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The development of an age-structured model for trachoma transmission dynamics, pathogenesis and control.

Gambhir M, Basáñez MG, Burton MJ, Solomon AW, Bailey RL, Holland MJ, Blake IM, Donnelly CA, Jabr I, Mabey DC, Grassly NC - PLoS Negl Trop Dis (2009)

Bottom Line: We present an age-structured mathematical model of trachoma transmission and disease to predict the impact of interventions on the prevalence of blinding trachoma.The model is based on the concept of multiple reinfections leading to progressive conjunctival scarring, trichiasis, corneal opacity and blindness.We present a framework that is sufficiently comprehensive to examine the outcomes of the A (antibiotic) component of the SAFE strategy on disease.

View Article: PubMed Central - PubMed

Affiliation: Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom. m.gambhir@imperial.ac.uk

ABSTRACT

Background: Trachoma, 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 findings: The 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/significance: We 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.

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Model fits to data from three endemic settings.Age-profiles of the prevalence of infection generated by fitting the model (solid lines) to the data (squares and 95% confidence interval error bars) from (A) Kongwa, Tanzania [25] (B) Rombo, Tanzania [24] and (C) Upper Saloum, The Gambia [22]. The dotted line in (A) corresponds to a model-fit to the data from Kongwa, Tanzania assuming a lower number of true infection positives, namely those that were determined to harbour chlamydial loads as measured by quantitative PCR (see main text and Text S1). Note the different scale on the y-axis of (A) in comparison to (B) and (C).
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pntd-0000462-g004: Model fits to data from three endemic settings.Age-profiles of the prevalence of infection generated by fitting the model (solid lines) to the data (squares and 95% confidence interval error bars) from (A) Kongwa, Tanzania [25] (B) Rombo, Tanzania [24] and (C) Upper Saloum, The Gambia [22]. The dotted line in (A) corresponds to a model-fit to the data from Kongwa, Tanzania assuming a lower number of true infection positives, namely those that were determined to harbour chlamydial loads as measured by quantitative PCR (see main text and Text S1). Note the different scale on the y-axis of (A) in comparison to (B) and (C).

Mentions: The curves shown in Figure 3A and 3B represent the model-generated age-profiles of the infection load and the rate of recovery from infection based on the maximum likelihood parameters obtained from the analysis of data from the hyperendemic setting (i.e. as in Table 2). These curves show the previously described reduction in infection duration and intensity with age and the parameters thus obtained were used for all subsequent modeling. Model fits to the data by endemicity level are shown in Figure 4. The solid line in Figure 4A illustrates the model-generated infection prevalence curve corresponding to the fit to the hyperendemic data set published by West et al. [25]. The dotted line in Figure 4A is an illustrative fit obtained assuming a lower number of individuals in each age-group classified as positive for infection (see Text S1 for details). The transmission parameter β estimated for this adjusted dataset is approximately two thirds the size of the value shown in Table 2. Figure 4B and 4C illustrate the model-generated infection prevalence curves for the model fitted to the hypo- and mesoendemic datasets (i.e. using the endemic-specific transmission parameter estimate but the infectivity and rate of recovery parameter estimates obtained from the analysis of the data from the hyperendemic setting). Figure 3B shows that the duration of chlamydial infection declines from its initial maximum value to its plateau very rapidly with age, and this is due to the rapid decrease in this value with infection number.


The development of an age-structured model for trachoma transmission dynamics, pathogenesis and control.

Gambhir M, Basáñez MG, Burton MJ, Solomon AW, Bailey RL, Holland MJ, Blake IM, Donnelly CA, Jabr I, Mabey DC, Grassly NC - PLoS Negl Trop Dis (2009)

Model fits to data from three endemic settings.Age-profiles of the prevalence of infection generated by fitting the model (solid lines) to the data (squares and 95% confidence interval error bars) from (A) Kongwa, Tanzania [25] (B) Rombo, Tanzania [24] and (C) Upper Saloum, The Gambia [22]. The dotted line in (A) corresponds to a model-fit to the data from Kongwa, Tanzania assuming a lower number of true infection positives, namely those that were determined to harbour chlamydial loads as measured by quantitative PCR (see main text and Text S1). Note the different scale on the y-axis of (A) in comparison to (B) and (C).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2691478&req=5

pntd-0000462-g004: Model fits to data from three endemic settings.Age-profiles of the prevalence of infection generated by fitting the model (solid lines) to the data (squares and 95% confidence interval error bars) from (A) Kongwa, Tanzania [25] (B) Rombo, Tanzania [24] and (C) Upper Saloum, The Gambia [22]. The dotted line in (A) corresponds to a model-fit to the data from Kongwa, Tanzania assuming a lower number of true infection positives, namely those that were determined to harbour chlamydial loads as measured by quantitative PCR (see main text and Text S1). Note the different scale on the y-axis of (A) in comparison to (B) and (C).
Mentions: The curves shown in Figure 3A and 3B represent the model-generated age-profiles of the infection load and the rate of recovery from infection based on the maximum likelihood parameters obtained from the analysis of data from the hyperendemic setting (i.e. as in Table 2). These curves show the previously described reduction in infection duration and intensity with age and the parameters thus obtained were used for all subsequent modeling. Model fits to the data by endemicity level are shown in Figure 4. The solid line in Figure 4A illustrates the model-generated infection prevalence curve corresponding to the fit to the hyperendemic data set published by West et al. [25]. The dotted line in Figure 4A is an illustrative fit obtained assuming a lower number of individuals in each age-group classified as positive for infection (see Text S1 for details). The transmission parameter β estimated for this adjusted dataset is approximately two thirds the size of the value shown in Table 2. Figure 4B and 4C illustrate the model-generated infection prevalence curves for the model fitted to the hypo- and mesoendemic datasets (i.e. using the endemic-specific transmission parameter estimate but the infectivity and rate of recovery parameter estimates obtained from the analysis of the data from the hyperendemic setting). Figure 3B shows that the duration of chlamydial infection declines from its initial maximum value to its plateau very rapidly with age, and this is due to the rapid decrease in this value with infection number.

Bottom Line: We present an age-structured mathematical model of trachoma transmission and disease to predict the impact of interventions on the prevalence of blinding trachoma.The model is based on the concept of multiple reinfections leading to progressive conjunctival scarring, trichiasis, corneal opacity and blindness.We present a framework that is sufficiently comprehensive to examine the outcomes of the A (antibiotic) component of the SAFE strategy on disease.

View Article: PubMed Central - PubMed

Affiliation: Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom. m.gambhir@imperial.ac.uk

ABSTRACT

Background: Trachoma, 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 findings: The 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/significance: We 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.

Show MeSH
Related in: MedlinePlus