Implemented Lockdown Measures Research Articles

Comparative Assessment of the Impact of COVID-19 Lockdown on Air Quality: A Multinational Study of SARS-CoV-2 Hotspots.

In response to the global COVID-19 pandemic, nations implemented lockdown measures to contain the virus. This study assessed air pollution levels during and after lockdowns, focusing on the following heavily affected locations: Oulu and Helsinki in Finland, Paris in France, Madrid in Spain, Milan in Italy, and Wuhan in China. Air Quality Index (AQI) data from these locations over two years were analyzed to understand the effects of lockdowns. The study compared COVID-19 lockdowns in these six cities with SARS-CoV-2 measurements using statistical methods. Variations in outdoor pollutants were evaluated through tests, revealing significant differences. Parametric analyses and regression were employed to study the impacts of lockdown measures on pollution and their relationships. The study comprehensively analyzed the effects of COVID-19 lockdowns on air quality, identifying differences, quantifying changes, and exploring patterns in each city. Pollutant correlations varied among cities during the lockdowns. Regression analysis highlighted the impact of independent variables on pollutants. Decreases in NO2 were observed in Helsinki, Madrid, Oulu, Paris, and Milan, reflecting reduced traffic and industrial activities. Reductions in PM2.5 and PM10 were noted in these cities and in Wuhan, except for O3 levels, which increased. The reduction in human activities improved air quality, particularly for NO2 and PM10. Regional variations underscore the need for tailored interventions. The study observed a substantial decrease in both PM2.5 and NO2 levels during the COVID-19 lockdowns, indicating a direct correlation between reduced human activities, such as transportation and industrial operations, and improved air quality. This underscores the potential impact of environmental measures and suggests the need for sustainable practices to mitigate urban pollution.

Robust-mv-M-LSTM-CI: Robust Energy Consumption Forecasting in Commercial Buildings during the COVID-19 Pandemic

The digitalization of the global landscape of electricity consumption, combined with the impact of the pandemic and the implementation of lockdown measures, has required the development of a precise forecast of energy consumption to optimize the management of energy resources, particularly in pandemic contexts. To address this, this research introduces a novel forecasting model, the robust multivariate multilayered long- and short-term memory model with knowledge injection (Robust-mv-M-LSTM-CI), to improve the accuracy of forecasting models under uncertain conditions. This innovative model extends the capabilities of mv-M-LSTM-CI by incorporating an additional branch to extract energy consumption from adversarial noise. The experiment results show that Robust-mv-M-LSTM-CI demonstrates substantial improvements over mv-M-LSTM-CI and other models with adversarial training: multivariate multilayered long short-term memory (adv-M-LSTM), long short-term memory (adv-LSTM), bidirectional long short-term memory (adv-Bi-LSTM), and linear regression (adv-LR). The maximum noise level from the adversarial examples is 0.005. On average, across three datasets, the proposed model improves about 24.01% in mean percentage absolute error (MPAE), 18.43% in normalized root mean square error (NRMSE), and 8.53% in R2 over mv-M-LSTM-CI. In addition, the proposed model outperforms “adv-” models with MPAE improvements ranging from 35.74% to 89.80% across the datasets. In terms of NRMSE, improvements range from 36.76% to 80.00%. Furthermore, Robust-mv-M-LSTM-CI achieves remarkable improvements in the R2 score, ranging from 17.35% to 119.63%. The results indicate that the proposed model enhances overall accuracy while effectively mitigating the potential reduction in accuracy often associated with adversarial training models. By incorporating adversarial noise and COVID-19 case data, the proposed model demonstrates improved accuracy and robustness in forecasting energy consumption under uncertain conditions. This enhanced predictive capability will enable energy managers and policymakers to better anticipate and respond to fluctuations in energy demand during pandemics, ensuring more resilient and efficient energy systems.

