Social dominance orientation, right-wing authoritarianism, and political attitudes toward governmental performance during the COVID-19 pandemic

This research examined the association of social dominance orientation (SDO) and right-wing authoritarianism (RWA) with the evaluations of the government's anti-coronavirus disease 2019 (COVID-19) policies and performance. In Study 1 (N = 261), we found that SDO and RWA were positively associated with resistance to criticism about the government's anti-COVID-19 measures. In addition, SDO was positively associated with favorable evaluations of the government's performance in handling the crisis. Support for lockdown policies mediated these attitudes. In Study 2 (N = 438), the results show that SDO and RWA had indirect associations with beliefs in the superiority of China's political system through three mediation variables. Evaluations of the US government's performance in handling the COVID-19 pandemic were negatively associated with beliefs in the superiority of China's political system, and there was a negative relationship between evaluations of the Chinese and US governments' performances. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Tracking Changes in Mobility Before and After the First SARS-CoV-2 Vaccination Using Global Positioning System Data in England and Wales (Virus Watch): Prospective Observational Community Cohort Study.

BACKGROUND: Evidence suggests that individuals may change adherence to public health policies aimed at reducing the contact, transmission, and spread of the SARS-CoV-2 virus after they receive their first SARS-CoV-2 vaccination when they are not fully vaccinated. OBJECTIVE: We aimed to estimate changes in median daily travel distance of our cohort from their registered addresses before and after receiving a SARS-CoV-2 vaccine. METHODS: Participants were recruited into Virus Watch starting in June 2020. Weekly surveys were sent out to participants, and vaccination status was collected from January 2021 onward. Between September 2020 and February 2021, we invited 13,120 adult Virus Watch participants to contribute toward our tracker subcohort, which uses the GPS via a smartphone app to collect data on movement. We used segmented linear regression to estimate the median daily travel distance before and after the first self-reported SARS-CoV-2 vaccine dose. RESULTS: We analyzed the daily travel distance of 249 vaccinated adults. From 157 days prior to vaccination until the day before vaccination, the median daily travel distance was 9.05 (IQR 8.06-10.09) km. From the day of vaccination to 105 days after vaccination, the median daily travel distance was 10.08 (IQR 8.60-12.42) km. From 157 days prior to vaccination until the vaccination date, there was a daily median decrease in mobility of 40.09 m (95% CI -50.08 to -31.10; P<.001). After vaccination, there was a median daily increase in movement of 60.60 m (95% CI 20.90-100; P<.001). Restricting the analysis to the third national lockdown (January 4, 2021, to April 5, 2021), we found a median daily movement increase of 18.30 m (95% CI -19.20 to 55.80; P=.57) in the 30 days prior to vaccination and a median daily movement increase of 9.36 m (95% CI 38.6-149.00; P=.69) in the 30 days after vaccination. CONCLUSIONS: Our study demonstrates the feasibility of collecting high-volume geolocation data as part of research projects and the utility of these data for understanding public health issues. Our various analyses produced results that ranged from no change in movement after vaccination (during the third national lock down) to an increase in movement after vaccination (considering all periods, up to 105 days after vaccination), suggesting that, among Virus Watch participants, any changes in movement distances after vaccination are small. Our findings may be attributable to public health measures in place at the time such as movement restrictions and home working that applied to the Virus Watch cohort participants during the study period.

Human mobility variations in response to restriction policies during the COVID-19 pandemic: An analysis from the Virus Watch community cohort in England, UK

Objective Since the outbreak of COVID-19, public health and social measures to contain its transmission (e.g., social distancing and lockdowns) have dramatically changed people's lives in rural and urban areas globally. To facilitate future management of the pandemic, it is important to understand how different socio-demographic groups adhere to such demands. This study aims to evaluate the influences of restriction policies on human mobility variations associated with socio-demographic groups in England, UK. Methods Using mobile phone global positioning system (GPS) trajectory data, we measured variations in human mobility across socio-demographic groups during different restriction periods from Oct 14, 2020 to Sep 15, 2021. The six restriction periods which varied in degree of mobility restriction policies, denoted as “Three-tier Restriction,” “Second National Lockdown,” “Four-tier Restriction,” “Third National Lockdown,” “Steps out of Lockdown,” and “Post-restriction,” respectively. Individual human mobility was measured with respect to the time period people stayed at home, visited places outside the home, and traveled long distances. We compared these indicators across the six restriction periods and across socio-demographic groups. Results All human mobility indicators significantly differed across the six restriction periods, and the influences of restriction policies on individual mobility behaviors are correlated with socio-demographic groups. In particular, influences relating to mobility behaviors are stronger in younger and low-income groups in the second and third national lockdowns. Conclusions This study enhances our understanding of the influences of COVID-19 pandemic restriction policies on human mobility behaviors within different social groups in England. The findings can be usefully extended to support policy-making by investigating human mobility and differences in policy effects across not only age and income groups, but also across geographical regions.

