The knowledge-based economy has drawn increasing attention recently, particularly in online shopping applications where all the transactions and consumer opinions are logged. Machine learning methods could be used to extract implicit knowledge from the logs. Industries and businesses use the knowledge to better understand the consumer behavior, and opportunities and threats correspondingly. The outbreak of coronavirus (COVID-19) pandemic has a great impact on the different aspects of our daily life, in particular, on our shopping behaviour. To predict electronic consumer behaviour could be of valuable help for managers in government, supply chain and retail industry. Although, before coronavirus pandemic we have experienced online shopping, during the disease the number of online shopping increased dramatically. Due to high speed transmission of COVID-19, we have to observe personal and social health issues such as social distancing and staying at home. These issues have direct effect on consumer behaviour in online shopping. In this paper, a prediction model is proposed to anticipate the consumers behaviour using machine learning methods. Five individual classifiers, and their ensembles with Bagging and Boosting are examined on the dataset collected from an online shopping site. The results indicate the model constructed using decision tree ensembles with Bagging achieved the best prediction of consumer behavior with the accuracy of 95.3%. In addition, correlation analysis is performed to determine the most important features influencing the volume of online purchase during coronavirus pandemic.Our research estimates COVID-19 non-fatal economic losses in the U.S. using detailed data on cumulative cases and hospitalizations from January 22, 2020 to July 27, 2020, from the Centers for Disease Control and Prevention (CDC). As of July 27, 2020, the cumulative confirmed number of cases was about 4.2 million with almost 300,000 of them entailing hospitalizations. Due to data collection limitations the confirmed totals reported by the CDC undercount the actual number of cases and hospitalizations in the U.S. Using standard assumptions provided by the CDC, we estimate that as of July 27, 2020, the actual number of cumulative COVID-19 cases in the U.S. is about 47 million with almost 1 million involving hospitalizations. Applying value per statistical life (VSL) and relative severity/injury estimates from the Department of Transportation (DOT), we estimate an overall non-fatal unadjusted valuation of $2.2 trillion for the U.S. with a weighted average value of about $46,000 per case. This is almost 40% higher than the total valuation of $1.6 trillion (using about $11 million VSL from the DOT) for all approximately 147,000 COVID-19 fatalities. We also show a variety of estimates that adjust the non-fatal valuations by the dreaded and uncertainty aspect of COVID-19, age, income, and a factor related to fatality categorization. The adjustments show current overall non-fatal valuations ranging from about $1.5 trillion to about $9.6 trillion. Finally, we use CDC forecast data to estimate non-fatal valuations through November 2020, and find that the overall cumulative valuation increases from about $2.2 trillion to about $5.7 trillion or to about 30% of GDP. Because of the larger numbers of cases involved our calculations imply that non-fatal infections are as economically serious in the aggregate as ultimately fatal infections.The COVID-19 pandemic has dramatically highlighted the isolation of domestic violence survivors, triggering media coverage and innovative efforts to reach out to those who are trapped in their homes, facing greater danger from their partners than from the virus. But another harmful aspect of this difficult time has received far less attention survivors' intensified loneliness. Although loneliness can be catalyzed by isolation, it is a distinct psychological phenomenon that is internal and subjective in nature. Loneliness is not only acutely painful in its own right; it also inflicts a range of long lasting, health-related harms, and heightens survivors' vulnerability to violence, creating a vicious cycle that may continue long after strict stay-at-home and physical distancing policies end. This may be particularly true for marginalized survivors, for whom larger structural inequalities and institutional failures compound the negative impact of loneliness. This brief report describes what we know about the nature and costs of survivor loneliness and uses the COVID-19 pandemic as a lens through which to review the ways current DV interventions may help alleviate loneliness (as distinct from isolation), and how these might be expanded to enhance survivor wellbeing, immediately and even after a return to "normal."The COVID-19 Psychological Wellbeing Study was designed and implemented as a rapid survey of the psychosocial impacts of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), known as COVID-19 in residents across the United Kingdom. This study utilised a longitudinal design to collect online survey based data. The aim of this paper was to describe (1) the rationale behind the study and the corresponding selection of constructs to be assessed; (2) the study design and methodology; (3) the resultant sociodemographic characteristics of the full sample; (4) how the baseline survey data compares to the UK adult population (using data from the Census) on a variety of sociodemographic variables; (5) the ongoing efforts for weekly and monthly longitudinal assessments of the baseline cohort; and (6) outline future research directions. We believe the study is in a unique position to make a significant contribution to the growing body of literature to help understand the psychological impact of this pandemic and inform future clinical and research directions that the UK will implement in response to COVID-19.The disinfection efficiencies of two chemical disinfectants, chlorine dioxide and weak acid hypochlorous water (WAHW), were examined in the soiled room and dishwashing room of a hospital infectious disease ward in Taiwan. The investigations were conducted in two seasons, namely winter and summer, in order to examine the correlation between the bioaerosol concentration and the environmental factors. In addition, a single-daily disinfection mode (SM) and a twice-daily disinfection mode (TM) were applied in this study. The results showed that the bacteria and fungi colony counts were strongly correlated with the temperature. Both disinfectants reduced the bacteria and fungi concentrations in the considered rooms. https://www.selleckchem.com/products/Nafamostat-mesylate.html However, of the two disinfectants, the ClO2 showed a stronger disinfection effect than the WAHW. It means that when using ClO2 as the disinfectant, the disinfection efficiency of the TM treatment mode is significantly better than that of the SM treatment mode. But, when using WAHW as the disinfectant, no significant difference is found between the disinfection efficiencies of the two methods.