Interaction and Movement Analysis

The interaction study of nosocomial transmission of COVID-19

Summary

The interaction study focuses on how the movements, activities, and interactions of healthcare workers and patients influence patterns of nosocomial transmission of COVID-19. By analysing and predicting these patterns of activity, we will be able advise on policies and practices that might reduce transmission, and identify ways of changing hospital environments to reduce opportunities for infection. We aim to achieve this through two interconnected parts of the study:

Movement Data Analysis: In this study, we combine fine-grained movement data collected on individual healthcare workers with testing data, to establish points of interaction between staff and patients, and potential transmission pathways. The movement datasets we use are derived from existing data sources (e.g., card access doors, healthcare records) and new forms of mobility data (e.g., Bluetooth, Wi-Fi polling). We aim to derive a comprehensive picture of interactions and transmissions within the hospital settings in which we work.

Transmission Modelling: Building on our empirical studies, our modelling work looks at simulating patterns of healthcare worker movement and interaction, and the conditions under which different transmission patterns emerge. Using an agent-based modelling environment, we will test different assumptions and policies for their effect on transmission rates, with a view to ‘road testing’ interventions before implementation. These models will build on theoretical models of human behaviour and decision making, and on the empirical findings from our analysis research.

 

Interaction and Movement Team

 

Nick Gotts

Nick Gotts

From July 2020, he has been employed in the School of Geography, University of Leeds, working on models of nosocomial transmission of Covid-19 in the SAFER project.

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Jared Wilson-Aggarwal

Jared Wilson-Aggarwal

A research fellow at the University of Leeds investigating the spatial and social risk factors associated with the nosocomial transmission of SARS-COV-2 in healthcare workers.

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Ed Manley

Ed Manley

Ed Manley is Professor of Urban Analytics in the School of Geography, University of Leeds, and Turing Fellow at the Alan Turing Institute for Data Science and Artificial Intelligence

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