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. 2025 Apr 21;21(4):e1013026.
doi: 10.1371/journal.pcbi.1013026. eCollection 2025 Apr.

Modelling transmission thresholds and hypoendemic stability for onchocerciasis elimination

Affiliations

Modelling transmission thresholds and hypoendemic stability for onchocerciasis elimination

Jacob N Stapley et al. PLoS Comput Biol. .

Abstract

The World Health Organization (WHO) has proposed elimination of onchocerciasis transmission (EOT) in a third of endemic countries by 2030. This requires country-wide verification of EOT. Prior to the shift from morbidity control to EOT, interventions in Africa were mostly targeted at moderate- to high-transmission settings, where morbidity was most severe. Consequently, there remain numerous low transmission (hypoendemic) settings which have hitherto not received mass drug administration (MDA) with ivermectin. The WHO has prioritised the delineation of hypoendemic settings to ascertain treatment needs. However, the stability of transmission at such low levels remains poorly understood. We use the stochastic EPIONCHO-IBM transmission model to characterise the stability of transmission dynamics in hypoendemic settings and identify a range of threshold biting rates (TBRs, the annual vector biting rates below which transmission cannot be sustained). We show how TBRs are dependent on population size, inter-individual exposure heterogeneity and simulation time. In contrast with deterministic expectations, there is no fixed TBR; instead, transmission can persist between 70 and 300 bites/person/year. Using survivorship models on data generated from model simulations, we find that multiple vector biting rates can sustain hypoendemic prevalence for several decades. These findings challenge the assumption that hypoendemic foci would naturally fade out following treatment in nearby higher-endemicity regions. Our modelling suggests that, to achieve EOT, treatment should be extended to all areas where endogenous infection is identified, emphasising the need for improved diagnostic tools suitable for detecting low-prevalence infection and for strategies that allow safe treatment of communities where MDA would not be suitable.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Stability of hypoendemicity and threshold biting rates (TBRs).
Three hundred repeat simulations of EPIONCHO-IBM were run for 500 years, determining the proportion of persistent runs with greater than 0% microfilarial (mf) prevalence. Panel A: Colours from dark red to light yellow indicate a decreasing proportion of model simulations persisting over time for different annual biting rate values (ABRs). Results are shown for a population of 400 individuals (N = 400) and inter-individual exposure heterogeneity parameter kE = 0.3, with the light blue box highlighting persistent proportions after 500 years of simulation. Panel B: Markers are the proportion of the 300 model runs persisting after 500 years for ABRs falling within the TBR range (i.e., > 0% and < 100% run persistence). The blue-highlighted box shows the proportion of persistent runs at the simulation endpoint as generated by the model parameters used in Panel A. Remaining boxes show different combinations of N and kE as indicated by the row and column labels, respectively.
Fig 2
Fig 2. Rate of fade-out of onchocerciasis transmission within and below threshold biting rate (TBR) range.
The colour of each line corresponds to an annual biting rate (ABR; no. bites/person/year) for a population of N = 400 and kE = 0.3, with the rate of fade-out calculated by fitting a Weibull survivorship function (Eqn. 1, 2) to 300 EPIONCHO-IBM simulations (Fig C in S1 Text) from the final timepoint which gave 100% run persistence (i.e., all simulations > 0% microfilarial prevalence).
Fig 3
Fig 3. Mean microfilarial (mf) prevalence dynamics within and below threshold biting rate (TBR) range.
The colour of each line corresponds to an annual biting rate (ABR; no. bites/person/year) for a population of N = 400 and kE = 0.3, with mean mf prevalence calculated from 300 EPIONCHO-IBM simulations. Dynamics are plotted from a 16% mf prevalence at time zero corresponding to the minimum mf prevalence with no fade-out over 500 years (i.e., zero rate of fade-out; Fig. 2). Mf prevalence declines more rapidly with decreasing ABR, but may persist (i.e., mf prevalence > 0%) for epidemiologically-relevant periods even at very low ABRs.

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