It is an alternative to negative binomial regression. It can also be used for overdispersed count data. Both the algorithms give similar results, there are differences in estimating the effects of covariates. The variance of a quasi-Poisson model is a linear function of the mean while the variance of a negative binomial model is a quadratic function of the mean.
qs.pos.model <- glm(Days ~ Sex/(Age + Eth*Lrn), data = quine, family = “quasipoisson”)
Quasi-Poisson regression can handle both over-dispersion and under-dispersion.