Dynamics of trachoma infection in West Africa revealed by a hidden state model

Trachoma is estimated to be the leading infectious cause of blindness globally, predominantly affecting low-income populations with poor sanitation and hygiene. Over a decade of mass drug administration with antibiotics has led to substantial progress in control and elimination, but hotspots remain where infection persists or rebounds following mass drug administration for reasons that remain unclear. Transmission modelling is a key component of understanding these dynamics, but the complex dynamics of infection and reinfection with Chlamydia trachomatis are challenging to infer from cross–sectional surveys. Here, we analyze longitudinal data collected over six months in 1991 using multiple diagnostics from two villages in The Gambia by developing and fitting a Bayesian epidemiological model that classifies individuals into disease states at each time point using a forward-filtering backward-sampling algorithm. We find that infection risk is clustered within households and the weekly probability of transmission within a shared room is 40–fold higher than in a shared village. Infected children are estimated to contribute disproportionately to transmission, accounting for 70–90% of the force of infection within the observed period. We estimate the basic reproduction number, R0, to be 2.2 by simulation and find that the distribution of secondary cases per individual is less aggregated than for other directly-transmitted pathogens. We further quantify heterogeneity in predisposition to becoming infected and estimate the sensitivity and specificity for PCR, antigen detection tests, and clinical examinations. Our study uncovers the natural history of trachoma infection, with implications for simulating pathogen dynamics and designing interventions to halt transmission and prevent avoidable cases of blindness.Author summary: Trachoma is an infectious disease that can lead to blindness through repeated infections over time. Understanding trachoma transmission is important for designing surveys, evaluating the impact of different intervention strategies, and allocating resources for control programmes. Here, we infer transmission properties by analysing data from two villages in The Gambia, in which the same cohort of individuals were followed for six months. By developing and fitting an individual-level model to the 1410 individuals, we derive the impacts of household structure and age on trachoma transmission. Our analysis finds that infection risk is strongly impacted by household structure, with transmission between individuals sharing a room being 40 times higher than between individuals sharing only a village. We also find that transmission is dominated by children, who contribute over 70% of the force of infection over the study period. We further quantify differences in predisposition to infection between individuals. Finally, we determine the error rates of PCR, antigen detection tests, and clinical examinations, which were used during the study.