Mathematical Models of Drug-Resistant Tuberculosis Fail to Address Bacterial Heterogeneity: A Systematic Review

Abstract

Background & aims of study: Antimicrobial resistance (AMR) poses a significant threat to the control of tuberculosis (TB), specifically drug-resistant tuberculosis (DR-TB). Mathematical models are increasingly utilized to inform public health decisions on managing DR-TB. However, an aspect often overlooked is the representation of bacterial heterogeneity in models, which can have implications for understanding DR-TB prevalence. With this study, we aimed to conduct a systematic review of published DR-TB mathematical models to determine the modelling landscape and explore methods of including bacterial heterogeneity.

Methods & results: The objectives of this study were two-fold. Firstly, to identify and analyse the general characteristics of mathematical models pertaining to DR-mycobacteria, including Mycobacterium tuberculosis. Secondly, to examine methods used to incorporate bacterial heterogeneity in these models, defined as the differentiation of bacteria within the same resistance level for human and bacterial population models. With the latter also including heterogeneity between multiple resistant strains. The search was conducted following PRISMA guidelines across five databases, with mechanistic models of DR-mycobacteria included in the analysis.

In total 195 studies were identified, with the majority focusing on dynamic transmission models assessing intervention impacts on M. tuberculosis. The geographical settings and antibiotic resistances modelled exhibited an uneven distribution, with 44% of models considering a single resistance category ("multidrug resistance (MDR)"). Bacterial heterogeneity was only included in 23 models, typically involving resistance to multiple antibiotics (n=17), while six models incorporated heterogeneity within a monoresistant population. Heterogeneity was commonly represented by distinct fitness values within resistant bacterial populations (61%).

Implications: Along with the limited inclusion of bacterial heterogeneity in mathematical models of DR-TB, we have highlighted gaps in our understanding of DR-TB dynamics. Little resistance modelling beyond MDR-TB may mean subtleties are being missed about resistance transmission. By neglecting these aspects, evolutionary effects may be missed, hindering our ability to effectively address AMR in TB. To enhance the accuracy and utility of future models, it is important to consider the diverse nature of DR-TB across different regions and resistance profiles. These considerations may enable policymakers to make informed decisions and develop targeted interventions to combat the growing threat of DR-TB.

Note: This conference abstract was presented at the 9th International Conference on Infectious Disease Dynamics organized by the journal Epidemics. This abstract has not been screened by SSRN for potential for public harm and should not be used to inform any clinical decision making. No competing interests or funding statements have been declared.