlying cultural product interests. To further examine how the COVID-19 mobility policy might influence cultural interest changes, we propose a new regression discontinuity design that has the additional potential to predict the recovery phase of peoples' cultural product interests. By analyzing the 1592 search interest time series in 6 countries, we found different patterns of change in interest in movies, music, and art during the COVID-19 pandemic, but a clear overall incremental increase. Across the six countries we studied, we found that changes in interest in cultural products were found to be strongly correlated with mobility and that as mobility declined, interest in movies, music, and art increased by an average of 35, 27 and 20, respectively, with these changes lasting at least eight weeks. © 2023 ACM.

The shock, the coping, the resilience: smartphone application use reveals Covid-19 lockdown effects on human behaviors.

Human mobility restriction policies have been widely used to contain the coronavirus disease-19 (COVID-19). However, a critical question is how these policies affect individuals' behavioral and psychological well-being during and after confinement periods. Here, we analyze China's five most stringent city-level lockdowns in 2021, treating them as natural experiments that allow for examining behavioral changes in millions of people through smartphone application use. We made three fundamental observations. First, the use of physical and economic activity-related apps experienced a steep decline, yet apps that provide daily necessities maintained normal usage. Second, apps that fulfilled lower-level human needs, such as working, socializing, information seeking, and entertainment, saw an immediate and substantial increase in screen time. Those that satisfied higher-level needs, such as education, only attracted delayed attention. Third, human behaviors demonstrated resilience as most routines resumed after the lockdowns were lifted. Nonetheless, long-term lifestyle changes were observed, as significant numbers of people chose to continue working and learning online, becoming "digital residents." This study also demonstrates the capability of smartphone screen time analytics in the study of human behaviors. Supplementary Information: The online version contains supplementary material available at 10.1140/epjds/s13688-023-00391-9.

Camera-based Non-Contact Physiology Sensing

Human behavior is linked to human health and well-being. Changes in vital signs are invisible to the naked eye, but they can reveal physiological events and psychological or behavioral processes, such as emotion. Measuring these biobehavioral signals on modern smart devices has the potential to offset the high demand for help during public health emergencies, such as the COVID-19 pandemic. © 2022 IEEE.

The Game of Words Human Creative Writing Against Computer Assisted Writing

Artificial Intelligence (AI) is fully embedded in different domains of our daily lives. However, the pandemic of 2020 seems to have accelerated and changed human behavior concerning our relationship with time as well as with language and communicative processes. Traditionally, we have learned that creative written tasks need great amounts of time, inspiration, and rewriting. Conversely, speed has become an important cultural value and computer assisted creative writing enables to accelerate the writing process. This paper is part of a preliminary research on the teaching and learning of creative writing in the post-covid times. It concerns the subject of Semiotics within the context of undergraduate Business Communication students, and it aims at studying the possibilities of speed writing and creative writing AI tools. We will discuss the qualitative results of an experiment in which our students performed a creative process of producing language elements for a media campaign (hashtag, a slogan, and a teaser text) to promote a cultural institutional event targeting a broad audience. Two groups of students were given the same time to produce the language contents above-mentioned, but they had to follow different writing methodologies according to the script. The results obtained reveal interesting perceptions concerning style, connotation, grammaticality, rewriting and editing effort. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Human Mobility Driven Modeling of an Infectious Disease

In conventional disease models, disease properties are dominant parameters (e.g., infection rate, incubation pe-riod). As seen in the recent literature on infectious diseases, human behavior - particularly mobility - plays a crucial role in spreading diseases. This paper proposes an epidemiological model named SEIRD+m that considers human mobility instead of modeling disease properties alone. SEIRD+m relies on the core deterministic epidemic model SEIR (Susceptible, Exposed, Infected, and Recovered), adds a new compartment D - Dead, and enhances each SEIRD component by human mobility information (such as time, location, and movements) retrieved from cell-phone data collected by SafeGraph. We demonstrate a way to reduce the number of infections and deaths due to COVID-19 by restricting mobility on specific Census Block Groups (CBGs) detected as COVID-19 hotspots. A case study in this paper depicts that a reduction of mobility by 50 % could help reduce the number of infections and deaths in significant percentages in different population groups based on race, income, and age. © 2022 IEEE.

Understanding Desire to Touch Using Large-scale Twitter Data

I and research colleagues investigated people's desire to touch by collecting and analyzing a large amount of text data that contain phrases such as "want to touch” on Twitter. We revealed the relationship between the body part that people want to touch and the touch gesture. We also revealed the effects of the COVID-19 pandemic on the desire to touch. Specifically, we observed "skin hunger,” i.e., the strong desire for physical communication, and variation of touch avoidance toward objects such as doorknobs. Our results will be beneficial for understanding human behavior as well as for the further development of haptic technology. © 2023 Nippon Telegraph and Telephone Corp.. All rights reserved.

