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A systematic review and guide for using multi-response statistical models in co-infection research
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Konstans Wells ,
Nicholas J. Clark
Royal Society Open Science, Volume: 11, Issue: 10
Swansea University Author:
Konstans Wells
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© 2024 The Author(s). Published by the Royal Society under the terms of the Creative Commons Attribution License.
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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,...
| Published in: | Royal Society Open Science |
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| ISSN: | 2054-5703 |
| Published: |
The Royal Society
2024
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa67913 |
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| 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, 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. |
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| Keywords: |
co-infection, ecology, epidemiology, multi-response, multivariate, statistical modelling |
| College: |
Faculty of Science and Engineering |
| Funders: |
This project was supported by an ARC Discovery Early Career Researcher Award (DE210101439) and TheRoyal Society (RGS\R2\222152). |
| Issue: |
10 |