A Data-Driven Pandemic Simulator with Reinforcement Learning

After the coronavirus disease 2019 (COVID-19) outbreak erupted, it swiftly spread globally and triggered a severe public health crisis in 2019. To contain the virus’s spread, several countries implemented various lockdown measures. As the governments faced this unprecedented challenge, understanding the impact of lockdown policies became paramount. The goal of addressing the pandemic crisis is to devise prudent policies that strike a balance between safeguarding lives and maintaining economic stability. Traditional mathematical and statistical models for studying virus transmission only offer macro-level predictions of epidemic development and often overlook individual variations’ impact, therefore failing to reflect the role of government decisions. To address this challenge, we propose an integrated framework that combines agent-based modeling (ABM) and deep Q-network (DQN) techniques. This framework enables a more comprehensive analysis and optimization of epidemic control strategies while considering real human behavior. We construct a pandemic simulator based on the ABM method, accurately simulating agents’ daily activities, interactions, and the dynamic spread of the virus. Additionally, we employ a data-driven approach and adjust the model through real statistical data to enhance its effectiveness. Subsequently, we integrated ABM into a decision-making framework using reinforcement learning techniques to explore the most effective strategies. In experiments, we validated the model’s effectiveness by simulating virus transmission across different countries globally. In this model, we obtained decision outcomes when governments focused on various factors. Our research findings indicate that our model serves as a valuable tool for decision-makers, enabling them to formulate prudent and rational policies.

Predictive Analysis of COVID-19 Epidemic in Thailand: Evaluating Control Lockdown Measures using LSTM Networks

This study addresses the critical objective of evaluating the effectiveness of non-pharmaceutical lockdown measures implemented during COVID-19 outbreaks in Thailand. Assessing the outcome of these measures provides valuable insight that can inform and guide response to future outbreaks. Utilizing a closed-loop forecasting model built on Long Short-Term Memory (LSTM) networks, the research focuses on achieving precise daily forecasts of COVID-19 cases. The methodology involves optimizing hyperparameters through grid-search and incorporating training data from other countries that implemented similar measures. The LSTM, configured with an optimal number of hidden processing units, utilizes past lagged data of daily infected cases as predictors to generate multi-step-ahead predicted values, which are subsequently used as predictors in a recursive approach. As a result, the predicted cases closely align with measured data, facilitating the estimation of the effective reproduction number (Reff) to assess the performance of lockdown measures. The effectiveness of the lockdown measures is quantified at different time intervals: 51%, 41%, and 23% one day after implementation, increasing to 84%, 98%, and 34% after one week, and reaching 96%, 99%, and 73% at the endpoint of the first, second, and fourth waves of infection, respectively. Throughout these waves, the final Reff remains below 1, indicating ongoing but controllable COVID-19, demonstrating the efficacy of the implemented lockdown measures. It is noted that these results are based on specific LSTM model, as the effectiveness of lockdown measures may vary with alternative modeling approaches. Therefore, the findings should be interpreted in the context of this LSTM-framework.

Impact of the COVID-19 virus on MENA stock market

This study examines the influence of contagious infectious diseases, specifically COVID-19, on stock market behavior. With the global emergence of COVID-19, we have witnessed not only the health implications of a pandemic but also its profound social and economic repercussions, exacerbated by the implementation of lockdown measures. Utilizing panel data analysis, we assess the impact of disseminated information regarding the virus on the stock markets of the MENA region. Our findings reveal that the daily increases in both total confirmed cases and total deaths attributable to COVID-19 have a consistently significant and negative effect on stock returns across all observed markets. In conclusion, our analysis confirms that the spread of COVID-19 has had a detrimental impact on stock market returns in the MENA region. This underscores the vulnerability of financial markets to global health crises and highlights the necessity for market participants to consider health-related information as a significant factor in risk assessment and investment decision-making processes.

The Impact of Quarantine on Physical Activity, Body Weight, and Eating Behaviors During the COVID-19 Pandemic in Saudi Arabia.

