Globalization and Pandemics

We provide theory and evidence on the relationship between global-ization and pandemics. Business travel facilitates trade and travel leads to human interactions that transmit disease. Trade-motivated travel generates an epidemiological externality across countries. If infections lead to deaths, or reduce individual labor supply, we establish a general equilibrium social distancing effect, whereby increases in relative prices in unhealthy countries reduce travel to those countries. If agents internalize the threat of infection, we show that their behavioral responses lead to a reduction in travel that is larger for higher-trade-cost locations, which initially reduces the ratio of trade to output. (JEL D91, F14, F60, I12, N30, N70, Z31)

Emotions in Covid-19 Twitter discourse following the introduction of social contact restrictions in Central Europe.

AIM: Non-pharmaceutical interventions such as lockdowns have played a critical role in preventing the spread of the Covid-19 pandemic, but may increase psychological burden. This study sought to examine emotions reflected in social media discourse following the introduction of social contact restrictions in Central Europe. SUBJECTS AND METHODS: German-language Twitter posts containing '#corona' and '#covid-19' were collected between 2020/03/18 - 2020/04/24. A total of 79,760 tweets were included in the final analysis. Rates of expressions of positive emotion, anxiety, sadness and anger were compared over time. Bi-term topic models were applied to extract topics of discussion and examine association with emotions. RESULTS: Rates of anxiety, sadness and positive emotion decreased in the period following the introduction of social contact restrictions. A total of 16 topics were associated with emotions, which related to four general themes: social contact restrictions, life during lockdown, infection-related issues, and impact of the pandemic on public and private life. Several unique patterns of association between topics and emotions emerged. CONCLUSION: Results suggest decreasing polarity of emotions among the public following the introduction of social contact restrictions. Monitoring of social media activity may prove beneficial for an adaptive understanding of changing public concerns during the Covid-19 pandemic.

Epidemiological modelling of the health and economic effects of COVID-19 control in Australia's second wave.

BACKGROUND: We investigated the public health and economy outcomes of different levels of social distancing to control a 'second wave' outbreak in Australia and identify implications for public health management of COVID-19. METHODS: Individual-based and compartment models were used to simulate the effects of different social distancing and detection strategies on Australian COVID-19 infections and the economy from March to July 2020. These models were used to evaluate the effects of different social distancing levels and the early relaxation of suppression measures, in terms of public health and economy outcomes. RESULTS: The models, fitted to observations up to July 2020, yielded projections consistent with subsequent cases and showed that better public health outcomes and lower economy costs occur when social distancing measures are more stringent, implemented earlier and implemented for a sufficiently long duration. Early relaxation of suppression results in worse public health outcomes and higher economy costs. CONCLUSIONS: Better public health outcomes (reduced COVID-19 fatalities) are positively associated with lower economy costs and higher levels of social distancing; achieving zero community transmission lowers both public health and economy costs compared to allowing community transmission to continue; and early relaxation of social distancing increases both public health and economy costs.

MLCNN-COV: A multilabel convolutional neural network-based framework to identify negative COVID medicine responses from the chemical three-dimensional conformer

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transfer-learning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses. 1225-6463/$ © 2023 ETRI.

The Role of Digital Innovation and Its Impact on Competitiveness and Performance: The Case of Business Hotel in Indonesia: Acces la Success

This research examines the effect of digital innovation on the competitiveness and performance of hospitality businesses in Indonesia. This research was conducted with a quantitative research approach. Participants in this study are managers of hotel companies that implement online systems in Indonesia. The samples in this study were 218 respondents. Hypotheses are tested using the Structural Equation Modeling method and processed using Amos Software Version 23. The results show that there is a positive and significant effect between digital innovation on competitiveness, digital innovation and competitiveness also effect hotel business performance positively and significantly. We also found that competitiveness can mediate the effect of digital innovation on business performance. Therefore, we suggest improving business performance with enhancing competitiveness, to improve competitiveness can be done by increasing the implementation of digital innovation.