AutoMID: A Novel Framework For Automated Computer Aided Diagnosis Of Medical Images

Machine Learning, a subtype of AI, enables computers to mimic human behavior without explicit programming. Machine learning models aren't used very often in diagnostic imaging because there isn't enough knowledge and resources to do so. Hence, this study aims to apply automated machine learning to the diagnosis of medical images to make machine learning more accessible to non-experts. In this study, a dataset containing 2313 images each of covid-19, pneumonia and normal chest x-rays were selected and divided into testing, training, and validation datasets. The AutoGluon library was used to train and produce a model that would classify an input image and infer the probable diagnosis from the diseases it was trained upon. This study can prove that applying hyperparameter optimization and neural architecture search is able to produce high accuracy models for medical image diagnosis. © 2022 Association for Computing Machinery.

Spatiotemporal heterogeneities of the associations between human mobility and close contacts with COVID-19 infections in the United States

It has been well-established that human mobility has an inseparable relationship with COVID-19 infections. As the COVID-19 pandemic progresses, our knowledge on how human behaviors including mobility and close contact associates with the pandemic also need to stay updated. In this paper, we examine the relationship of the effective reproduction number (Rt) of COVID-19 daily cases with the two indices that provide mobility insights: Mobility Index (CMI) and Contact Index (CCI). Both relationships are evaluated through Maximal Information Coefficient (MIC). Using the Bayesian Change Point Detection and the KShape clustering algorithms, we found significant temporal and spatial heterogeneities among the relationship between two indices and the daily confirmed COVID-19 cases. Although CMI has demonstrated high correlation with COVID-19 cases in 2020, CCI became much more correlated with COVID-19 cases than CMI in 2021. During the first wave in 2020, it is also shown that mobility has a high impact on states outside of Farwest and Southeast than those states within that region. © 2022 ACM.

A web-based tool for participatory science learning in the context of human psychology research

We describe MindHive (www.mindhive.science), an online citizen science platform for human brain and behavior research that uses a participatory science learning approach to engage learners in the full spectrum of scientific inquiry. Building on an open science philosophy, it features a collaborative study design environment comprising an experiment builder, a catalogue of validated tasks and surveys, and a public-facing study page;a peer review center where students can engage with and reflect on studies designed by peers from their own schools and schools around the globe;and GDPR-compliant data collection, data management, and data visualization and interpretation functionality. We describe research generated during the COVID-19 pandemic by students to illustrate how the platform supports student-teacher-scientist community partnerships for participatory learning in authentic inquiry. © ISLS.

Optimizing Pharmaceutical and Non-pharmaceutical Interventions During Epidemics

Controlling the spread of infectious diseases is a major challenge. Understanding the dynamics between human behavior and the spread of infection is essential for policymakers. Evolving contagion dynamics make it difficult to develop an efficient mitigation strategy. In this paper, we develop an epidemiological model to forecast the epidemic and use an offline reinforcement learning framework that adapts to the evolving dynamics of disease spread to optimize the mitigation strategy. We demonstrate that our framework can produce efficient mitigation strategies for the COVID-19 pandemic based on data collected from New York, USA. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Temporal analysis of cooperative behaviour in a blockchain for humanitarian aid during the COVID-19 pandemic

In the last few years, there has been a growing interest in the subject of blockchain technology for good. Among the many endeavours, blockchain technology has lately been exploited to build complementary currencies in the sphere of humanitarian aid: currencies that support national economies to provide humanitarian aid and promote development. While there have been numerous research projects on complementary currencies (CCs) and their success, some critical aspects remain largely unexplored. First, even though cooperation is a key factor in the development of these systems, as local communities organize themselves in times of crisis, there is a lack of studies that investigate the cooperative behaviour in these systems and how it changes over time. Besides, there are only a few works studying these currencies during the recent crisis of the COVID-19 pandemic. In this work, we investigate Sarafu, a digital complementary currency based on blockchain technology. To support cooperation, Sarafu implements a special type of account, the group account, thus allowing the study of cooperation groups, that cannot be easily analyzed in other CC systems;furthermore, it was successfully used for humanitarian aid during the COVID-19 pandemic. We find that Sarafu users show strong cooperative behaviour, facilitated by the usage of these group accounts. Furthermore, we observe the increasing importance of cooperation groups over time, as well as differences over time in their spending behaviour. From the analysis, we highlight the presence of cooperation patterns and the importance of group accounts, a takeaway for current and future humanitarian projects. © 2022 ACM.

