Optimising sampling regimes and data collection to inform surveillance for trachoma control

Abstract

It is estimated that 190 million individuals are at risk of blindness from trachoma, and that control by mass drug administration (MDA) is reducing this risk in many populations. Programs are monitored using prevalence of follicular trachoma disease (TF) in children. However, as programs progress to low prevalence there are challenges interpreting this indirect measure of infection. PCR and sero-surveillance are being considered as complementary tools to monitor low-level transmission, but there are questions on how they can be most effectively used. We use a previously-published, mathematical model to explore the dynamic relationship between TF and PCR throughout a control program and a sero-catalytic model to evaluate the utility of two cross-sectional sero-surveys for estimating sero-conversion rates. The simulations show that whilst PCR is more sensitive than TF at detecting infection, the probability of detecting at least one positive individual declines during an MDA program more quickly for PCR than for TF (for the same sample size). Towards the end of a program there is a moderate chance of a random sample showing both low PCR prevalence and higher TF prevalence, which may contribute to the lack of correlation observed in epidemiological studies. We also show that conducting two cross-sectional sero-surveys 10 years apart can provide more precise and accurate estimation of epidemiological parameters than a single survey, supporting previous findings that whilst serology holds great promise, multiple cross-sections from the same community are needed to generate the most valuable information about transmission. These results highlight that the quantitative dynamics of infection and disease should be included alongside the many logistical and practical factors to be considered in designing a monitoring and evaluation strategy at the operational research level, in order to help subsequently inform data collection for individual country programs. Whilst our simulations provide some insight, they also highlight that some level of longitudinal, individual-level data on reinfection and disease may be needed to monitor elimination progress.

Modelling trachoma post-2020: opportunities for mitigating the impact of COVID-19 and accelerating progress towards elimination

“…The model for C. trachomatis transmission is based on a previously described framework. 15 , 16 The original population-based, deterministic model based on ordinary differential equations has subsequently been adapted to be stochastic 14 and then further developed here to a fully stochastic individual-based model. The model incorporates current knowledge of the natural history and transmission of trachoma, including direct person-to-person transmission with infectivity proportional to an individual's bacterial load, children acting as a core group for transmission, individuals being susceptible to repeated infections and the persistence of TF after clearance of ocular C. trachomatis infection.…”

“… 4 , 18 , 20 These aspects are represented within the model framework, with bacterial load, duration of ID and D for each individual assumed to decrease with each subsequent infection following negative exponentials. 14 , 16 Age, current infection/disease status and total number of infections for each individual are explicitly incorporated; the model runs in 1-wk time steps.…”

Modelling trachoma post 2020: Opportunities for mitigating the impact of COVID-19 and accelerating progress towards elimination

“…The model for C. trachomatis transmission is based on a previously described framework, 13 which accounts for TF persisting after clearance of ocular C. trachomatis infection. Individuals transitioning through four sequential states: Susceptible (S), infected but not yet diseased (I), infected and diseased (ID) or diseased but no longer infected (D), illustrated in Figure 1.…”

“…Following the specifications of the original model and evidence from empirical studies, 2,12,13 duration of ID and D disease for each individual are assumed to decrease with each subsequent infection following a negative exponential; calculated durations for each infection are used as fixed transition periods in contrast to the exponential transitions utilised in previous models. 13,14…”

Lessons learned for surveillance strategies for trachoma elimination as a public health problem, from the evaluation of approaches utilised by Guinea worm and onchocerciasis programmes: A literature review

“…Further the cross-reactivity of the antibodies to multiple antigens, whether it be other filaria or in the case of trachoma, urogenital chlamydia, means that epidemiological context must be taken into account when determining serological cutoffs. There is potentially greater utility in using serology as an indicator of recent transmission dynamics [83] and for aiding the identification of areas at potential increased risk of recrudescence [65,71,72]. Serology is also a useful indicator as it can easily be integrated with the monitoring of other infectious diseases [12,84].…”