Uncertainty-Aware Interpretable Deep Learning for Slum Mapping and Monitoring

Over a billion people live in slums, with poor sanitation, education, property rights and working conditions having a direct impact on current residents and future generations. Slum mapping is one of the key problems concerning slums. Policymakers need to delineate slum settlements to make informed decisions about infrastructure development and allocation of aid. A wide variety of machine learning and deep learning methods have been applied to multispectral satellite images to map slums with outstanding performance. Since the physical and visual manifestation of slums significantly varies with geographical region and comprehensive slum maps are rare, it is important to quantify the uncertainty of predictions for reliable and confident application of models to downstream tasks. In this study, we train a U-Net model with Monte Carlo Dropout (MCD) on 13-band Sentinel-2 images, allowing us to calculate pixelwise uncertainty in the predictions. The obtained outcomes show that the proposed model outperforms the previous state-of-the-art model, having both higher AUPRC and lower uncertainty when tested on unseen geographical regions of Mumbai using the regional testing framework introduced in this study. We also use SHapley Additive exPlanations (SHAP) values to investigate how the different features contribute to our model’s predictions which indicate a certain shortwave infrared image band is a powerful feature for determining the locations of slums within images. With our results, we demonstrate the usefulness of including an uncertainty quantification approach in detecting slum area changes over time.

Measures to prevent nosocomial transmissions of COVID-19 based on interpersonal contact data.

BACKGROUND: With the global spreading of Coronavirus disease (COVID-19), many primary care medical workers have been infected, particularly in the early stages of this pandemic. Although extensive studies have explored the COVID-19 transmission patterns and (non-) pharmaceutical intervention to protect the general public, limited research has analysed the measures to prevent nosocomial transmission based upon detailed interpersonal contacts between medical staff and patients. AIM: This paper aims to develop and evaluate proactive prevention measures to contain the nosocomial transmission of COVID-19. The specific objectives are (1) to understand the virus transmission via interpersonal contacts among medical staff and patients; (2) to define proactive measures to reduce the risk of infection of medical staff and (3) evaluate the effectiveness of these measures to control the COVID-19 epidemic in hospitals. METHODS: We observed the operation of a typical primary hospital in China to understand the interpersonal contacts among medical staff and patients. We defined effective distance as the indicator for risk of transmission. Then three proactive measures were proposed based upon the observations, including a medical staff rotation system, the establishment of a separate fever clinic and medical staff working alone. Finally, the impacts of these measures are evaluated with a modified Susceptible-Exposure-Infected-Removed model accommodating the situation of hospitals and asymptomatic and latent infection of COVID-19. The case study was conducted with the hospital observed in December 2019 and February 2020. FINDINGS: The implementation of the medical staff rotation system has the most significant impact on containing the epidemic. The establishment of a separate fever clinic and medical staff working alone also benefits from inhibiting the epidemic outbreak. The simulation finds that if effective prevention and control measures are not taken in time, it will lead to a surge of infection cases in all asymptomatic probabilities and incubation periods.

Risk factors, symptom reporting, healthcare-seeking behaviour and adherence to public health guidance: protocol for Virus Watch, a prospective community cohort study.

INTRODUCTION: The coronavirus (COVID-19) pandemic has caused significant global mortality and impacted lives around the world. Virus Watch aims to provide evidence on which public health approaches are most likely to be effective in reducing transmission and impact of the virus, and will investigate community incidence, symptom profiles and transmission of COVID-19 in relation to population movement and behaviours. METHODS AND ANALYSIS: Virus Watch is a household community cohort study of acute respiratory infections in England and Wales and will run from June 2020 to August 2021. The study aims to recruit 50 000 people, including 12 500 from minority ethnic backgrounds, for an online survey cohort and monthly antibody testing using home fingerprick test kits. Nested within this larger study will be a subcohort of 10 000 individuals, including 3000 people from minority ethnic backgrounds. This cohort of 10 000 people will have full blood serology taken between October 2020 and January 2021 and repeat serology between May 2021 and August 2021. Participants will also post self-administered nasal swabs for PCR assays of SARS-CoV-2 and will follow one of three different PCR testing schedules based on symptoms. ETHICS AND DISSEMINATION: This study has been approved by the Hampstead National Health Service (NHS) Health Research Authority Ethics Committee (ethics approval number 20/HRA/2320). We are monitoring participant queries and using these to refine methodology where necessary, and are providing summaries and policy briefings of our preliminary findings to inform public health action by working through our partnerships with our study advisory group, Public Health England, NHS and government scientific advisory panels.

Revealing spatiotemporal transmission patterns and stages of COVID-19 in China using individual patients' trajectory data.

Gauging viral transmission through human mobility in order to contain the COVID-19 pandemic has been a hot topic in academic studies and evidence-based policy-making. Although it is widely accepted that there is a strong positive correlation between the transmission of the coronavirus and the mobility of the general public, there are limitations to existing studies on this topic. For example, using digital proxies of mobile devices/apps may only partially reflect the movement of individuals; using the mobility of the general public and not COVID-19 patients in particular, or only using places where patients were diagnosed to study the spread of the virus may not be accurate; existing studies have focused on either the regional or national spread of COVID-19, and not the spread at the city level; and there are no systematic approaches for understanding the stages of transmission to facilitate the policy-making to contain the spread. To address these issues, we have developed a new methodological framework for COVID-19 transmission analysis based upon individual patients' trajectory data. By using innovative space-time analytics, this framework reveals the spatiotemporal patterns of patients' mobility and the transmission stages of COVID-19 from Wuhan to the rest of China at finer spatial and temporal scales. It can improve our understanding of the interaction of mobility and transmission, identifying the risk of spreading in small and medium-sized cities that have been neglected in existing studies. This demonstrates the effectiveness of the proposed framework and its policy implications to contain the COVID-19 pandemic.