The zero-inflated negative binomial model is a statistical method used to handle count data with excessive zeros and overdispersion, where the variance exceeds the mean. It combines a negative binomial distribution for count data with a logit model to account for the excess zeros, providing a more accurate fit for datasets that have more zeros than expected by a standard negative binomial model.