Background Since February 2020, the number of coronavirus disease 2019 (COVID-19) cases in Saudi Arabia has been rising. The Saudi Arabian government implemented strict lockdown measures in March 2020 in an effort to stop the spread of the virus. Globally, changes in socioeconomic levels and health during quarantine have been reported. Additional detrimental effects of the stay-at-home mandate include less physical activity among adult populations, anxiety, stress, and emotional eating. This study aimed to assess the impact of quarantine during the COVID‑19 pandemic on physical activity practice and weight. Methodology This cross-sectional study was conducted using a newly developed validated online questionnaire survey. The data were collected during quarantine in June 2020, including each adult who lived in Saudi Arabia during the quarantine. The data were reviewed and analyzed using SPSS version 21 (IBM Corp., Armonk, NY, USA). Results A total of 4,970 eligible participants completed the study survey. About 78.1% of the study participants performed physical activity inside the house during the pandemic. Overall, 30.9% reported increased body weight during the lockdown, and the most reported cause of increased body weight was feeling bored and empty. Regarding factors associated with physical activity and weight changes, 82.2% of participants aged 25-30 years performed physical activity during the pandemic. Increased weight was significantly higher among participants aged more than 30 years. Conclusions This study shows an increased level of physical activity among the study participants during the COVID-19 pandemic, especially among younger, female, and unmarried participants. More than half of the participants reported weight change, either weight gain or weight loss. Weight gain was higher in older participants. Regarding eating habits, there was an increased tendency toward eating a healthy diet.

Psychosocial strain and coping of Finnish working mothers during the COVID-19 lockdown: a job demand-control approach.

In March 2020 many countries around the world, including Finland, implemented lockdown measures to mitigate the unprecedented impacts of the coronavirus infectious disease (COVID-19) on public health. As a result, school and daycare settings closed indefinitely and working from home became the new normal for a big part of the workforce, which came with increased homeschooling and childcare responsibility for mothers. In this article we present the findings from maternal responses to open ended questions on psychosocial well-being, and experiences of combining work, family life and homeschooling during the COVID-19 national lockdown in Finland in March-May 2020. Working mothers' responses (n = 72) were analyzed through the lens of Karasek's job demand-control model, focusing on how the mothers experienced the demands of their life during the lockdown, and how they saw their possibilities to control the situation. The findings indicated important variation in the level of experienced demand and control and associated compensatory factors during the COVID-19 lockdown across different subgroups of working mothers. The findings have implications for understanding strain and plausible supports among working mothers during the COVID-19 lockdown as well as in the face of acute adversity including the next possible public health crisis.

Strict COVID-19 lockdown and popular regime support in China

ABSTRACT In response to the COVID-19 crisis, China implemented lockdown measures that were the most stringent and enduring in the world. This study conducts the first systematic analysis of how China’s historic COVID-19 lockdown impacted popular support for the authoritarian regime. I argue that strict lockdowns have differential effects on popular regime support depending on their duration. A relatively short lockdown signals to citizens the state’s resolution and capacity to protect them from threats, thereby increasing their support. Long-lasting restrictions, however, cause excessive harm to their victims and ultimately turn the citizenry against the state. I utilize people’s varying exposure to the lockdown measures and two rounds of original surveys to provide evidence for the theory. The study contributes to our understanding of the effectiveness of authoritarian rule in managing major crises and highlights the role that fear and coercion play in maintaining authoritarian stability.

Epid data explorer: A visualization tool for exploring and comparing spatio-temporal epidemiological data.

The analysis of large sets of spatio-temporal data is a fundamental challenge in epidemiological research. As the quantity and the complexity of such kind of data increases, automatic analysis approaches, such as statistics, data mining, machine learning, etc., can be used to extract useful information. While these approaches have proven effective, they require a priori knowledge of the information being sought, and some interesting insights into the data may be missed. To bridge this gap, information visualization offers a set of techniques for not only presenting known information, but also exploring data without having a hypothesis formulated beforehand. In this paper, we introduce Epid Data Explorer (EDE), a visualization tool that enables exploration of spatio-temporal epidemiological data. EDE allows easy comparisons of indicators and trends across different geographical areas and times. It facilitates this exploration through ready-to-use pre-loaded datasets as well as user-chosen datasets. The tool also provides a secure architecture for easily importing new datasets while ensuring confidentiality. In two use cases using data associated with the COVID-19 epidemic, we demonstrate the substantial impact of implemented lockdown measures on mobility and how EDE allows assessing correlations between the spread of COVID-19 and weather conditions.

