摘要： The COVID-19 global health crisis has caused widespread concern among people worldwide, affecting their mental health and behavior. People’s response to this crisis varied greatly from country to country, with cultural tightness being one of the key influences. Countries, like China, with tighter cultures demonstrated a clear advantage. Its strict anti-pandemic measures prompted the population to adopt more effective protective behavior to curb the spread of the virus while protecting their mental health. However, research on people's attitudes towards such cultural tightness is lacking. Additionally, previous studies have mainly focused on individuals' concern of their own infections, neglecting their concern about the infections of people around them. Lastly, it is unclear whether this process varies during and post COVID-19 lockdown. To address those questions, a study of 614 mainland Chinese participants was conducted. The results indicate that, Chinese individuals were more concerned about their family, significant others and the public being infected than they were about themselves being infected. In terms of infection concern of oneself and public, individuals who more concerned desired more cultural tightness, which in turn led to better mental health and greater engagement in protective behavior. Interestingly however, people who more concerned about their family members being infected expect a looser culture during the lockdown and desire a tighter culture post-lockdown, which promotes more protective behavior and better mental health. Furthermore, the association between the desire for tightness in response to concern about COVID-19 infection and promoting mental health and protective behavior was more pronounced post-lockdown compared to lockdown phase.

摘要： The COVID-19 pandemic has underscored the importance of understanding how social norms influence preventative health behaviors across cultures. Based on the theory of normative social behavior (TNSB), this study investigates the direct and moderated effects of social norms on intentions to wear masks and practice social distancing cross-culturally during the peak of COVID-19 in 2020. Representative samples were recruited from the United States ( N  = 333) and Italy ( N  = 331), two countries with different cultural tightness-looseness and orientations (individualistic-collectivistic; indulgence-restraint; uncertainty avoidance). Results revealed that perceived descriptive norms directly influenced behavioral intentions in the U.S., while perceived injunctive norms were more influential in Italy. Outcome expectations moderated the relationship between descriptive norms and mask-wearing intentions in both countries. Specifically, as perceived benefits increased, the effect of norms on mask-wearing intentions attenuated in both countries. Group identity strengthened the association between descriptive norms and mask-wearing intentions only in Italy. These findings highlight that cultural differences play an important role in normative influence mechanisms, with the U.S. being more influenced by what others do (descriptive norms) and Italy by what others approve of (injunctive norms). The study contributes to the refinement of the TNSB and emphasizes the importance of considering cultural factors in developing effective norm-based interventions to address global health challenges.

摘要： This article investigates the impact of the COVID-19 pandemic on household food waste management behavior. By utilizing 1.6 million dining transactions at 87 restaurants in 10 Chinese cities, we find that an average 8.4 percent reduction in plate waste in restaurants associated with new COVID-19 infections at the national level during the pandemic. Further evidence shows that this change in behavior is not permanent, as the level of plate waste tends to revert to pre-pandemic levels once the pandemic came under control. We test several explanations for the empirical findings and conclude that salience bias is the most plausible explanations. This insight into waste management behavior highlights the importance of utilizing salience-friendly policy instruments to effectively implement resource conservation and waste management policy goals.

摘要： Social capital is an important social determinant of health, more specifically bridging social capital, which connects individuals and communities across societal divides. This article reports on the findings of a study about the relationship between bridging social capital and COVID-19 infection trends within the state of Arizona from October 2020 to November 2021. Economic connectedness (EC), derived from Facebook friendship connections, served as a measure of aggregated bridging social capital among residents in each ZCTA (ZIP code tabulation area). Analysis of 192 ZCTAs in Arizona revealed that below-median SES individuals had fewer above-median SES friends (mean EC = 0.86). Multiple linear regression analyses were conducted to assess the correlation between EC and biweekly COVID-19 case growth, adjusting for other social determinants of health. Results showed that higher EC was associated with slower biweekly COVID-19 case growth (p < 0.001). This suggests that bridging social capital plausibly facilitated members of underserved and vulnerable groups to better access health-related information during the COVID-19 pandemic, thereby reducing the risks of infection during the pandemic. These findings suggest that promoting bridging social capital, particularly through social network sites, could be leveraged during early phase of public health crisis. The article concludes by recommending to strengthening bridging social capital for individuals with limited access to public health information and medical care.

