The article explores different models for analyzing data on parasites intensity, focusing on count data with more variance than expected. The researchers used various generalized regression models to handle this overdispersion, including the generalized Poisson and negative binomial models. They also considered zero-inflated models for data with many zeros. By comparing different models using statistical measures like log-likelihood and Akaike information criterion, the study found that these generalized models can effectively analyze count data with excessive zeros and variance.