COVID-19 pandemic and the momentum of seaweed industry development in Indonesia towards sustainable food system: An overview

The COVID-19 pandemic has had an impact on the seaweed industry due to changes in supply and demand, cost increases, market volatility, and the implementation of lockdown measures. This study aimed to determine the effect of the COVID-19 pandemic on the seaweed industry to support a sustainable food system. This study used both primary data from the seaweed industry, especially the upstream sector, and secondary data to determine the progress of the national and international seaweed industries. The data was analyzed descriptively to obtain information regarding the role of the seaweed industry in accelerating post-pandemic coastal economic recovery. The results indicated that seaweed cultivation can support coastal economic recovery in the post-COVID-19 pandemic. After demand recovers, production can be enlarged in a short time without excessive investment and has the potential to generate income within 30–45 days. It was found that total seaweed exports have dropped by 30.54% and 19.90% by volume and value, respectively, in only the early stages of the COVID-19 outbreak. However, the effect of the pandemic has encouraged accelerative industrialization by increasing the capacity of domestic seaweed factories from 50% to 72%. The pandemic has also triggered the domestic industry to produce seaweed derivative products independently, such as carrageenan, alginate, and agar, to reduce dependence on imports. It is imperative to recommend to the Indonesian government that the export tariff on dried seaweed be increased to boost the competitiveness of the domestic seaweed processing industry and invest in the diversification of processed seaweed derivative products.

Transforming libraries and information professionals for the industry 4.0 in developing countries: Towards the development of a framework for accelerating change post-Covid-19

During the various stages of the COVID-19 pandemic, numerous countries, including several in Africa, implemented lockdown measures. Unfortunately, libraries and information centres were not deemed essential services providers, resulting in partial operations or complete closures. Among the hardest-hit by the pandemic were developing economies, where libraries, typically non-profit organizations, play a crucial role. These institutions usually rely on funding from the government (in the case of public libraries) or parent institutions, which themselves often depend on government support (as is the case with academic libraries in government-owned institutions). With the economies of these countries suffering and experiencing a slow recovery, libraries in developing nations are likely to face further budgetary constraints. In addition to the economic impact on funding, there is a significant risk of a decline in the literacy rate among children due to the closure of libraries and schools during lockdowns. This paper employs a SWOT analysis to identify the challenges and benefits for libraries and information professionals from both societal and professional perspectives. Despite the obstacles that libraries in the developing world may encounter in the post-pandemic era, as well as during the fourth industrial revolution, there are also valuable opportunities that can be harnessed. One such opportunity is the provision of information services to digitally literate, yet informationally illiterate communities.

Covid lockdowns and hospitalisations for oro-facial trauma among children in Australia and the United Kingdom.

This retrospective study investigated the effect of lockdowns for Covid-19 on the rates of oro-facial trauma hospitalisations in children in Australia and the United Kingdom (UK). Hospitalisation data for children up to 19 years old for oro-facial trauma injuries (according to ICD-10-AM principal diagnosis codes) over 5 years were collected for Australian and UK children. The age-standard rate (ASR) for each age group was calculated per 100,000 population. In the younger Australian children, there was no statistically significant change in the ASR of oro-facial trauma hospitalisations during the 'Covid year', compared to the average rate over the 3 years preceding. However, there was a significant decrease in the ASR of oro-facial trauma hospitalisations during the 'Covid year' in older Australian children (aged 10-19), and for all UK children, except for infants under one-year-old. Reduction of the ASR of hospitalisations due to oro-facial trauma during the 'Covid year', and the implementation of lockdown measures in the two countries provides further evidence of the impact of physical activities and travel as contributing factors to oro-facial injuries in children. In addition to its role in reducing the spread of virus infections during the pandemic, the lockdown measures also resulted in a simultaneous decrease in trauma injuries.