Towards improved thermal comfort predictions and higher energy savings: building energy model of an open-plan office based on indoor CO2 and temperature controls

In recent work, a Hierarchical Bayesian model was developed to predict occupants' thermal comfort as a function of thermal indoor environmental conditions and indoor CO2 concentrations. The model was trained on two large IEQ field datasets consisting of physical and subjective measurements of IEQ collected from over 900 workstations in 14 buildings across Canada and the US. Posterior results revealed that including measurements of CO2 in thermal comfort modelling credibly increases the prediction accuracy of thermal comfort and in a manner that can support future thermal comfort prediction. In this paper, the predictive model of thermal comfort is integrated into a building energy model (BEM) that simulates an open-concept mechanically-ventilated office space located in Vancouver. The model predicts occupants' thermal satisfaction and heating energy consumption as a function of setpoint thermal conditions and indoor CO2 concentrations such that, for the same thermal comfort level, higher air changes per hour can be achieved by pumping a higher amount of less-conditioned fresh air. The results show that it is possible to reduce the energy demand of increasing fresh air ventilation rates in winter by decreasing indoor air temperature setpoints in a way that does not affect perceived thermal satisfaction. This paper presents a solution for building managers that have been under pressure to increase current ventilation rates during the COVID-19 pandemic. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

On stylized facts of cryptocurrencies returns and their relationship with other assets, with a focus on the impact of COVID-19

This study provides an empirical analysis on the main univariate and multivariate stylized facts iin return series of the two of the largest cryptocurrencies, namely Ethereum and Bitcoin. A Markov-Switching Vector AutoRegression model is considered to further explore the dynamic relationships between cryptocurrencies and other financial assets. We estimate the presence of volatility clustering, a rapid decay of the autocorrelation function, an excess of kurtosis and multivariate little cross-correlation across the series, except for contemporaneous returns. The analysis covers the pandemic period and sheds lights on the behaviour of cryptocurrencies under unexpected extreme events.

Under what circumstances would airborne transmission of SARS-CoV-2 occur?

To find out the circumstances under which airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) would happen, we conducted mechanistic and systematic modelling of two Coronavirus disease 2019 (COVID-19) outbreaks, i.e., Hunan 2-bus outbreak and Luk Chuen House outbreak (the horizontal cluster). Computational fluid dynamics (CFD) simulations, multi-zone airflow modelling, multi-route mechanistic modelling, and dose-response estimation were carried out selectively according to the transmission characteristics in each outbreak. Our results revealed that poorly ventilated bus indoor environments bred the Hunan 2-bus outbreak in which airborne transmission predominates;prevailing easterly background wind and probable door opening behaviour led to the secondary infections across the corridor in Luk Chuen House outbreak. Measures to facilitate sufficient ventilation indoors and positive pressure in the housing building corridor may help minimise infection risk. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Introduction

L'industrie automobile est a la croisée des chemins, car elle est soumise a une conjonction d'externalités d'une ampleur et d'une intensité rarement vues : absorption de toutes les crises récentes, celles du Covid et des semi-conducteurs, mais aussi celle de la hausse des prix de l'énergie. C'est celui de parvenir å intégrer ces techniques afin de parvenir a réinventer l'automobile de demain, une automobile connectée, robotisée et traitant automatiquement de grands volumes de données afin de progresser en intelligence, et de mettre celle-ci au service de l'humain, pour un usage réinventé de l'automobile et pour instaurer un systéme de mobilité plus durable. Il appelle ainsi a mieux cibler l'accompagnement des fournisseurs pour faire émerger une nouvelle génération d'ETI et aussi l'apparition de grands équipementiers leaders dans le monde de l'électrique, de l'hydrogene et, plus globalement, de l'automobile de demain. Il demande aussi que l'Europe se donne du temps pour s'adapter a la transition en protégeant temporairement, a l'instar des États-Unis, son marché intérieur.

