Understanding community level influences on the prevalence of SARS-CoV-2 infection in England: new insights from comparison over time and space.

Understanding and monitoring the major influences on SARS-CoV-2 prevalence is essential to inform policy making and devise appropriate packages of non-pharmaceutical interventions. Through evaluating community level influences on the prevalence of SARS-CoV-2 infection and their spatio-temporal variations in England, this study aims to provide some insights into the most important risk parameters. We used spatial clusters developed in Jahanshahi and Jin (2021 Transportation 48, 1329-1359 (doi:10.1007/s11116-020-10098-9)) as geographical areas with distinct land use and travel patterns. We also segmented our data by time periods to control for changes in policies or development of the disease over the course of the pandemic. We then used multivariate linear regression to identify influences driving infections within the clusters and to compare the variations of those between the clusters. Our findings demonstrate the key roles that workplace and commuting modes have had on some of the sections of the working population after accounting for several interrelated influences including mobility and vaccination. We found communities of workers in care homes and warehouses and to a lesser extent textile and ready meal industries and those who rely more on public transport for commuting tend to carry a higher risk of infection across all residential area types and time periods.

Inferring risks of coronavirus transmission from community household data.

The response of many governments to the COVID-19 pandemic has involved measures to control within- and between-household transmission, providing motivation to improve understanding of the absolute and relative risks in these contexts. Here, we perform exploratory, residual-based, and transmission-dynamic household analysis of the Office for National Statistics COVID-19 Infection Survey data from 26 April 2020 to 15 July 2021 in England. This provides evidence for: (i) temporally varying rates of introduction of infection into households broadly following the trajectory of the overall epidemic and vaccination programme; (ii) susceptible-Infectious transmission probabilities of within-household transmission in the 15-35% range; (iii) the emergence of the Alpha and Delta variants, with the former being around 50% more infectious than wildtype and 35% less infectious than Delta within households; (iv) significantly (in the range of 25-300%) more risk of bringing infection into the household for workers in patient-facing roles pre-vaccine; (v) increased risk for secondary school-age children of bringing the infection into the household when schools are open; (vi) increased risk for primary school-age children of bringing the infection into the household when schools were open since the emergence of new variants.

Individual, Workplace, and Combined Effects Modeling of Employee Productivity Loss.

OBJECTIVE: Systematically and simultaneously investigate a wide range of influences on workplace productivity loss. METHODS: Data were collected from 31,950 employees in the UK. Influences of employees' socioeconomic characteristics, lifestyle, commuting, physical and mental health, well-being, and job and workplace environment were assessed using structural equation models, allowing systematic decomposition of the complex network of influences and creating new, deeper insights. RESULTS: Mental health, physical health, job characteristics, and support from organization are the most important (direct or indirect) determinants of employees' productivity. 93% of the indirect influences are mediated through mental and/or physical health. Some influences that appear as strong predictors in simple models lose most of their explanatory power in more complex models with additional explanatory variables. CONCLUSIONS: There is a need for a more tailored strategy to improve employees' wellbeing as well as the overall organizational, work, and management culture.