Attributed causes of excess mortality during the COVID-19 pandemic in a south Indian city

Globally, excess deaths during 2020–21 outnumbered documented COVID-19 deaths by 9.5 million, primarily driven by deaths in low- and middle-income countries (LMICs) with limited vital surveillance. Here we unravel the contributions of probable COVID-19 deaths from other changes in mortality related to pandemic control measures using medically-certified death registrations from Madurai, India—an urban center with well-functioning vital surveillance. Between March, 2020 and July, 2021, all-cause deaths in Madurai exceeded expected levels by 30% (95% confidence interval: 27–33%). Although driven by deaths attributed to cardiovascular or cerebrovascular conditions, diabetes, senility, and other uncategorized causes, increases in these attributions were restricted to medically-unsupervised deaths, and aligned with surges in confirmed or attributed COVID-19 mortality, likely reflecting mortality among unconfirmed COVID-19 cases. Implementation of lockdown measures was associated with a 7% (0–13%) reduction in all-cause mortality, driven by reductions in deaths attributed to injuries, infectious diseases and maternal conditions, and cirrhosis and other liver conditions, respectively, but offset by a doubling in cancer deaths. Our findings help to account for gaps between documented COVID-19 mortality and excess all-cause mortality during the pandemic in an LMIC setting.

An investigation of the effects of lockdowns and COVID-19 vaccinations in Ireland

Abstract. The COVID-19 pandemic resulted in many deaths and much upheaval worldwide. Public health responses to the pandemic differed greatly between countries. In 2023, as we emerge from the aftermath of the pandemic, it is now timely to assess the impact of specific public health response measures such as lockdowns and vaccinations. This assessment can help inform the development of evidence-based strategies for future public health responses in pandemic scenarios. We describe the implementation of a Bayesian Hierarchical Poisson Regression (BHPR) model to estimate the impact of pandemic response measures and vaccination on all-cause deaths, including COVID-19, in Ireland. We find that the implementation of lockdown measures and an appropriate vaccination timeline were effective in reducing the number of deaths in Ireland by, most likely, reducing the COVID- 19 mortality rate. We believe our approach could be used to assess the impact of pandemic response measures and vaccination in other countries as well where similar data is available.

Knowledge, attitudes, and practices regarding immunity boosting dietary behavior of mass population amid COVID-19.

During the increasing spread of COVID-19 occurrences in Chattogram Metropolitan Area (CMA) of Bangladesh, a series of measures were taken to control the transmission. These measures greatly influenced the knowledge, attitudes, and practices (KAP) of the population on their dietary behavior. However, there are no current studies demonstrating the KAP of the CMA citizens regarding their dietary habit that can boost the immunity. In this study, we appraised KAP in regard to immunity boosting dietary behavior from April 26, 2021 to November 17, 2021 during implementation of lockdown measures by the government of Bangladesh. Apart from the basic knowledge and attitudes toward immunity boosting dietary behavior, we have also aimed to assess the practices of the population by whether the nutrients, especially vitamin A, B6, B9, B12, C, D, E, and trace minerals such as zinc, selenium, and iron were included in their diet and in what frequency. This study is a cross-sectional study, and the participants were recruited using both online platforms during the lockdown and through in-person interviews after the withdrawal of lockdown. After obtaining the proper consent from the participants, their sociodemographic variables, and KAP towards immunity boosting dietary behavior were assessed. Total 400 participants were included in this study and a non-probability sampling technique named purposive sampling has been followed for participants recruitment. Among the 400 participants, the majority of them (64.3%) were male, most of them (62.7%) were students, unmarried (69.5%), aged between 18-35 years (82.5%), had a bachelor's degree (50.0%), and had a monthly family income between 10000-30000 BDT (35.5%). This study indicated that 82.8% of the populations had the correct knowledge, 71.3% had favorable attitudes, and 44% had good practices regarding immunity boosting diet during COVID-19. The majority (79.3%) of the participants had an idea about nutrition, most of them (78.5%) knew the nutrients needed to strengthen their immune system, almost all (98.5%) washed fruits and vegetables purchased from the market before eating them, 78% did not often purchase food online, and 53% often ate junk food. In a binary logistic regression, correct knowledge was significantly associated with the females, having HSC or bachelor's degree, being in the occupation of business, laborer or others, and having a monthly family income between 50000-100000 or >100000. The favorable attitudes were significantly associated with having a master's degree or above, and for government job holders. However, the good practices did not show any significant association with the sociodemographic factors in binary logistic regression. Moreover, the study found the presence of bad or unhealthy practices among the populations despite having correct knowledge and favorable attitudes. Thus, this study could identify the variables, such as gender differences, education, monthly family income, and occupation on which emphasis should be given during public health campaigns or training programs to improve the KAP regarding immunity boosting diet.

