Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 2 de 2
Filter
Add filters

Main subject
Language
Document Type
Year range
1.
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.04.14.22273759

ABSTRACT

The COVID-19 pandemic has impacted communities far and wide and put tremendous pressure on healthcare systems of countries across the globe. Understanding and monitoring the major influences on COVID-19 prevalence is essential to inform policy making and device appropriate packages of non-pharmaceutical interventions (NPIs). This study evaluates community level influences on COVID-19 incidence in England and their variations over time with specific focus on understanding the impact of working in so called high-risk industries such as care homes and warehouses. Analysis at community level allows accounting for interrelations between socioeconomic and demographic profile, land use, and mobility patterns including residents' self-selection and spatial sorting (where residents choose their residential locations based on their travel attitudes and preferences or social structure and inequality); this also helps understand the impact of policy interventions on distinct communities and areas given potential variations in their mobility, vaccination rates, behavioural responses, and health inequalities. Moreover, community level analysis can feed into more detailed epidemiological and individual models through tailoring and directing policy questions for further investigation. We have assembled a large set of static (socioeconomic and demographic profile and land use characteristics) and dynamic (mobility indicators, COVID-19 cases and COVID-19 vaccination uptake in real time) data for small area statistical geographies (Lower Layer Super Output Areas, LSOA) in England making the dataset, arguably, the most comprehensive set assembled in the UK for community level analysis of COVID-19 infection. The data are integrated from a wider range of sources including telecommunications companies, test and trace data, national travel survey, Census and Mid-Year estimates. To tackle methodological challenges specifically accounting for highly interrelated influences, we have augmented different statistical and machine learning techniques. We have adopted a two-stage modelling framework: a) Latent Cluster Analysis (LCA) to classify the country into distinct land use and travel patterns, and b) multivariate linear regression to evaluate influences at each distinct travel cluster. We have also segmented our data into different time periods based on changes in policies and evolvement in the course of pandemic (such as the emergence of a new variant of the virus). By segmenting and comparing influences across spaces and time, we examine more homogeneous behaviour and uniform distribution of infection risks which in turn increase the potential to make causal inferences and help understand variations across communities and over time. Our findings suggest that there exist significant spatial variations in risk influences with some being more consistent and persistent over time. Specifically, the analysis of industrial sectors shows that communities of workers in care homes and warehouses and to a lesser extent textile and ready meal industries tend to carry a higher risk of infection across all spatial clusters and over the whole period we modelled in this study. This demonstrates the key role that workplace risk has to play in COVID-19 risk of outbreak after accounting for the characteristics of workers' residential area (including socioeconomic and demographic profile and land use features), vaccination rate, and mobility patterns.


Subject(s)
COVID-19
2.
arxiv; 2021.
Preprint in English | PREPRINT-ARXIV | ID: ppzbmed-2104.04605v3

ABSTRACT

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 (ONS) COVID-19 Infection Survey (CIS) 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 (SITPs) 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 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.


Subject(s)
COVID-19
SELECTION OF CITATIONS
SEARCH DETAIL