Financial resilience of two-worker households from a health perspective

The COVID-19 pandemic highlighted the significant impact of health conditions on household finance. Traditional measures of financial resilience ignored households' ability to adjust to labor income disruptions. We proposed a more comprehensive two-tier measure of financial resilience by accounting for nonlabor income and spending adjustments in the face of income loss associated with health situations. Using this measure, we evaluated the financial resilience of two-worker households with members having COVID-19 health risk conditions and other mental and physical chronic diseases. Our findings showed that households with cancer patients were more financially resilient yet those having obese members were less financially resilient. Decomposition of the financial resilience measure revealed differences in financial resources allocation-households with cancer patients allocated more wealth to noncash financial assets, whereas households with obese members saved less and spent more. Our findings shed light on financial planning practices and public policies of emergency assistance. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

U.S. Household Financial Vulnerability: Prediction Analyses in the COVID-19 Pandemic

In this article, we projected household financial vulnerability in the COVID-19 pandemic. Using a nationally representative sample of households from the 2017 Panel Study of Income Dynamics (PSID), we analyzed potential changes in financial status in the pandemic resulting from loss of income and savings from discretionary consumption. We provided a ranking of household groups by their financial vulnerability and the first estimate of the number of households at various degrees of financial vulnerability. Our study showed that a substantial part of the universal stimulus payments was made to households that had sufficient income to cover basic needs and those saved by reducing discretionary expenses. For the most financially vulnerable, the first one-time stimulus payment was too little and too late to help with their financial difficulties. Our findings shed light on to whom and in what form the US government should direct financial assistance during the pandemic.

How Fear of COVID-19 Affects the Behavioral Intention of Festival Participants-A Case of the HANFU Festival.

The recovery of the tourism industry is an important issue that has attracted much attention during the COVID-19 pandemic. Sustainable and safe festival tourism is considered an effective way of aiding in the recovery of the industry. A face-to-face survey of participants in the Guangzhou Hanfu Festival was conducted to examine the relationship between fears of COVID-19, perceived risks, perceptual evaluations, festival attitudes, behavioral intentions, and crowding during this difficult time. Results clarified how fear affects behavioral intentions in festival research, and the mediation role of perceived risk and the moderation role of crowding was confirmed. A timely set of recommendations was provided to festival operators and local governments.

Analysis of social media data for public emotion on the Wuhan lockdown event during the COVID-19 pandemic.

BACKGROUND: With outbreaks of COVID-19 around the world, lockdown restrictions are routinely imposed to limit the spread of the virus. During periods of lockdown, social media has become the main channel for citizens to exchange information with others. Public emotions are being generated and shared rapidly online with citizens using internet platforms to reduce anxiety and stress, and stay connected while isolated. OBJECTIVES: This study aims to explore the regularity of emotional evolution by examining public emotions expressed in online discussions about the Wuhan lockdown event in January 2020. METHODS: Data related to the Wuhan lockdown was collected from Sina Weibo by web crawler. In this study, the Ortony, Clore, and Collins (OCC) model, Word2Vec, and Bi-directional Long Short-Term Memory model were employed to determine emotional types, train vectorization of words, and identify each text emotion for the training set. Latent Dirichlet Allocation models were also employed to mine the various topic categories, while topic emotional evolution was visualized. RESULTS: Seven types of emotions and four phases were categorized to describe emotional evolution on the Wuhan lockdown event. The study found that negative emotions such as blame and fear dominated in the early days, and public attitudes towards the lockdown gradually alleviated and reached a balance as the situation improved. Emotional expression about Wuhan lockdown event were significantly related to users' gender, location, and whether or not their account was verified. There were statistically significant correlations between different emotions within the subtle emotional categories. In addition, the evolution of emotions presented a different path due to different topics. CONCLUSIONS: Multiple emotional categories were determined in our study, providing a detailed and explainable emotion analysis to explored emotional appeal of citizen. The public emotions were gradually easing related to the Wuhan lockdown event, there yet exists regional discrimination and post-traumatic stress disorder in this process, which would lead us to pay continuous attention to citizens lives and psychological status post-pandemic. In addition, this study provided an appropriate method and reference case for the government's public opinion control and emotional appeasement.