Chinese Older Adults' Motivations and Preferences for Television Watching During the Pandemic

Amidst the COVID-19 pandemic, numerous countries implemented lockdown measures, which significantly impacted individuals' mental well-being, particularly older adults. As a pervasive medium, television constitutes a vital resource upon which this demographic relies, and is posited to bolster their psychological health. Given the rapid growth of the aging population in China and the ongoing implementation of the Dynamic Covid-zero strategy, it becomes imperative to examine the preferences and motivations underlying television consumption amongst older adults during this era, an aspect often neglected in extant literature. In the present investigation, semi-structured interviews employing qualitative methodologies were conducted with a sample of 19 older adults, followed by tertiary coding utilizing NVivo software. The findings reveal a substantial increase in television viewership among older Chinese adults during the COVID-19 pandemic. Key motivations underpinning their television consumption include recreation, information acquisition, social engagement, and educational purposes. Furthermore, their programmatic preferences encompass news, dramatic productions, variety programs, and sporting events.

Predictors of Cyberchondria During the COVID-19 Pandemic: Cross-sectional Study Using Supervised Machine Learning.

Cyberchondria is characterized by repeated and compulsive online searches for health information, resulting in increased health anxiety and distress. It has been conceptualized as a multidimensional construct fueled by both anxiety and compulsivity-related factors and described as a "transdiagnostic compulsive behavioral syndrome," which is associated with health anxiety, problematic internet use, and obsessive-compulsive symptoms. Cyberchondria is not included in the International Classification of Diseases 11th Revision or the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and its defining features, etiological mechanisms, and assessment continue to be debated. This study aims to investigate changes in the severity of cyberchondria during the COVID-19 pandemic and identify the predictors of cyberchondria at this time. Data collection started on May 4, 2020, and ended on June 10, 2020, which corresponds to the first wave of the COVID-19 pandemic in Europe. At the time the study took place, French-speaking countries in Europe (France, Switzerland, Belgium, and Luxembourg) all implemented lockdown or semilockdown measures. The survey consisted of a questionnaire collecting demographic information (sex, age, education level, and country of residence) and information about socioeconomic circumstances during the first lockdown (eg, economic situation, housing, and employment status) and was followed by several instruments assessing various psychological and health-related constructs. Inclusion criteria for the study were being at least 18 years of age and having a good understanding of French. Self-report data were collected from 725 participants aged 18-77 (mean 33.29, SD 12.88) years, with females constituting the majority (416/725, 57.4%). The results showed that the COVID-19 pandemic affected various facets of cyberchondria: cyberchondria-related distress and compulsion increased (distress z=-3.651, P<.001; compulsion z=-5.697, P<.001), whereas the reassurance facet of cyberchondria decreased (z=-6.680, P<.001). In addition, COVID-19-related fears and health anxiety emerged as the strongest predictors of cyberchondria-related distress and interference with functioning during the pandemic. These findings provide evidence of the impact of the COVID-19 pandemic on cyberchondria and identify factors that should be considered in efforts to prevent and manage cyberchondria at times of public health crises. In addition, they are consistent with a theoretical model of cyberchondria during the COVID-19 pandemic proposed in 2020. These findings have implications for the conceptualization and future assessment of cyberchondria.

