True versus false parasite interactions: a robust method to take risk factors into account and its application to feline viruses

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.

Figure 1. Robustness of the two approaches.

Type I error (%) for the logistic regressions approach (blue empty points) and the corrected chi-square test (red full points) depending on the ratio of the number of factors to the sample size (N_F/n), considering three scenarios: i) all factors are qualitative (A); ii) all factors are quantitative (B) and iii) half of the factors are quantitative and the other half are qualitative (C). The type I error of the corrected chi-square tests represented here is based on P-value2 but similar results were observed with P-value1 (Fig. S3). Note that for the logistic regression approach, points resulting from a given sample size were linked to see the effect of the N_F/n ratio for different sample sizes (solid line: n = 100, dashed line: n = 200, dotted line: n = 300). The dashed horizontal line represents a type I error of 5%.