Simple models for macro-parasite distributions in hosts

TL;DR

This paper compares simple alternative distributions to the negative binomial for modeling macro-parasite burdens, demonstrating better fit and easier parameter estimation.

Contribution

It introduces simple, closed-form maximum likelihood estimators for zero-inflated and hurdle geometric distributions, improving modeling of parasite data.

Findings

Zero-inflated geometric fits data better than negative binomial.

Closed-form estimators simplify parameter estimation.

Alternative models outperform traditional negative binomial.

Abstract

Negative binomial distribution is the most used distribution to model macro-parasite burden in hosts. However reliable maximum likelihood parameter estimation from data is far from trivial. No closed formula is available and numerical estimation requires sophisticated methods. Using data from the literature we show that simple alternatives to negative binomial, like zero-inflated geometric or hurdle geometric distributions, produce a good and even better fit to data than negative binomial distribution. We derived closed simple formulas for the maximum likelihood parameter estimation which constitutes a significant advantage of these distributions over negative binomial distribution.