About: True versus False Parasite Interactions: A Robust Method to Take Risk Factors into Account and Its Application to Feline Viruses

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	True versus False Parasite Interactions: A Robust Method to Take Risk Factors into Account and Its Application to Feline Viruses
Creator	Fouchet, David Pontier, Dominique Poulet, Hervé Sauvage, Frank Onore Hellard, Elé
source	PMC
abstract	BACKGROUND: Multiple infections are common in natural host populations and interspecific parasite interactions are therefore likely within a host individual. As they may seriously impact the circulation of certain parasites and the emergence and management of infectious diseases, their study is essential. In the field, detecting parasite interactions is rendered difficult by the fact that a large number of co-infected individuals may also be observed when two parasites share common risk factors. To correct for these “false interactions”, methods accounting for parasite risk factors must be used. METHODOLOGY/PRINCIPAL FINDINGS: In the present paper we propose such a method for presence-absence data (i.e., serology). Our method enables the calculation of the expected frequencies of single and double infected individuals under the independence hypothesis, before comparing them to the observed ones using the chi-square statistic. The method is termed “the corrected chi-square.” Its robustness was compared to a pre-existing method based on logistic regression and the corrected chi-square proved to be much more robust for small sample sizes. Since the logistic regression approach is easier to implement, we propose as a rule of thumb to use the latter when the ratio between the sample size and the number of parameters is above ten. Applied to serological data for four viruses infecting cats, the approach revealed pairwise interactions between the Feline Herpesvirus, Parvovirus and Calicivirus, whereas the infection by FIV, the feline equivalent of HIV, did not modify the risk of infection by any of these viruses. CONCLUSIONS/SIGNIFICANCE: This work therefore points out possible interactions that can be further investigated in experimental conditions and, by providing a user-friendly R program and a tutorial example, offers new opportunities for animal and human epidemiologists to detect interactions of interest in the field, a crucial step in the challenge of multiple infections.
has issue date	2012-01-03(xsd:dateTime)
bibo:doi	10.1371/journal.pone.0029618
bibo:pmid	22235312
has license	cc-by
sha1sum (hex)	315303952d93ba93f55029cca73e2f483bc16362
schema:url	https://doi.org/10.1371/journal.pone.0029618
resource representing a document's title	True versus False Parasite Interactions: A Robust Method to Take Risk Factors into Account and Its Application to Feline Viruses
has PubMed Central identifier	PMC3250451
has PubMed identifier	22235312
schema:publication	PLoS One
resource representing a document's body	covid:315303952d93ba93f55029cca73e2f483bc16362#body_text
is schema:about of	named entity 'natural' named entity 'observed' named entity 'Robust' named entity 'Viruses' named entity 'VIRUSES' named entity 'LARGE' named entity 'EMERGENCE' named entity 'TRUE' named entity 'ROBUST' named entity 'INFECTIOUS DISEASES' named entity 'COMMON' named entity 'INFECTED' named entity 'METHODS' named entity 'INDIVIDUAL' named entity 'OBSERVED' named entity 'INTERACTIONS' named entity 'TO TAKE' named entity 'INTERACTIONS' named entity 'IMPACT' named entity 'IN THE FIELD' named entity 'USED' named entity 'FALSE' named entity 'PARASITE' named entity 'ACCOUNT' named entity 'ITS' named entity 'METHOD' named entity 'FELINE' named entity 'STUDY' named entity 'THESE' named entity 'NATURAL' named entity 'MANAGEMENT' named entity 'ESSENTIAL' named entity 'PARASITE' named entity 'CERTAIN' named entity 'HOST' named entity 'SHARE' named entity 'MULTIPLE INFECTIONS' named entity 'CIRCULATION' named entity 'PARASITES' named entity 'LIKELY' named entity 'ACCOUNTING' named entity 'THEIR' named entity 'CORRECT' named entity 'DETECTING' named entity 'BACKGROUND' named entity 'RISK FACTORS' named entity 'POPULATIONS' named entity 'RISK FACTORS' named entity 'APPLICATION' named entity 'NUMBER OF' named entity 'DIFFICULT' named entity 'FACT' covid:arg/315303952d93ba93f55029cca73e2f483bc16362 named entity 'parasite' named entity 'large number' named entity 'field' named entity 'parasites' named entity 'Risk Factors' named entity 'parasite' named entity 'risk factors' named entity 'Risk Factors' named entity 'Viruses' named entity 'null hypothesis' named entity 'microparasites' named entity 'odds ratios' named entity 'infection' named entity 'seropositivity' named entity 'risk factors' named entity 'seropositive' named entity 'infection' named entity 'chi-square test' named entity 'complex networks' named entity 'protozoan' named entity 'seropositive' named entity 'hormonal' named entity 'European wildcat' named entity 'immune responses'

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