A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coast Population Regions.

The ongoing coronavirus disease 2019 (COVID-19) pandemic is of global concern and has recently emerged in the US. In this paper, we construct a stochastic variant of the SEIR model to estimate a quasi-worst-case scenario prediction of the COVID-19 outbreak in the US West and East Coast population regions by considering the different phases of response implemented by the US as well as transmission dynamics of COVID-19 in countries that were most affected. The model is then fitted to current data and implemented using Runge-Kutta methods. Our computation results predict that the number of new cases would peak around mid-April 2020 and begin to abate by July provided that appropriate COVID-19 measures are promptly implemented and followed, and that the number of cases of COVID-19 might be significantly mitigated by having greater numbers of functional testing kits available for screening. The model is also sensitive to assigned parameter values and reflects the importance of healthcare preparedness during pandemics.

SARS-CoV-2 Mpro inhibitors with antiviral activity in a transgenic mouse model.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continually poses serious threats to global public health. The main protease (Mpro) of SARS-CoV-2 plays a central role in viral replication. We designed and synthesized 32 new bicycloproline-containing Mpro inhibitors derived from either boceprevir or telaprevir, both of which are approved antivirals. All compounds inhibited SARS-CoV-2 Mpro activity in vitro, with 50% inhibitory concentration values ranging from 7.6 to 748.5 nM. The cocrystal structure of Mpro in complex with MI-23, one of the most potent compounds, revealed its interaction mode. Two compounds (MI-09 and MI-30) showed excellent antiviral activity in cell-based assays. In a transgenic mouse model of SARS-CoV-2 infection, oral or intraperitoneal treatment with MI-09 or MI-30 significantly reduced lung viral loads and lung lesions. Both also displayed good pharmacokinetic properties and safety in rats.

Safety and efficacy of meplazumab in healthy volunteers and COVID-19 patients: a randomized phase 1 and an exploratory phase 2 trial.

Recent evidence suggests that CD147 serves as a novel receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Blocking CD147 via anti-CD147 antibody could suppress the in vitro SARS-CoV-2 replication. Meplazumab is a humanized anti-CD147 IgG2 monoclonal antibody, which may effectively prevent SARS-CoV-2 infection in coronavirus disease 2019 (COVID-19) patients. Here, we conducted a randomized, double-blinded, placebo-controlled phase 1 trial to evaluate the safety, tolerability, and pharmacokinetics of meplazumab in healthy subjects, and an open-labeled, concurrent controlled add-on exploratory phase 2 study to determine the efficacy in COVID-19 patients. In phase 1 study, 59 subjects were enrolled and assigned to eight cohorts, and no serious treatment-emergent adverse event (TEAE) or TEAE grade ≥3 was observed. The serum and peripheral blood Cmax and area under the curve showed non-linear pharmacokinetic characteristics. No obvious relation between the incidence or titer of positive anti-drug antibody and dosage was observed in each cohort. The biodistribution study indicated that meplazumab reached lung tissue and maintained >14 days stable with the lung tissue/cardiac blood-pool ratio ranging from 0.41 to 0.32. In the exploratory phase 2 study, 17 COVID-19 patients were enrolled, and 11 hospitalized patients were involved as concurrent control. The meplazumab treatment significantly improved the discharged (P = 0.005) and case severity (P = 0.021), and reduced the time to virus negative (P = 0.045) in comparison to the control group. These results show a sound safety and tolerance of meplazumab in healthy volunteers and suggest that meplazumab could accelerate the recovery of patients from COVID-19 pneumonia with a favorable safety profile.

Mental Disorders Associated with COVID-19 Related Unemployment.

