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Coinfections by noninteracting pathogens are not independent and require new tests of interaction.
PLOS Biology ( IF 9.8 ) Pub Date : 2019-12-03 , DOI: 10.1371/journal.pbio.3000551
Frédéric M Hamelin 1 , Linda J S Allen 2 , Vrushali A Bokil 3 , Louis J Gross 4 , Frank M Hilker 5 , Michael J Jeger 6 , Carrie A Manore 7 , Alison G Power 8 , Megan A Rúa 9 , Nik J Cunniffe 10
Affiliation  

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.

中文翻译:

非相互作用病原体的合并感染不是独立的,需要进行新的相互作用测试。

如果病原体种类,菌株或克隆不相互作用,则直觉表明合并感染宿主的比例应为个体患病率的产物。因此,独立性为从横截面调查数据中检测病原体相互作用的广泛方法奠定了基础。但是,最简单的流行病学模型挑战了统计独立性的基本假设。即使病原体不相互作用,合并感染的宿主死亡也会导致单个病原体的净流行率同时下降。在患病率之间诱发正相关,这意味着合并感染宿主的比例预计将比乘法提示的比例高。通过对导致慢性感染的多种非相互作用病原体的动力学进行建模,我们开发了一对新颖的相互作用测试,可以正确解释导致终身感染的病原体之间的非独立性。我们的测试使我们能够重新解释以前的研究数据,包括人类,植物和动物的病原体。我们的工作表明如何使用简单的流行病模型更新识别病原体之间相互作用的方法。
更新日期:2019-12-04
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