“Help Us!”: a content analysis of COVID-19 help-seeking posts on Weibo during the first lockdown

BackgroundSocial media is playing an increasingly important role in public emergencies for help-seekers, especially during the global COVID-19 pandemic. Wuhan, China, firstly official reported COVID-19 cases and implemented lockdown measures to prevent the spread of the virus. People during the first lockdown were restricted from seeking help face-to-face. Social media is more prominent as an online tool for people seeking help, especially for patients, than in other stages of the COVID-19 pandemic.ObjectiveThis study aimed to explore the urgent needs presented in help-seeking posts in Wuhan during the first COVID-19 lockdown, the content features of these posts, and how they influenced online user engagement.MethodsThis study collected posts from Weibo posted with specific help tags during the first COVID-19 lockdown in Wuhan: from 23 January 2020 to 24 March 2020, and eventually received 2055 data, including textual content, comments, retweets, and publishing location. Content analysis was conducted, and manual coding was performed on help-seeking typology, narrative mode, narrative subject, and emotional valence.ResultsThe result showed that help-seeking posts primarily were seeking medical (97.7%). Features of these posts were mainly adopting a mixed narrative mode (46.4%), released by relatives of patients (61.7%), and expressing negative emotions (93.2%). Chi-square tests suggested that help-seeking posts with mixed narrative modes released by relatives express more frequent negative emotions. Results of negative binomial regression indicated posts of seeking information (B = 0.52, p < .001, e0.52 = 1.68), with mixed narrative mode (B = 0.63, p < .001, e0.63 = 1.86), released by themselves (as referential groups) and with neutral emotions increased comments. Posts of seeking medical (B = 0.57, p < .01, e0.57 = 1.77), with mixed narrative mode (B = 1.88, p < .001, e1.88 = 6.53), released by people of unrelated patients (B = 0.47, p < .001, e0.47 = 1.60) and with neutral emotions increased retweets.ConclusionsThis study provides evidence of what actual public demands are to be considered and addressed by governments and public administrators before implementing closure and lockdown policies to limit the spread of the virus. Meanwhile, our findings offer strategies for people help-seeking on social media in similar public health emergencies.

Lockdown, information quality, and political trust: An empirical study of the Shanghai lockdown under COVID-19

Many countries have adopted various measures to curb the spread of the COVID-19 pandemic. The regulation measures of lockdown have triggered changes in public political trust in the government (including in its competence, benevolence and integrity). Information shapes the attitudes and values of residents; this paper aims to study the effect of a lockdown on political trust and the moderating effect from quality of government shared information. From 12 March to 31 May 2022, Shanghai implemented strict lockdown measures. In this study, we randomly sampled the participation information of 1063 participants. Data-based regression analysis shows that lockdown has had a negative impact on all subcategories of political trust. However, timely and accuracy information weakens its negative effect. The accuracy of information moderates the relationship between lockdown and political trust in competence and integrity, while a sufficient supply of information moderates the relationship between lockdown and political trust in benevolence. Points for practitioners This study explores how different dimensions of information quality affect each component public’s political trust, and thereby inspires practitioners to strategically improve information communication in crisis.

Factors Associated with Increased or Decreased Stress Level in French Children during the First COVID-19 Lockdown

In spring 2020, governments of many countries implemented lockdown measures to prevent the spread of the COVID-19 pandemic. Worldwide, the pandemic forced about 1.5 billion children to stay at home for several weeks and to experience homeschooling. The objective of this study was to assess the variation in stress levels and associated factors in school-aged children in France during the first COVID-19 lockdown. A cross-sectional study using an online questionnaire was designed by an interdisciplinary team involving hospital child psychiatrists and school doctors. Between 15 June and 15 July 2020, Educational Academy of Lyon (France) invited the parents of school-aged children to participate in this survey. The first part of the questionnaire concerned the children with data on lockdown conditions, socio-demographic data, daily rhythms (eating and sleeping), perceived stress variations, and feelings. The second part assessed parental perspectives on their child's psychological state and use of the mental health care system. Multivariate logistic regression was performed to identify factors associated with stress variation (increased or decreased). A total of 7218 questionnaires were fully completed by children from elementary school to high school with a balanced sex ratio. In summary, 29% of children reported a higher stress level during the lockdown, 34% reported a lower stress level, and 37% reported no stress variation in the usual situation prior to COVID-19. Parents were most often able to identify signs of increased stress levels in their children. The most influential factors in the variation of stress for children were academic pressure, family relationships, and fear of being infected or infecting a family member with SARS-CoV-2. Our study underlines the high impact of school attendance stressors on children in usual conditions and encourages vigilance for children whose stress levels have decreased during the lockdown but who may have increased difficulty re-exposing themselves upon deconfinement.