In response to the COVID-19 pandemic, restrictions on economic activities have resulted in a sharp rise of unemployment. The purpose of this research is to explore mental disorders associated with COVID-19 related unemployment using a large, nationally representative dataset, the 2020 COVID-19 Household Pulse Survey. ANOVA with post hoc tests (Tukey HSD) are utilized to reveal the mean difference of mental disorders between various employment status, as well as between reasons of unemployment. Binary logit model is used to investigate the potential effect of different reasons of unemployment on mental disorders. Individuals who were not working during the pandemic due to involuntary reasons had higher probabilities of mental disorders than those who were working and those who voluntarily separated from work. Among respondents who were not working due to COVID-19 related reasons, respondents whose employer went out of business were the most likely to experience mental disorders. Household job uncertainty in the next four weeks positively contributed to mental disorders. Government should consider measures to contain the spread of virous while keeping as many people employed as possible. Government should also consider providing adequate financial and counseling assistance to individuals who are in the greatest need for such support.

Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data.

BACKGROUND: Wuhan, China, the epicenter of the COVID-19 pandemic, imposed citywide lockdown measures on January 23, 2020. Neighboring cities in Hubei Province followed suit with the government enforcing social distancing measures to restrict the spread of the disease throughout the province. Few studies have examined the emotional attitudes of citizens as expressed on social media toward the imposed social distancing measures and the factors that affected their emotions. OBJECTIVE: The aim of this study was twofold. First, we aimed to detect the emotional attitudes of different groups of users on Sina Weibo toward the social distancing measures imposed by the People's Government of Hubei Province. Second, the influencing factors of their emotions, as well as the impact of the imposed measures on users' emotions, was studied. METHODS: Sina Weibo, one of China's largest social media platforms, was chosen as the primary data source. The time span of selected data was from January 21, 2020, to March 24, 2020, while analysis was completed in late June 2020. Bi-directional long short-term memory (Bi-LSTM) was used to analyze users' emotions, while logistic regression analysis was employed to explore the influence of explanatory variables on users' emotions, such as age and spatial location. Further, the moderating effects of social distancing measures on the relationship between user characteristics and users' emotions were assessed by observing the interaction effects between the measures and explanatory variables. RESULTS: Based on the 63,169 comments obtained, we identified six topics of discussion-(1) delaying the resumption of work and school, (2) travel restrictions, (3) traffic restrictions, (4) extending the Lunar New Year holiday, (5) closing public spaces, and (6) community containment. There was no multicollinearity in the data during statistical analysis; the Hosmer-Lemeshow goodness-of-fit was 0.24 (χ28=10.34, P>.24). The main emotions shown by citizens were negative, including anger and fear. Users located in Hubei Province showed the highest amount of negative emotions in Mainland China. There are statistically significant differences in the distribution of emotional polarity between social distancing measures (χ220=19,084.73, P<.001), as well as emotional polarity between genders (χ24=1784.59, P<.001) and emotional polarity between spatial locations (χ24=1659.67, P<.001). Compared with other types of social distancing measures, the measures of delaying the resumption of work and school or travel restrictions mainly had a positive moderating effect on public emotion, while traffic restrictions or community containment had a negative moderating effect on public emotion. CONCLUSIONS: Findings provide a reference point for the adoption of epidemic prevention and control measures, and are considered helpful for government agencies to take timely actions to alleviate negative emotions during public health emergencies.

A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coasts (preprint)

The ongoing coronavirus disease 2019 (COVID-19) pandemic is of global concern and has recently emerged in the US. In this paper, we construct a stochastic variant of the SEIR model to make a quasi-worst-case scenario prediction of the COVID-19 outbreak in the US West and East Coasts. The model is then fitted to current data and implemented using Runge-Kutta methods. Our computation results predict that the number of new cases would peak around mid-April and begin to abate by July, and that the number of cases of COVID-19 might be significantly mitigated by having greater numbers of functional testing kits available for screening. The model also showed how small changes in variables can make large differences in outcomes and highlights the importance of healthcare preparedness during pandemics.