The transmission dynamics of Strongyloides stercoralis and the impact of mass drug administration

Abstract

The epidemiology of Strongyloides stercoralis is briefly reviewed with an emphasis on cross section and longitudinal studies of infection prevalence stratified by age, performance of different diagnostic tools, mass drug administration (MDA) impact and estimates of key population parameters within the complex life cycle of the parasite that determine transmission intensity and response to control measures. The paucity of studies is highlighted, and gaps in current knowledge identified about the population biology of this very prevalent infection in tropical and sub-tropical regions around the world. A stochastic individual based stochastic model is described in part to highlight gaps in knowledge. The impact of repeated MDA is simulated to illustrate some aspects of transmission dynamics of this helminth infection. Specifically, the impact and bounce back times in either the intervals between treatment rounds, or post the cessation of treatment, depend critically on the magnitude of two distinct components of the basic reproductive number R₀. The absence of data on these key components is highlighted, as is the value of studies of longitudinal cohorts of people in regions of endemic infection post rounds of MDA to record how infection levels bounce back post treatment at individual and population levels of study. This article is part of the Theo Murphy meeting issue 'Strongyloides: omics to worm-free populations'.

Keywords: Strongyloides stercoralis; disease modelling; parasite.

Figure 1.

Number of publications per year on S. stercoralis NIH National Library of Medicine PubMed up to June 2023 (using search term Strongyloides steracolis).

Figure 2.

Age prevalence patterns of infection reaching a plateau with increasing age (from Conway et al. [9]).

Figure 3.

Age prevalence of infection with patterns increasing with age (from Conway et al. [9]).

Figure 4.

Flow chart of the key populations and population processes in the life cycle of Strongyloides stercoralis. (Online version in colour.)

Figure 5.

The mean equilibrium prevalence as a function of R₀, with R_I = R_F = R₀/2, k = 0.1. The estimates are generated from 50 time points after the system has reached an equilibrium, and averaged over 200 independent runs. (Online version in colour.)

Figure 6.

Fit of the stochastic model to age aggregated data [5]: (a) age aggregated prevalence, (b) prevalence as a function of R₀, with R_I = R_F = R₀/2, derived from numerical studies of the stochastic model. *** signifies the Pearson correlation coefficient is statistically significant at the p-value <0.001 level. (Online version in colour.)

Figure 7.

The true prevalence and mean burden across the total population following five rounds of annual mass treatment as predicted by the stochastic model. The orange line shows the mean model behaviour for R_F = 0.9, R_I = 1.6, k = 0.01 and the shaded area shows 95% of the distribution of outcomes. (Online version in colour.)

Figure 8.

The true prevalence and mean burden across the total population following five rounds of annual mass treatment as predicted by the stochastic model. The orange line shows the mean model behaviour for R_F = 2.3, R_I = 0.5, k = 0.1 and the shaded area shows 95% of the distribution of outcomes. (Online version in colour.)

Figure 9.

R_F = 1.4 R_I = 1.4, k = 0.05. The mean burden and true prevalence following five rounds of mass treatment as predicted by the stochastic model. The shaded areas show the range of outcomes generated by the stochastic model for a fixed parameter set. (Online version in colour.)