Data were analyzed by two-way analysis of variance using GraphPad Prism version 5.00 (GraphPad Software, San Diego, CA, USA). When a significant effect was observed, pairwise comparisons were performed using the Bonferroni post-hoc test. All data are reported as mean 6 SD.  True versus False Parasite Interactions: A Robust Method to Take Risk Factors into Account and Its Application to Feline Viruses 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. Numerous parasites species circulate simultaneously in natural populations. Many of them are able to infect a same host species and a host individual can therefore be infected by several parasites at the same time. These multiple infections are not only common in nature but usually more frequently encountered than infections by a single parasite [1] . Within a host individual, parasites can thus interact, either in a synergistic manner (parasite A favours infection by parasite B or worsens the symptoms caused by B) or in an antagonistic manner (parasite A decreases the infection risk by parasite B or reduces the symptoms caused by B) [2] . As these interactions can have important epidemiological, biological and clinical consequences (e.g., [3] [4] [5] [6] [7] ), detecting, understanding and evaluating them is essential to understand the phenomena and to control and manage infectious diseases.