# Inferences on the acquisition of multidrug resistance in \emph{Mycobacterium tuberculosis} using molecular epidemiological data

## TL;DR

This study uses molecular epidemiological data and modeling to understand how multidrug resistance in tuberculosis develops and spreads, focusing on mutation rates, resistance acquisition pathways, and transmission versus evolution contributions.

## Contribution

It introduces a novel model combining resistance evolution and VNTR loci tracking, applying approximate Bayesian computation to analyze incomplete data on MDR TB in Bolivia.

## Key findings

- Double resistance can evolve directly from sensitive states within hosts.
- Differences in mutation rates influence resistance patterns at the population level.
- A significant proportion of MDR TB cases result from transmission rather than resistance acquisition.

## Abstract

We investigate the rates of drug resistance acquisition in a natural population using molecular epidemiological data from Bolivia. First, we study the rate of direct acquisition of double resistance from the double sensitive state within patients and compare it to the rates of evolution to single resistance. In particular, we address whether or not double resistance can evolve directly from a double sensitive state within a given host. Second, we aim to understand whether the differences in mutation rates to rifampicin and isoniazid resistance translate to the epidemiological scale. Third, we estimate the proportion of MDR TB cases that are due to the transmission of MDR strains compared to acquisition of resistance through evolution. To address these problems we develop a model of TB transmission in which we track the evolution of resistance to two drugs and the evolution of VNTR loci. However, the available data is incomplete, in that it is recorded only {for a fraction of the population and} at a single point in time. The likelihood function induced by the proposed model is computationally prohibitive to evaluate and accordingly impractical to work with directly. We therefore approach statistical inference using approximate Bayesian computation techniques.