HiBGT: High-Performance Bayesian Group Testing for COVID-19

The COVID-19 pandemic has necessitated disease surveillance using group testing. Novel Bayesian methods using lattice models were proposed, which offer substantial improvements in group testing efficiency by precisely quantifying uncertainty in diagnoses, acknowledging varying individual risk and dilution effects, and guiding optimally convergent sequential pooled test selections. Computationally, however, Bayesian group testing poses considerable challenges as computational complexity grows exponentially with sample size. HPC and big data stacks are needed for assessing computational and statistical performance across fluctuating prevalence levels at large scales. Here, we study how to design and optimize critical computational components of Bayesian group testing, including lattice model representation, test selection algorithms, and statistical analysis schemes, under the context of parallel computing. To realize this, we propose a high-performance Bayesian group testing framework named HiBGT, based on Apache Spark, which systematically explores the design space of Bayesian group testing and provides comprehensive heuristics on how to achieve high-performance, highly scalable Bayesian group testing. We show that HiBGT can perform large-scale test selections (> 250 state iterations) and accelerate statistical analyzes up to 15.9x (up to 363x with little trade-offs) through a varied selection of sophisticated parallel computing techniques while achieving near linear scalability using up to 924 CPU cores. © 2022 IEEE.

Template for Extended Abstract contributions to INDOOR AIR 2022

This investigation presents results of Computational Fluid Dynamics (CFD) modelling of aerosol behaviour within an arbitrary 'realistic' 100m2 office environment, with dynamic and variable respiratory droplet release profile applied based on published findings (Morawska et al., 2009). A multitude of ventilation strategies and configurations have been applied to the base model to compare the effectiveness of reducing the concentration of suspended aerosols over time. A key finding of the investigation indicates a relatively low sensitivity to increasing outside air percentage, and that the benefit from this strategy is heavily dependent on the in-duct droplet decay factor. The application of local recirculating air filtration systems with MERV-13 filters mounted on occupant desks proved significantly more effectiveness than increasing outside air concentration from 25% to 100% in reducing the quantity of suspended aerosols. This highlights that the ventilation industry should perhaps focus on opportunities to integrate filtration systems into furniture, partitions, cabinetry etc., and that an appliance-based solution may be more beneficial for reducing COVID-19 transmission in buildings (and likely more straightforward) than modifications to central ventilation systems, particularly in the application of refurbishments and retrofits. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Fault detection model for a variable speed heat pump

The impact of the COVID pandemic has resulted in many people cultivating a remote working culture and increasing building energy use. A reduction in the energy use of heating, ventilation, and air-conditioning (HVAC) systems is necessary for decreasing the energy use in buildings. The refrigerant charge of a heat pump greatly affects its energy use. However, refrigerant leakage causes a significant increase in the energy use of HVAC systems. The development of refrigerant charge fault detection models is, therefore, important to prevent unwarranted energy consumption and CO2 emissions in heat pumps. This paper examines refrigerant charge faults and their effect on a variable speed heat pump and the most accurate method between a multiple linear regression and multilayer perceptron model to use in detecting the refrigerant charge fault using the discharge temperature of the compressor, outdoor entering water temperature and compressor speed as inputs, and refrigerant charge as the output. The COP of the heat pump decreased when it was not operating at the optimum refrigerant charge, while an increase in compressor speed compensated for the degradation in the capacity during refrigerant leakage. Furthermore, the multilayer perception was found to have a higher prediction accuracy of the refrigerant charge fault with a mean square error of ± 3.7%, while the multiple linear regression model had a mean square error of ± 4.5%. The study also found that the multilayer perception model requires 7 neurons in the hidden layer to make viable predictions on any subsequent test sets fed into it under similar experimental conditions and parameters of the heat pump used in this study.

Indian Act Philanthropy: Why are Community Foundations Missing from Native Communities in Manitoba, Canada?: Revue Canadienne de Recherche sur les OSBL et l'Économie Sociale (ANSERJ)

