Coinfections by noninteracting pathogens are not independent and require new tests of interaction

Hamelin, Frédéric M.; Allen, Linda J. S.; Bokil, Vrushali A.; Gross, Louis J.; Hilker, Frank M.; Jeger, Michael J.; Manore, Carrie Anna; Power, Alison G.; Rúa, Megan A.; Cunniffe, Nik J.; Kucharski, Adam J.

doi:10.1371/journal.pbio.3000551

Title: Coinfections by noninteracting pathogens are not independent and require new tests of interaction

Journal Article · 03 December 2019 · PLoS Biology (Online)

DOI: https://doi.org/10.1371/journal.pbio.3000551 · OSTI ID:1603988

^[1]; Allen, Linda J. S. ^[2]; Bokil, Vrushali A. ^[3]; Gross, Louis J. ^[4]; Hilker, Frank M. ^[5]; Jeger, Michael J. ^[6];

^[7];

^[8];

^[9];

^[10]; Kucharski, Adam J. ^[11]

Univ. de Rennes (France)
Texas Tech Univ., Lubbock, TX (United States)
Oregon State Univ., Corvallis, OR (United States)
Univ. of Tennessee, Knoxville, TN (United States)
Osnabruck Univ. (Germany)
Imperial College London, Ascot (United Kingdom)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Cornell Univ., Ithaca, NY (United States)
Wright State Univ., Dayton, OH (United States)
Univ. of Cambridge (United Kingdom)
London School of Hygiene & Tropical Medicine (United Kingdom)

If pathogen species, strains, or clones do not interact, intuition suggests the proportion of coinfected hosts should be the product of the individual prevalences. Independence consequently underpins the wide range of methods for detecting pathogen interactions from cross-sectional survey data. However, the very simplest of epidemiological models challenge the underlying assumption of statistical independence. Even if pathogens do not interact, death of coinfected hosts causes net prevalences of individual pathogens to decrease simultaneously. The induced positive correlation between prevalences means the proportion of coinfected hosts is expected to be higher than multiplication would suggest. By modelling the dynamics of multiple noninteracting pathogens causing chronic infections, we develop a pair of novel tests of interaction that properly account for nonindependence between pathogens causing lifelong infection. Our tests allow us to reinterpret data from previous studies including pathogens of humans, plants, and animals. Our work demonstrates how methods to identify interactions between pathogens can be updated using simple epidemic models.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: LDRD; National Science Foundation (NSF)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1603988

Report Number(s):: LA-UR--19-23586

Journal Information:: PLoS Biology (Online), Journal Name: PLoS Biology (Online) Journal Issue: 12 Vol. 17; ISSN 1545-7885

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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Multiple infections and the evolution of virulence Alizon, Samuel; de Roode, Jacobus C.; Michalakis, Yannis Ecology Letters, Vol. 16, Issue 4 https://doi.org/10.1111/ele.12076	journal	January 2013
The community ecology of pathogens: coinfection, coexistence and community composition Seabloom, Eric W.; Borer, Elizabeth T.; Gross, Kevin Ecology Letters, Vol. 18, Issue 4 https://doi.org/10.1111/ele.12418	journal	March 2015
Detecting interspecific macroparasite interactions from ecological data: patterns and process: Detecting interspecific parasite interactions Fenton, Andy; Viney, Mark E.; Lello, Jo Ecology Letters, Vol. 13, Issue 5 https://doi.org/10.1111/j.1461-0248.2010.01458.x	journal	April 2010
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