Development of E-Module based on Phenomenon-based Learning in Thermochemistry to Facilitate Student during Covid-19 Pandemic

Covid-19 pandemic has affected psychological condition and alteration of human behavior. This also has an impact on education system in Indonesia. To continue the learning process, an effort is needed by conducting an online learning. To bolster up the implementation of online learning processes, e-module is needed to be used as online teaching material. This study aims to develop an e-module based on phenomenon-based learning in Thermochemistry material which is feasible to be implemented in the learning processes in school by offline and online. This study used a research and development (R & D) study using Plomp model. Data collection techniques used expert validation questionnaires and user response questionnaires. Feasibility level of e-module based on phenomenon-based learning in thermochemistry seen form the function aspect as a teaching source based on the experts and users' research is 98.3%;hence, the e-module based on phenomenon-based learning which developed is feasible to be implemented as the teaching sources. The e-module based on phenomenon-based learning is tested to 10 high school students, and then they are requested to fill out the respondent questionnaires and the results are 90.13%. The advantages of e-module based on phenomenon-based learning are: (1) equipped with a description of phenomena-based material found in everyday life;(2) equipped with critical questions to improve students' critical thinking skills;(3) equipped with learning videos as a support for students to repeat the teaching material;(4) and it can be used anywhere and anytime. © 2021 IEEE.

Understanding and Rewiring Cities

Nowadays, the ever increasing digitization of our societies is producing an unprecedented amount of data about human behavior. At the same time, advances in machine learning and complex systems enable us to build explanatory and/or predictive computational models of human behavior. Interestingly, these data and models can also be used to better understand the factors associated with specific neighborhoods’ outcomes such as vitality, safety perception, crime levels, innovation, segregation, traffic congestion, etc., and to design more efficient policymakers’ interventions. In particular, leveraging census data, mobile phone traces, information from OpenStreetMap, and street view images, we describe a set of studies where we (i) infer how vital and livable a city is;(ii) find urban appearance conditions that magnify and influence urban life;(iii) study the relationship of urban conditions with societal outcomes such as urban crime levels;and (iv) model the impact of pandemic shocks such as COVID-19 and related non-pharmaceutical interventions on human behavior. © 2022, Springer Nature Switzerland AG.

Modelling the Effect of Vaccination and Human Behaviour on the Spread of Epidemic Diseases on Temporal Networks

Motivated by the increasing number of COVID-19 cases that have been observed in many countries after the vaccination campaign and relaxation of non-pharmaceutical interventions (NPIs), we propose a network model for the spread of recurrent epidemic diseases in a partially vaccinated population. The model encapsulates several realistic features, such as different vaccine efficacy against transmission and development of severe symptoms, testing practices, implementation of NPIs, isolation of detected individuals, and human behaviour. Using a mean-field approach, we analytically derive the epidemic threshold of the model and, if the system is below such a threshold, we compute the epidemic prevalence at the endemic equilibrium. These theoretical results show that precautious human behaviour and effective testing practices are key towards avoiding epidemic outbreaks. Interestingly, we found that, in many realistic scenarios, vaccination is successful in mitigating the outbreak by reducing the prevalence of seriously ill patients, but it could be a double-edged sword, favouring resurgent outbreaks, and it thus calls for higher testing rates, more cautiousness and responsibility among the population, or the reintroduction of NPIs to achieve full eradication. © 2022 EUCA.

Standardized Risk Mitigation Measurement in Extended Reality Environments Utilizing the IEEE Experience API (xAPI) Standard

Recent reports indicate increased organizational appetite and spend in the energy industry in both the areas of operational risk management training and enablement and in extended reality hardware and software, as part of larger automation and digital transformation initiatives. Furthermore, recent advances in immersive technology, along with more dispersed, asynchronous working conditions due to COVID, have resulted in scalable, immersive simulations that more and more closely resemble real world environments. While recent standards have defined JSON syntax appropriate for tracking and measuring human behavior data in generic learning environments (IEEE P9274.1) and in a manner that more closely approximates human behavior in the workplace, as typically tracked in operational risk management systems, no risk-based ontology has yet been defined that more closely crosswalks and correlates data from simulated environment systems to those in operational environments. Thus, the true efficacy of extended reality-based risk mitigation training cannot be fully measured. In this effort, a risk-based ontology and matrix was constructed in accordance with the xAPI standard syntax and allowable extensions and was utilized to transform a subset of historical data from simulated operational risk-based scenarios from the energy industry. Transformed data from this initial subset closely approximated operational risk reporting data and provided insights into human behavior data in simulated environments that can be easily compared and correlated to existing operational excellence and risk mitigation KPIs. Implications for mapping of additional advanced data from simulated environments in larger, more complex datasets, such as eye tracking and biometrics, were also considered and explored. © 2022 IEEE.

