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A systematic review and guide for using multi-response statistical models in co-infection research

Francisca Powell-Romero Orcid Logo, Konstans Wells Orcid Logo, Nicholas J. Clark Orcid Logo

Royal Society Open Science, Volume: 11, Issue: 10

Swansea University Author: Konstans Wells Orcid Logo

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DOI (Published version): 10.1098/rsos.231589

Abstract

The simultaneous infection of organisms with two or more co-occurring pathogens, otherwise known as co-infections, concomitant infections or multiple infections, plays a significant role in the dynamics and consequences of infectious diseases in both humans and animals. To understand co-infections,...

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Published in: Royal Society Open Science
ISSN: 2054-5703
Published: The Royal Society 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa67913
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first_indexed 2024-10-05T12:09:36Z
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spelling v2 67913 2024-10-05 A systematic review and guide for using multi-response statistical models in co-infection research d18166c31e89833c55ef0f2cbb551243 0000-0003-0377-2463 Konstans Wells Konstans Wells true false 2024-10-05 BGPS The simultaneous infection of organisms with two or more co-occurring pathogens, otherwise known as co-infections, concomitant infections or multiple infections, plays a significant role in the dynamics and consequences of infectious diseases in both humans and animals. To understand co-infections, ecologists and epidemiologists rely on models capable of accommodating multiple response variables. However, given the diversity of available approaches, choosing a model that is suitable for drawing meaningful conclusions from observational data is not a straightforward task. To provide clearer guidance for statistical model use in co-infection research, we conducted a systematic review to (i) understand the breadth of study goals and host–pathogen systems being pursued with multi-response models and (ii) determine the degree of crossover of knowledge among disciplines. In total, we identified 69 peer-reviewed primary studies that jointly measured infection patterns with two or more pathogens of humans or animals in natural environments. We found stark divisions in research objectives and methods among different disciplines, suggesting that cross-disciplinary insights into co-infection patterns and processes for different human and animal contexts are currently limited. Citation network analysis also revealed limited knowledge exchange between ecology and epidemiology. These findings collectively highlight the need for greater interdisciplinary collaboration for improving disease management. Journal Article Royal Society Open Science 11 10 The Royal Society 2054-5703 co-infection, ecology, epidemiology, multi-response, multivariate, statistical modelling 4 10 2024 2024-10-04 10.1098/rsos.231589 COLLEGE NANME Biosciences Geography and Physics School COLLEGE CODE BGPS Swansea University Another institution paid the OA fee This project was supported by an ARC Discovery Early Career Researcher Award (DE210101439) and TheRoyal Society (RGS\R2\222152). 2024-11-05T13:12:57.8817355 2024-10-05T09:08:24.1376844 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Francisca Powell-Romero 0000-0001-9800-3100 1 Konstans Wells 0000-0003-0377-2463 2 Nicholas J. Clark 0000-0001-7131-3301 3 67913__32539__e43b543ec0a34e70a0f70f8abc861601.pdf Powell-Romero_etal_2024_RSocOpenSci.pdf 2024-10-05T13:07:38.0554336 Output 791193 application/pdf Version of Record true © 2024 The Author(s). Published by the Royal Society under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/ 284 Francisca Powell-Romero 0000-0001-9800-3100 true https://dx.doi.org/10.5281/zenodo.13382166 false
title A systematic review and guide for using multi-response statistical models in co-infection research
spellingShingle A systematic review and guide for using multi-response statistical models in co-infection research
Konstans Wells
title_short A systematic review and guide for using multi-response statistical models in co-infection research
title_full A systematic review and guide for using multi-response statistical models in co-infection research
title_fullStr A systematic review and guide for using multi-response statistical models in co-infection research
title_full_unstemmed A systematic review and guide for using multi-response statistical models in co-infection research
title_sort A systematic review and guide for using multi-response statistical models in co-infection research
author_id_str_mv d18166c31e89833c55ef0f2cbb551243
author_id_fullname_str_mv d18166c31e89833c55ef0f2cbb551243_***_Konstans Wells
author Konstans Wells
author2 Francisca Powell-Romero
Konstans Wells
Nicholas J. Clark
format Journal article
container_title Royal Society Open Science
container_volume 11
container_issue 10
publishDate 2024
institution Swansea University
issn 2054-5703
doi_str_mv 10.1098/rsos.231589
publisher The Royal Society
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
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description The simultaneous infection of organisms with two or more co-occurring pathogens, otherwise known as co-infections, concomitant infections or multiple infections, plays a significant role in the dynamics and consequences of infectious diseases in both humans and animals. To understand co-infections, ecologists and epidemiologists rely on models capable of accommodating multiple response variables. However, given the diversity of available approaches, choosing a model that is suitable for drawing meaningful conclusions from observational data is not a straightforward task. To provide clearer guidance for statistical model use in co-infection research, we conducted a systematic review to (i) understand the breadth of study goals and host–pathogen systems being pursued with multi-response models and (ii) determine the degree of crossover of knowledge among disciplines. In total, we identified 69 peer-reviewed primary studies that jointly measured infection patterns with two or more pathogens of humans or animals in natural environments. We found stark divisions in research objectives and methods among different disciplines, suggesting that cross-disciplinary insights into co-infection patterns and processes for different human and animal contexts are currently limited. Citation network analysis also revealed limited knowledge exchange between ecology and epidemiology. These findings collectively highlight the need for greater interdisciplinary collaboration for improving disease management.
published_date 2024-10-04T13:12:56Z
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