In the present paper, we propose another method (termed the ''corrected chi-square'') to detect microparasite interactions from serological data, based on an adaptation of the Pearson's chisquare test. By combining logistic regressions and chi-square tests, we are able to calculate the expected frequencies of co-infected individuals if parasites are independent considering their risk factors, and to compare them to the observed ones. In a first step, we perform a theoretical comparison of the robustness of the corrected chi-square and the logistic regression approaches. In a second step, both approaches are applied to serological data obtained in natural populations of domestic cats to search for potential interactions between four feline viruses. The domestic cat is indeed an appropriate model to investigate such questions as its main viruses are well known and rather easy to survey on the field, and its natural populations, although very flexible in their social and spatial organisation, have been extensively studied [32, [58] [59] [60] [61] [62] .