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Abstract Background After COVID-19 was declared a Public Health Emergency of International Concern by WHO, several non-pharmaceutical interventions were adopted for containing the virus. Success to which largely depend upon citizens’ compliance to these measures. There is growing body of evidence linking social support with health promoting behaviour. Hence, this research aimed to study the effects on compliance with stay-at-home order in relation to their perceived social support. Methods A web-based cross-sectional study was conducted among adult participants aged 18 years and above residing in Bagmati Province, Nepal. A convenient non-probability sampling method was adopted to select the required number of samples. The questionnaire was developed through an extensive review of literature, and consultations with the research advisor, subject experts, as well as peers and converted to online survey form using Google Forms. Perceived social support was measured using the Multidimensional Scale of Perceived Social Support (MSPSS) scale whereas compliance was assessed using a single screening question. Statistical analysis was performed using SPSS version 20 involving both the descriptive and inferential statistics. Results Two fifth (40.2%) of the participants reported poor compliance with stay-at-home order which was found higher among participants who were not vaccinated against COVID-19 compared to those vaccinated (p value

Introduction: Although surveys are a well-established instrument to capture the population prevalence of mental health at a moment in time, public Twitter is a continuously available data source that can provide a broader window into population mental health. We characterized the relationship between COVID-19 case counts, stay-at-home orders because of COVID-19, and anxiety and depression in 7 major U.S. cities utilizing Twitter data. Methods: We collected 18 million Tweets from January to September 2019 (baseline) and 2020 from 7 U.S. cities with large populations and varied COVID-19 response protocols: Atlanta, Chicago, Houston, Los Angeles, Miami, New York, and Phoenix. We applied machine learning‒based language prediction models for depression and anxiety validated in previous work with Twitter data. As an alternative public big data source, we explored Google Trends data using search query frequencies. A qualitative evaluation of trends is presented. Results: Twitter depression and anxiety scores were consistently elevated above their 2019 baselines across all the 7 locations. Twitter depression scores increased during the early phase of the pandemic, with a peak in early summer and a subsequent decline in late summer. The pattern of depression trends was aligned with national COVID-19 case trends rather than with trends in individual states. Anxiety was consistently and steadily elevated throughout the pandemic. Google search trends data showed noisy and inconsistent results. Conclusions: Our study shows the feasibility of using Twitter to capture trends of depression and anxiety during the COVID-19 public health crisis and suggests that social media data can supplement survey data to monitor long-term mental health trends.

Countries around the world have resorted to issuing stay-at-home orders to slow viral transmission since the COVID-19 pandemic. During the lockdown, access to public park plays a central role in the public health of surrounding communities. However, we know little about how such an unprecedented policy may exacerbate the preexisting unequal access to green space (i.e., green space justice). To address this research void, we used difference-in-difference models to examine socioeconomic disparities, urban-rural disparities, and mobility disparities in terms of public park access in the United States. Our national analysis using the weekly mobile phone movement data robustly suggests the following three key findings during COVID-19: (1) The elderly, non-college-educated people, poor people, and blacks are less likely to visit public parks frequently, while unemployed people appear to be the opposite. (2) Compared to rural areas, populations in urban neighborhoods appear to visit public parks more frequently and they generally go to larger parks to minimize the risk of infection. (3) Populations in neighborhoods with higher private vehicle ownership or those with a higher density of transit stops would more frequently visit and travel a longer distance to public parks during the stay-at-home order. Our results imply that conventional inequality in green space access may still exist and even become worse during COVID-19, which could negatively impact people's health during isolation. We suggest that special attention should be paid to park-poor neighborhoods during the pandemic and in the post-pandemic recovery phase.

Two of the most important categories of government intervention in response to COVID-19 are business closures and mask mandates. The scientific literature supports the efficacy of mask-wearing to reduce the transmission of respiratory viruses (including COVID-19). However, the efficacy is greater in stopping outbound transmission (meaning that my mask protects you) than inbound transmission (meaning that my mask protects me). Evidence suggests that the full benefits to society of wearing masks are far greater than the full costs to society of wearing masks. The author argues that mask-wearing is far more cost effective than business closures in controlling the spread of COVID-19. Moreover, the author argues that highly infectious diseases have an externality dimension. The person infected with COVID-19 makes a decision regarding whether to wear a mask based on their own perceived costs and benefits of mask-wearing, but that decision has consequences for those they come in contact with: the infected person’s decision not to wear a mask imposes costs on others that are external to the infected person’s decision process not to do so. The author further argues that some possible methods by which to deal with such an external cost (individual negotiations, a tax on spreading COVID-19, or as subsidy for wearing masks) are impractical. This makes a mask-wearing government mandate economically valid.