关键词： Suicide   Suicide-related behaviours   Adolescents   Mental health   Regression models   Bayesian analysis  

摘要： Objective Literature on temporal patterns of suicidality among youths during the COVID-19 pandemic is growing. The present work proposes a Bayesian approach to assess temporal patterns of suicide-related behaviours among inpatient adolescents during the COVID-19 pandemic. Methodology Data referred to the first hospital discharge record with ICD9-CM codes related to suicide-related behaviour and/or suicidal ideation among adolescents aged 13–19 between 1 January 2017 and 31 March 2021 were collected in the Piedmont region, Italy (n = 334; median age: 15 years, IQR: 14–16; 80% girls). A Poisson Bayesian regression model performed on pre-COVID-19 data (2017–2020), adjusted by seasonality and stratified by sex, was adopted to provide the probability that the predicted counts exceed the observed ones in each pandemic year quarter. Results A declining trend of suicidality was observed in April–June 2020 among both sex groups. Among females, an increasing pattern of suicidality was registered in early 2021 (January–March) compared to the pre-pandemic period. Conclusion The present findings contributed to a growing literature on the COVID-19 pandemic's impact on adolescents' suicide-related behaviours from a gender perspective and encouraged wider adoption of Bayesian approaches as valuable tools to explore rare events and deeply enlighten open public health issues.

关键词： Risk assessment   Prediction of COVID-19 pandemic   Deep neural network   Tunicate swarm algorithm   Data envelopment analysis   DATA ENVELOPMENT ANALYSIS   SWARM-OPTIMIZATION   FRAMEWORK   STRENGTH  

摘要： The WHO has declared that the COVID-19 pandemic is a severe health crisis. Currently, variants of concern are delta and omicron, including sub-lineages of the omicron which are XBB and BQ.1 variants. Decision planning with situation awareness is important during the COVID-19 pandemic, especially demand planning for medical supplies following pandemic probability or severity via pandemic risk assessment. Therefore, this research proposes an intelligent risk assessment on the prediction of the COVID-19 pandemic using deep learning with deep neural network (DNN) and tunicate swarm algorithm (TSA). The results show the model can accurately predict the distance and elapsed time of the next COVID-19 case based upon the previous case and evaluate the associated risks. The contribution of this research, as prediction model is based upon a DNN, it has the ability to learn and by implementing the TSA, it can improve theoretically the performance of the DNN for more precise prediction and faster convergence to the optimal solution. The prediction results are practically expanded to analyze risk assessment using probability and the data envelopment analysis (DEA). The benefit of this research is that the proposed methodology demonstrates the prediction results using risks assessment based upon intelligent risk assessment charts. The Government or those involved can use the proposed methodology to achieve a better decision-making and management to control the COVID-19 pandemic in terms of supplying the medical supplies into pandemic areas.

关键词： SARS-CoV-2   COVID-19   Systematic review   Mathematical modelling   Vaccination  

摘要： Objectives Since COVID-19 first emerged in 2019, mathematical models have been developed to predict transmission and provide insight into disease control strategies. A key research need now is for models to inform long-term vaccination policy. We aimed to review the early modelling methods utilised during the pandemic period (2019–2023) in order to identify gaps in the literature and highlight areas for future model development. Study design This study was a systematic review. Methods We searched PubMed, Embase and Scopus from 1 January 2019 to 6 February 2023 for peer-reviewed, English-language articles describing age-structured, dynamic, mathematical models of COVID-19 transmission and vaccination in high-income countries that include waning immunity or reinfection. We extracted details of the structure, features and approach of each model and combined them in a narrative synthesis. Results Of the 1109 articles screened, 47 were included. Most studies used deterministic, compartmental models set in Europe or North America that simulated a time horizon of 3.5 years or less. Common outcomes included cases, hospital utilisation and deaths. Only nine models included long COVID, costs, life years or quality of life-related measures. Two studies explored the potential impact of new variants beyond Omicron. Conclusions This review demonstrates a need for long-term models that focus on outcome measures such as quality-adjusted life years, the population-level effects of long COVID and the cost effectiveness of future policies – all of which are essential considerations in the planning of long-term vaccination strategies.