Un modèle philanthropique axé sur le développement communautaire serait-il en train de renforcer les politiques coloniales plutôt que d'offrir des bénéfices économiques équitables? Cette étude analyse les transcriptions de vingt webinaires publics sur la philanthropie et la Loi sur les Indiens et évalue les 54 fondations communautaires établis au Manitoba, Canada. Ces 54 fondations servent seulement les villes et municipalités des colons-il n'y en a pas une seule dans les communautés autochtones. Comme elles ne desservent que leurs régions géographiques spécifiques, les fondations communautaires au Manitoba concentrent la richesse dans les villes et municipalités dominées par les colons, accaparant des ressources qui pourraient aider les communautés autochtones. Ce modèle philanthropique, en excluant les communautés les plus pauvres du Manitoba, renforce la marginalisation, la pauvreté et les risques de santé dans les communautés autochtones.Alternate :Could a philanthropic model aimed at community development enforce colonial policy rather than providing equitable economic opportunity? This research analyzes the transcripts of 20 public webinars on philanthropy and the Indian Act and maps the 54 community foundations in Manitoba, Canada. All 54 community foundations in Manitoba service only settler-dominated cities and municipalities, with none on Native communities. As community foundations serve only their specific geographical areas, the community foundations in Manitoba effectively concentrate wealth in settler-dominated cities and municipalities, taking away needed resources from Native communities. In excluding the poorest communities in Manitoba, this philanthropic model further entrenches marginalization, poverty, and health risks for Native people on Native communities.

Technology transfer in e-communication for small and medium enterprises as promoters of innovation and competitiveness

Globalization, technological innovations, and the coronavirus disease (COVID-19) pandemic have promoted disruptive changes in buying and selling negotiation models through e-communication. As a result, Small and Medium Enterprises (SMEs) have been forced to adapt to online channels. Considering market relevance, this article describes the survey results with 11 SMEs regarding their adherence to digital media. Moreover, a case study of a selected company demonstrated barriers and propulsions to digital adequacy. The aim was to promote SMEs' competitiveness through technology transfer, focusing on e-communication and strategic digital planning. The results show that the insertion of technology through digital media depends on the knowledge of the tools used in this medium. Therefore, despite being ready to use, SMEs have not yet fully leveraged digital media. Organizational barriers, such as lack of time for those responsible, lack of training and knowledge, and strategic planning, were observed. However, environmental factors such as competitive pressure and innovation-related policies are positive for insertion. Thus, there is room for companies to invest in digital strategic planning focused on improving sales, customer relations, and competitiveness. © 2022 IEEE.

Is My Model Up-to-date? Detecting CoViD-19 Variants by Machine Learning

Machine learning extracts models from huge quantities of data. Models trained and validated over past data can be deployed in making forecasts as well as in classifying new incoming data. The real world which generates data may change over time, making the deployed model an obsolete one. To preserve the quality of the currently deployed model, continuous machine learning is required. Our approach retrospectively evaluates in an online fashion the behaviour of the currently deployed model. A drift detector detects any performance slump, and, in case, can replace the previous model with an up-to-date one. The approach experiments on a dataset of 8642 hematochemical examinations from hospitalized patients gathered over 6 months: the outcome of the model predicts the RT-PCR test result about CoViD-19. The method reached an area under the curve (AUC) of 0.794, 6% better than offline and 5% better than standard online-binary classification techniques. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

The role of traditional aspects, health consciousness and environmental concerns in Italian agri-food consumption during Covid-19

PurposeThe study investigates how consumers' food purchasing habits changed during the Covid-19 pandemic in Italy. The research aims to understand if traditional aspects, health consciousness and environmental concerns have influenced and changed the purchases of food products post-pandemic.Design/methodology/approachThe authors developed a theoretical model to understand whether health consciousness, traditional aspects and environmental concerns affect consumers' purchasing intention. The study collects secondary data to analyse state of the art and investigate consumer behaviour in the agri-food system after the pandemic. Thereafter, a survey was conducted via a convenience random sampling procedure. The data (n = 622) were analysed using the formulated research framework and tested through the structural equation modelling procedure.FindingsThe findings reveal that health consciousness and traditional aspects (culinary traditions, ingredients usage from one's territory of origin, products' origin attention) are among the main reasons for purchasing agri-food goods after the pandemic. Instead, environmental concerns negatively affect consumers' purchase intentions.Originality/valueThe study identifies which aspects influenced consumers' purchasing intentions after the Covid-19 pandemic. It also provides insights for food companies and policymakers on the factors to be improved to optimize the agri-food sector following a sustainable perspective and in order to develop effective business strategies.