Abstract Background The surge of COVID-19 infections has prompted many countries to take unprecedented policy measures. In Belgium, the authorities implemented a nation-wide stay-at-home order for several months. Evidence of the mental health effect of such measures is scarce. A recent review by Brooks et al. has compiled a defined list of stressors affecting people’s mental health under quarantine during previous epidemic settings. This study aims to test the association between these stressors and the mental health of students attending higher education during the stay-at-home order in Belgium. Methods In this cross-sectional study, 18,301 students from 13 higher education institutions (HEI) participated in an online survey between 26 April and 11 May 2020. We assessed the association between potential stressors and depressive symptoms severity scores and structural equation modeling was used to assess how stressors may mediate the association between duration of exposure and depressive symptoms severity. Results The stressors proposed by Brooks et al. were found to be associated with depressive symptoms severity. The stressors ‘perceived academic stress’, ‘institutional dissatisfaction’ and ‘fear of being infected’ were associated with substantially higher depressive symptoms severity scores. The association between duration of exposure and depressive symptoms severity was mediated by ‘academic stress’. Being in a steady relationship and living together with others were both associated with a lower depressive symptoms severity. Conclusion Findings underline the need for a student-centered approach and mental health prevention. Authorities and HEIs should consider whether and if so, how a stay-at-home order should be implemented.

Objective: Our study aims to evaluate the impact of a stay-at-home order on stroke metrics during the 2019-novel coronavirus (COVID-19) pandemic. Methods: Data on baseline characteristics, stroke subtype, initial National Institutes of Health Stroke Scale (NIHSS) score, the time between last known well (LKW) to emergency department (ED) arrival, tissue plasminogen activator (tPA) administration, the involvement of large vessel occlusion (LVO), and whether mechanical thrombectomy (MT) was pursued in patients with acute stroke were extracted from 24 March to 23 April 2020 (the time period of a stay-at-home order was placed due to the COVID-19 pandemic as the study group) at a tertiary care hospital in West Michigan, USA, compared with data from 24 March to 23 April 2019 (control group). Results: Our study demonstrated a reduction in cases of acute ischemic stroke (AIS), although this did not reach statistical significance. However, there was an increase in hemorrhagic stroke (7.5% controls vs. 19.2% study group). The age of stroke patients was significantly younger during the period of the stay-at-home order compared to the control group. We identified a significant overall delay of ED arrivals from LKW in the study group. Additionally, an increased number of AIS patients with LVO in the study group (34.8%) was found compared to the control group (17.5%). A significantly increased number of patients received MT in the study group. Additionally, 11 patients were COVID-19 PCR-positive in the study group, 10 with AIS and only 1 with hemorrhagic stroke. Patients with COVID-19 had a high incidence of atrial fibrillation and hyperlipidemia. One AIS patient with COVID-19 rapidly developed cytotoxic edema with corresponding elevated inflammatory biomarkers. No statistical significance was noted when stroke subtype, LVO, and MT groups were compared. Conclusions: There was a trend of decreasing AIS admissions during the COVID-19 pandemic. There was also a significantly increased number of AIS patients with LVO who received MT, especially those with COVID-19. We conclude that cytokine storm resulting from SARS-CoV-2 infection might play a role in AIS patients with COVID-19.

During COVID-19 lockdowns, transit agencies need to respond to the decline in travel but also maintain the essential mobility of transit-dependent people. However, there are a few lessons that scholars and practitioners can learn from. Using highway traffic data in the Twin Cities, this study applies a generalized additive model to explore the relationships among the share of low-income population, transit service, and highway traffic during the week that occurred right after the 2020 stay-at-home order. Our results substantiate that transportation impacts are spread unevenly across different income groups and low-income people are less able to reduce travel, leading to equity concerns. Moreover, transit supply influences highway traffic differently in areas with different shares of low-income people. Our study suggests that transportation agencies should provide more affordable travel options for areas with concentrated poverty during lockdowns. In addition, transit agencies should manage transit supply strategically depending on the share of low-income people to better meet people’s mobility needs.

The purpose of this paper was to compare the relative efficiency of COVID-19 transmission mitigation among 23 selected countries, including 19 countries in the G20, two heavily infected countries (Iran and Spain), and two highly populous countries (Pakistan and Nigeria). The mitigation efficiency for each country was evaluated at each stage by using data envelopment analysis (DEA) tools and changes in mitigation efficiency were analyzed across stages. Pearson correlation tests were conducted between each change to examine the impact of efficiency ranks in the previous stage on subsequent stages. An indicator was developed to judge epidemic stability and was applied to practical cases involving lifting travel restrictions and restarting the economy in some countries. The results showed that Korea and Australia performed with the highest efficiency in preventing the diffusion of COVID-19 for the whole period covering 105 days since the first confirmed case, while the USA ranked at the bottom. China, Japan, Korea, and Australia were judged to have recovered from the attack of COVID-19 due to higher epidemic stability.