Introducing risk inequality metrics in tuberculosis policy development

Abstract

Global stakeholders including the World Health Organization rely on predictive models for developing strategies and setting targets for tuberculosis care and control programs. Failure to account for variation in individual risk leads to substantial biases that impair data interpretation and policy decisions. Anticipated impediments to estimating heterogeneity for each parameter are discouraging despite considerable technical progress in recent years. Here we identify acquisition of infection as the single process where heterogeneity most fundamentally impacts model outputs, due to selection imposed by dynamic forces of infection. We introduce concrete metrics of risk inequality, demonstrate their utility in mathematical models, and pack the information into a risk inequality coefficient (RIC) which can be calculated and reported by national tuberculosis programs for use in policy development and modeling.

The effects of individual nonheritable variation on fitness estimation and coexistence

“…Simpler versions of heterogeneous systems such as these have been shown to provide more accurate descriptions of infectious disease dynamics than their homogeneous analogues (Dwyer, Elkinton, & Buonaccorsi, 1997;Gomes et al, 2019;King, Souto-Maior, Sartori, Maciel-de-Freitas, & Gomes, 2018;Langwig et al, 2017). Here, we demonstrate their capacity to support coexistence of multiple strains in a scenario where competition mediated by host immunity is maximal, as shown for two and three strains in Figure 6 and generated inductively for any natural number N.…”

“…Supported by evidence from bacterial systems (Balaban et al, 2004;Cadena, Fortune, & Flynn, 2017;Gomes et al, 2019;Hashimoto et al, 2016;Jouvet, Rodriguez-Rojas, & Steiner, 2018;Kiviet et al, 2014;Levin, 2004;Trauer et al, 2019), we build two model suites which in later sections will be used to explore how nonheritable variation in fitness components may affect the response of a population under different levels of stress, bias common measures of relative fitness between genotypes or strains and associated selection coefficients, and affect their ability to coexist when placed in competition for shared resources.…”

“…Second, we resort to models for microbial colonization of a host population to address variation in susceptibility among hosts (Diekmann, Heesterbeek, & Britton, 2012;Gomes et al, 2019) as a natural manifestation of variation in resource suitability (from the bacterial viewpoint):…”

On the Mathematics of Populations

“…become infected given a pathogen challenge) and therefore cannot be measured directly. This obstacle, which may be part of the reason behind the widespread adoption of homogeneous models, is starting to be overcome by specific study designs that recognise the need for unpacking exposure gradients [7, 20, 12, 5, 17, 13, 8] as explicit experimental or observational dimensions.…”

Modelling the epidemiology of residual Plasmodium vivax malaria in a heterogeneous host population: a case study in the Amazon Basin

“…SIS models of infectious diseases may incorporate risk heterogeneity among hosts as, for example, a continuous distribution of hosts’ susceptibility to infection, which can be determined empirically from the proportions of hosts that are experimentally infected at different pathogen challenge doses [7–9]. Alternatively, models may assume that the population of susceptible individuals is divided into a finite number of susceptibility classes or frailty groups [9–13].…”