Assessing the Impact of SARS-CoV-2 on Water Consumption in São Paulo State, Brazil

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2020 led to a significant change in human behaviors, mainly because of the quarantine to avoid the spread of the virus. Measures affected both economic activities and citizens' behaviors as they developed more intense hygiene habits to avoid contamination and switched to home offices. These exceptional behaviors also affected the way that water is consumed and need to be fully understood to manage supply systems. Therefore, this study aims to investigate changes in residential and commercial water consumption in 31 municipalities in the state of São Paulo, Brazil, during SARS-CoV-2. To do this, the expected consumption for the first half of 2020 was forecasted using the Holt-Winters multiplicative method and compared with the data observed for the same period. In addition, we compared monthly records of new contaminations and the social distancing index to establish a correlation with changes in water consumption. The results show an average difference between forecasted and observed consumption equal to +6.23% and -18.59% for residential and commercial activities, respectively. For the first one, the consumption per capita increased at the rate of 8.44 L.person-1.day-1. The observed changes in consumption seem to be a consequence of hygiene habits, social distancing and the closing of nonessential services in commerce. © 2022 American Society of Civil Engineers.

The Effect of Face Mask and Approach Pattern on Interpersonal Distance in COVID-19 Pandemic Using VR Technology

In the COVID-19 pandemic, control measures including wearing masks, ensuring hand hygiene, and maintaining a physical distance of at least 1 m were recommended to prevent the spread of virus. The purpose of this study was to investigate the influence of face mask, approach pattern and participants’ gender on interpersonal distance in the pandemic environment. Virtual reality (VR) technology was applied to build the experimental environment. This study recruited 31 participants including 17 males and 14 females, who were asked to interact with virtual confederates with and without a face mask. The interpersonal distance was recorded when participants actively walk towards the virtual confederate or approached passively by the confederate. Three-way ANOVA results showed that face mask and approach pattern had significant effects on interpersonal distance. The distance when facing the confederate with a face mask was significantly closer than without a face mask. Moreover, participants preferred a significantly larger distance in the passive pattern than in the active pattern. The participants’ gender showed no significant effect on interpersonal distance and no interaction effects were found. The findings in this study helped to further investigate the nature of interpersonal distance and contributed to a better understanding of the human behaviors in the pandemic environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Fuzzy Decision-Making in Crisis Situation: Crowd Flow Control in Closed Public Spaces in COVID’19

Human behavior in a crisis situation can be very different from what is expected. Although intrinsically related to the personality of individuals and several other educational and innate parameters, in response to crisis situations, several emotional characters, and spontaneous behaviors can be triggered in search of outcomes. These psychological expressions are multiple and can be followed by variable decision-making inadequacy with the situation. This paper presents the impact of fuzzy behavior in the decision-making process in a crisis. The objective is to control the flow in public closed spaces while avoiding crowd formation to prevent contamination of the COVID’19 virus. We evaluate our model by simulating passenger behaviors in a closed public area during the post-pandemic COVID’19 context. In the experiments, we show the impact of the combination of rationality and emotional characters on the traffic flow and the risk of the pandemic spread. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

EV Charging Behavior Under Lockdown: A Multi-Method Comparison

The COVID-19 pandemic has affected human behavior drastically in various ways, including commuter patterns and traffic volumes. This paper investigates how the COVID-19 outbreak has changed the user habits and utilization patterns at public electric vehicle service equipment (EVSE). More than 7,300 charging sessions collected at 54 public Level 2 charging stations across the State of Rhode Island were analyzed using a multi-method approach comparing charging events from two time periods, before and during the pandemic. The study shows that charging behavior has changed significantly since the COVID-19 outbreak. We found that the energy consumption, charging duration, distance from home, and charging frequency decreased significantly during the pandemic. Additionally, the study discovered a relationship between the observation period and the day of the charging session. During the pandemic, charging on Sundays has become significantly more important for users than charging between Monday and Friday. We provide important insights for policymakers about how the COVID-19 pandemic has changed electric vehicle user charging behavior and demand. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

Exploring the role of spatial configuration and human behavior on the spread of the epidemic: A study of factors that affect Covid-19 spreading in the city

This research explores how exterior public space - defined through the configuration of the city - and human behavior affect the spread of disease. In order to understand the virus spreading mechanism and influencing factors of the epidemic which accompany residents' movement, this study attempts to reproduce the process of virus spreading in city areas through computer simulation. The simulation can be divided into residents' movement simulation and the virus spreading simulation. First, the Agent-based model (ABM) can effectively simulate the behavior of the individual and crowd;real location data - uploaded by residents via mobile phone applications - is used as a behavioral driving force for the agent's movement. Second, a mathematical model of infectious diseases is constructed based on SIR (SEIR) Compartmental models in epidemiology. Finally, by analyzing the simulation results of the agent's movement in the city and the virus spreading under different conditions, the influence of multiple factors of city configuration and human behavior on the virus spreading process is explored, and the effectiveness of countermeasures such as social distancing and lockdown are further demonstrated. © Association for Computer Aided Design in Architecture Annual Conference, ACADIA 2021.