The Development of an Age-Structured Model for Trachoma Transmission Dynamics, Pathogenesis and Control

Abstract

BackgroundTrachoma, the worldwide leading infectious cause of blindness, is due to repeated conjunctival infection with Chlamydia trachomatis. The effects of control interventions on population levels of infection and active disease can be promptly measured, but the effects on severe ocular sequelae require long-term monitoring. We present an age-structured mathematical model of trachoma transmission and disease to predict the impact of interventions on the prevalence of blinding trachoma.Methodology/Principal FindingsThe model is based on the concept of multiple reinfections leading to progressive conjunctival scarring, trichiasis, corneal opacity and blindness. It also includes aspects of trachoma natural history, such as an increasing rate of recovery from infection and a decreasing chlamydial load with subsequent infections that depend upon a (presumed) acquired immunity that clears infection with age more rapidly. Parameters were estimated using maximum likelihood by fitting the model to pre-control infection prevalence data from hypo-, meso- and hyperendemic communities from The Gambia and Tanzania. The model reproduces key features of trachoma epidemiology: 1) the age-profile of infection prevalence, which increases to a peak at very young ages and declines at older ages; 2) a shift in this prevalence peak, toward younger ages in higher force of infection environments; 3) a raised overall profile of infection prevalence with higher force of infection; and 4) a rising profile, with age, of the prevalence of the ensuing severe sequelae (trachomatous scarring, trichiasis), as well as estimates of the number of infections that need to occur before these sequelae appear.Conclusions/SignificanceWe present a framework that is sufficiently comprehensive to examine the outcomes of the A (antibiotic) component of the SAFE strategy on disease. The suitability of the model for representing population-level patterns of infection and disease sequelae is discussed in view of the individual processes leading to these patterns.

Serology reflects a decline in the prevalence of trachoma in two regions of The Gambia

“…This resulted in a more acute change in SCR, as has been demonstrated in malaria modelling exercises 45,46 . Previous trachoma modelling studies suggest that an individual may require upwards of 100 lifetime ocular Ct infections in order to develop TT 47 , so even a modest reduction in transmission may have significant public health implications and reduce the future incidence of TT.…”

Mass drug administration: the importance of synchrony

“…More comprehensive studies should consider the influence of partial acquired immunity (Liu et al ., 2013), chemoprophylaxis in susceptible persons, seasonality in transmission (Lee et al ., 2005), age-structure in the host population (Bailey et al ., 1999; Lietman et al ., 1999; Gambhir et al ., 2009), bacterial load (Shattock et al ., 2015), case importation, and other ecological or epidemiological factors (Lietman et al ., 1999). Changes in population size, structure and distribution need to be reflected in a long-term MDA program.…”

The Allee Effect and Elimination of Neglected Tropical Diseases: A Mathematical Modelling Study

“…This article discussed the proposal that there might be an Allee effect operating for trachoma, an idea based on the observation that the community prevalence level in a mass drug administration (MDA) field trial had held steady over a year after the administration of drug to the population (Solomon et al, 2004). This observation stood in contrast to the predictions of modelling and other posttreatment observational studies which found re-emergence of infection and disease to be the most likely outcome (Gambhir et al, 2010a(Gambhir et al, , 2009Liu et al, 2014;Burton et al, 2005;West et al, 2005). The proposed mechanisms for an Allee effect for trachoma include reductions in bacterial genotypic diversity leading to more effective immune responses from the host population, and a dose-dependent effect in which lower community prevalence leads to lower average chlamydial load and lower probability of transmission per contact.…”