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

Abstract

Background: Progress towards elimination of trachoma as a public health problem has been substantial, but the coronavirus disease 2019 (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 1. We find that when the basic reproduction number is <1, no significant delays in disease control will be caused. However, when the basic reproduction number is >1, significant delays can occur. In most districts, 1 y 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.

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

Keywords: COVID-19; control; elimination; mass drug administration; mathematical modelling; trachoma.

Figure 1.

Schematic of the 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 assumes disruption of MDA programs occurs at year 1.0 and includes one missed round of MDA at year 1.5. Infection prevalence represents the proportion of children 1–9 y of age 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 in trachoma control due to disruption of MDA. The horizontal grey line represents the 0.05 benchmark for trachoma control that aligns with the WHO's active trachoma criterion for elimination of trachoma as a public health problem.

Figure 2.

Modelling scenarios. Each panel corresponds to a different level of transmission, as defined by whether R₀ and R_T are >1 or <1 (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 a 1-y disruption starting at year 1.0 and includes 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. Between MDA cycles, the transmission dynamics are determined by a susceptible–infected–susceptible model (see Methods for details). For visual clarity, the time series corresponding to the scenarios are slightly offset horizontally. An R₀ of 0.95, 1.5 and 2.5 are assumed for subcritical, MDA-subcritical and supercritical transmission, respectively. At the time of MDA disruption (year 1.0), the infection prevalence of the three transmission scenarios is 0.06, 0.13 and 0.36, respectively. This corresponds to an R_effective of 0.89, 1.30 and 1.61, respectively.

Figure 3.

Our estimate of the control delay for trachoma is depicted by colour, based on the R_effective of the district and the program delay for the administration of MDA. The underlying model assumes annual MDA leads to a 70% decrease in trachoma incidence in the immediate post-treatment interval. The terms subcritical (R_effective<1), MDA-subcritical (R_effective>1–<1.6) and supercritical (R_effective>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. For the subcritical and MDA-subcritical scenarios depicted in Figure 2 with a 1-y MDA disruption occurring at year 1.0, our estimate for the control delay is 0.85 and 2.02 y, respectively. The control delay is not calculable for the supercritical scenario because control is not expected to be achieved with annual MDA.