Implications of the COVID-19 pandemic on eliminating trachoma as a public health problem

Abstract

Background: Progress towards elimination of trachoma as a public health problem has been substantial, but the COVID-19 pandemic has disrupted community-based control efforts.

Methods: We use a susceptible-infected model to estimate the impact of delayed distribution of azithromycin treatment on the prevalence of active trachoma.

Results: We identify three distinct scenarios for geographic districts depending on whether the basic reproduction number and the treatment-associated reproduction number are above or below a value of one. We find that when the basic reproduction number is below one, no significant delays in disease control will be caused. However, when the basic reproduction number is above one, significant delays can occur. In most districts a year of COVID-related delay can be mitigated by a single extra round of mass drug administration. However, supercritical districts require a new paradigm of infection control because the current strategies will not eliminate disease.

Conclusion: If the pandemic can motivate judicious, community-specific implementation of control strategies, global elimination of trachoma as a public health problem could be accelerated.

Figure 1:

Schematic of model. The ‘MDA as planned’ scenario involves periods of exponential growth of prevalence, punctuated by uniformly spaced reduction due to annual MDA. The ‘MDA disrupted’ scenario includes one missed round of MDA at year 1.5. Infection prevalence represents the proportion of children age 1–9 with current infection. Note that infection prevalence is distinct from the clinical manifestation of trachomatous inflammation—follicular. An R₀ of 1.5 is assumed. The ‘program delay’ is the length of MDA disruption. The ‘control delay’ is the expected delay of trachoma control due to disruption of MDA. The horizontal grey line represents the 0.05 benchmark used by WHO as part of their criteria for elimination of trachoma as a public health problem.

Figure 2:

Modeling scenarios. Each panel corresponds to a different level of transmission, as defined by whether R₀ and R_T are greater or less than a value of one (Table 1). The layout of each panel is similar to Figure 1. Within each panel the ‘MDA as planned’ scenario corresponds to no disruption of annual MDA. The ‘MDA disrupted’ scenario corresponds to skipping one annual MDA cycle at year 1.5. The ‘MDA catch-up’ scenario involves giving an extra MDA at year 3, after skipping an annual MDA at year 1.5. An R₀ of 0.95, 1.3, and 1.65 are assumed for subcritical, MDA-subcritical, and supercritical transmission respectively. For visual clarity, the time series corresponding to the scenarios are offset horizontally slightly.

Figure 3:

The control delay for trachoma is depicted by color, based on the R₀ of the district and the program delay for the administration of MDA. The underlying model assumes annual MDA leads to a 70% decrease of trachoma incidence in the immediate post-treatment interval. The terms subcritical (R₀ < 1), MDA-subcritical (1 < R₀ < 1.6), and supercritical (R₀ > 1.6) refer to conditions in which infection is expected to be self-limited, requires annual MDA in order for control targets to be achieved, or requires a new paradigm of treatment for eventual control, respectively. The different categories of transmission are demarcated by vertical black lines. Although our focus is on how the COVID-19 pandemic impacts trachoma control, the underlying model can be applied to a variety of diseases and program delay scenarios.