摘要： Background During the COVID-19 pandemic sex-related differences concerning the spectrum of cardiovascular complications have been observed in the acute infection, and during recovery. This study aims to emphasize sex-related disparities regarding left ventricular systolic function (LVSF), right ventricular function (RVF), diastolic dysfunction (DD), and pericardial pathologies during the post-COVID-19 syndrome. Methods 274 patients with post-acute COVID-19 syndrome, 127 men and 147 women, aged under 55, were evaluated within 90 days after the acute illness and followed at 3 and 6 months. Results Based on detailed transthoracic echocardiography (TTE), we identified significantly more frequently (p˂0.001) altered LVSF in men, while in women impaired RVF, and DD were significantly more common (p˂0.001). Pericardial impairment did not seem to be influenced by gender. The TTE parameters characterizing these patterns were correlated with the severity of the initial infection and the time elapsed since and alleviated in time. The multivariate regression analysis confirmed these sex-related associations and their impact on patients' functional status. Conclusions Male patients had a higher tendency to develop altered LVSF, while female subjects had more frequently impaired RVF and DD. These abnormalities alleviated in time and exerted a significant influence on patients' functional status.

关键词： Wastewater surveillance   SARS-CoV-2 Omicron variant   Faecal viral shedding   Infection rate   Cryptic SARS-CoV-2 variant   EPIDEMIOLOGY   TRANSMISSION   HEALTH   RISK  

摘要： Wastewater-based epidemiology (WBE) has emerged as a promising tool for monitoring the spread of COVID-19, as SARS-CoV-2 can be shed in the faeces of infected individuals, even in the absence of symptoms. This study aimed to optimize a prediction model for estimating COVID-19 infection rates based on SARS-CoV-2 RNA concentrations in wastewater, and reveal the infection trends and variant diversification in Shenzhen, China following the lifting of a strict COVID-19 strategy. Faecal samples (n n = 4337) from 1204 SARS-CoV-2 infected individuals hospitalized in a designated hospital were analysed to obtain Omicron variant-specific faecal shedding dynamics. Wastewater samples from 6 wastewater treatment plants (WWTPs) and 9 pump stations, covering 3.55 million people, were monitored for SARS-CoV-2 RNA concentrations and variant abundance. We found that the viral load in wastewater increased rapidly in December 2022 in the two districts, demonstrating a sharp peak in COVID-19 infections in late-December 2022, mainly caused by Omicron subvariants BA.5.2.48 and BF.7.14. The prediction model, based on the mass balance between total viral load in wastewater and individual faecal viral shedding, revealed a surge in the cumulative infection rate from <0.1 % to over 70 % within three weeks after the strict COVID-19 strategy was lifted. Additionally, 39 cryptic SARS-CoV-2 variants were identified in wastewater, in addition to those detected through clinical surveillance. These findings demonstrate the effectiveness of WBE in providing comprehensive and efficient assessments of COVID-19 infection rates and identifying cryptic variants, highlighting its potential for monitoring emerging pathogens with faecal shedding.

关键词： Risk perception  

摘要： Individual evacuation decision making has been studied for multiple decades mainly using theory-based approaches, such as random utility theory. This study aims to bridge the research gap that no studies have adopted data-driven approaches in modeling the compliance of hurricane evacuees with government-issued evacuation orders using survey data. To achieve this, we conducted a survey in two coastal metropolitan regions of Florida (Jacksonville and Tampa) during the 2020 Atlantic hurricane season. After preprocessing survey data, we employed three supervised learning algorithms with different complexities, namely, multinomial logistic regression, random forest, and support vector classifier, to predict evacuation decisions under various hypothetical hurricane threats. We found that the evacuation decision is mainly determined by people's perception of hurricane risk regardless of whether the government issued an order; COVID-19 risk is not a major factor in evacuation decisions but influences the destination type choice if an evacuation decision is made. Additionally, past and future evacuation destination types were found to be highly correlated. After comparing the algorithms for predicting evacuation decisions, we found that random forest can achieve satisfactory classification performance, especially for certain categories or when some categories are merged. Finally, we presented a conceptual optimization model to incorporate the data-driven modeling approach for evacuation behavior into a government-led evacuation planning framework to improve the compliance rate. © 2024 American Society of Civil Engineers.