Age-dependent final size equation to anticipate mortality impact of COVID-19 in China

Before reopening society in December 2022, China had not achieved sufficiently high vaccination coverage among people aged 80 years and older, who are vulnerable to severe infection and death owing to COVID-19. Suddenly ending the zero-COVID policy was anticipated to lead to substantial mortality. To investigate the mortality impact of COVID-19, we devised an age-dependent transmission model to derive a final size equation, permitting calculation of the expected cumulative incidence. Using an age-specific contact matrix and published estimates of vaccine effectiveness, final size was computed as a function of the basic reproduction number, R0. We also examined hypothetical scenarios in which third-dose vaccination coverage was increased in advance of the epidemic, and also in which mRNA vaccine was used instead of inactivated vaccines. Without additional vaccination, the final size model indicated that a total of 1.4 million deaths (half of which were among people aged 80 years and older) were anticipated with an assumed R0 of 3.4. A 10% increase in third-dose coverage would prevent 30,948, 24,106, and 16,367 deaths, with an assumed second-dose effectiveness of 0%, 10%, and 20%, respectively. With mRNA vaccine, the mortality impact would have been reduced to 1.1 million deaths. The experience of reopening in China indicates the critical importance of balancing pharmaceutical and non-pharmaceutical interventions. Ensuring sufficiently high vaccination coverage is vital in advance of policy changes. ©2023 the Author(s)

Towards COVID-19 fake news detection using transformer-based models

The COVID-19 pandemic has resulted in a surge of fake news, creating public health risks. However, developing an effective way to detect such news is challenging, especially when published news involves mixing true and false information. Detecting COVID-19 fake news has become a critical task in the field of natural language processing (NLP). This paper explores the effectiveness of several machine learning algorithms and fine-tuning pre-trained transformer-based models, including Bidirectional Encoder Representations from Transformers (BERT) and COVID-Twitter-BERT (CT-BERT), for COVID-19 fake news detection. We evaluate the performance of different downstream neural network structures, such as CNN and BiGRU layers, added on top of BERT and CT-BERT with frozen or unfrozen parameters. Our experiments on a real-world COVID-19 fake news dataset demonstrate that incorporating BiGRU on top of the CT-BERT model achieves outstanding performance, with a state-of-the-art F1 score of 98%. These results have significant implications for mitigating the spread of COVID-19 misinformation and highlight the potential of advanced machine learning models for fake news detection.

Contribution of mathematical models and Big Data in epidemiological events such as SARS Cov-2

The pandemic caused by the COVID-19 virus has given rise to numerous analyses and studies due to the implications and serious consequences it has had on all areas of human development worldwide. The data unquestionably reflect the degree of impact it has had, not only on the mortality rate, but also on the economic indices of nations. In analyzing all these indicators, the question arises as to whether some key elements, such as the number of incidences, the variables of the effective reproductive factor of the disease could better reflect the predictability of the cases and, in turn, evaluate the mitigating measures to placate the incidence of new cases. This analysis is especially significant considering that the pandemic is not over, and that more and better resolutions are still needed to address this ongoing crisis. In this context, the present study aims to analyze, from the theoretical mathematical models, what has been the contribution of this area of science to find and predict possible solutions to quell the effects of this global pandemic. For this purpose, statistical analyses based on three models will be used: non-linear phenomenological models, data modeling and the generalized logistic model, which are expected to contribute to a better evaluation and understanding of the measures taken to face this health crisis and, in the future, the importance of understanding the use of data and the technological tools available to mankind today in the face of any new virus. © 2022, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Joystick Controlled Autonomous Robotic Arm Using Inverse Kinematics with Object Detection

In 2019, the Novel Coronavirus Disease (COVID-19) was categorized as a pandemic. This disease can be transmitted via droplets on items or surfaces within several hours. Therefore, the researchers aimed to develop a wirelessly controlled robot arm and platform capable of picking up objects detected via object detection. Robot arm movements are done via the use of inverse kinematics. Meanwhile, a custom object detection model that can detect objects of interest will be trained and implemented in this project. To achieve this, the researchers utilize various open-source libraries, microcontrollers, and readily available materials to construct and program the entire system. At the end of this research, the prototype could reliably detect objects of interest, along with a grab-and-dispose success rate of 88%. Instruction data can be properly sent and received, and dual web cam image transfer reaches up to 1.72 frames per second. © 2023 IEEE.