In which situation is a zero-inflated model particularly useful?

A zero-inflated model is particularly useful in situations where the data exhibits an excess of zeros. This type of model is specifically designed to handle count data that has more zero outcomes than would be expected under a standard Poisson or negative binomial model. For example, in scenarios such as modeling the number of times a person visits a doctor in a year, it's common to have a large number of individuals who do not visit at all (resulting in a zero count). The zero-inflated model helps to parameterize these extra zeros by assuming that there are two processes at play: one that determines whether the count is zero and another that generates the count of non-zero occurrences.

By capturing these excess zeros effectively, the zero-inflated model provides a better fit and more accurate inference compared to models that do not account for this characteristic of the data. Thus, when excess zeros are evident in the dataset, employing a zero-inflated model is the correct approach to accurately depict the underlying distribution of the data.