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This study develops a stratified worm burden framework that explicitly models how parasitic worm infections are distributed across different host populations, comparing it to classical models that assume statistical distributions like Poisson or negative-binomial. The analysis reveals that transmission breakpoints—critical thresholds below which parasite populations collapse—occur in more limited circumstances than traditional models suggest, and that predictions about mass drug administration outcomes can differ substantially between modeling approaches even when calibrated to the same initial disease prevalence. The research demonstrates that incorporating demographic factors and mechanistic transmission dynamics produces endemic infection patterns that systematically differ from classical assumptions.
Why it matters
These findings have important implications for helminth disease control programs, suggesting that elimination predictions based on traditional models may overestimate the likelihood of successfully interrupting transmission through mass drug administration. The framework provides a more mechanistic tool for evaluating deworming strategies and understanding when parasite elimination is realistically achievable versus when ongoing control measures will be necessary.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Background. Classical macroparasite models commonly represent worm burden heterogeneity through prescribed Poisson or negative-binomial (NB) distributions, while transmission dynamics are formulated in terms of aggregate quantities such as mean worm burden and prevalence. We revisit these assumptions using a stratified worm burden (SWB) framework that explicitly tracks the distribution of parasite burdens across heterogeneous host classes and allows demographic turnover, transmission, and interventions to be analyzed within a unified model. Methods. We derive equilibrium distributions of the SWB system under homogeneous and heterogeneous infection processes and compare them with the classical Poisson and NB distributions used in macroparasite epidemiology and control policymaking. The framework is then extended to include host demography, coupled human-snail transmission dynamics, and periodic mass drug administration (MDA). Corresponding SWB and NB-type MacDonald models are calibrated to comparable endemic prevalence and mean worm burden, allowing direct comparison of potential transmission breakpoints and intervention outcomes. Results. In the absence of demographic turnover, the SWB framework naturally generates Poisson and NB equilibrium distributions as limiting cases of homogeneous and heterogeneous infection processes. Incorporating demographic recruitment and mortality alters these benchmark distributions, producing endemic burden structures that differ systematically from their classical counterparts. Analysis of coupled human-snail systems reveals that transmission breakpoints and bistable equilibria occur only within a relatively restricted region of SWB parameter space. In contrast, comparable NB-MacDonald formulations exhibit breakpoint behavior over substantially broader parameter ranges with the net effect of overestimating the likelihood of interruption of transmission. Although calibrated models can reproduce near identical endemic prevalence and mean worm burden, they often generate markedly different responses to MDA, including differences in rebound dynamics, persistence, and elimination thresholds. Conclusions. The SWB framework links classical statistical descriptions of worm aggregation to mechanistic burden-resolved population dynamics, clarifying how endemic distributions emerge and how they are modified by demography and control interventions. Our results suggest that transmission breakpoints are not a universal feature of helminth systems but depend strongly on model structure, and that predictions of elimination and post-treatment recovery may be considerably more model-dependent than implied by prevalence-based calibration alone. The SWB approach therefore provides a flexible alternative framework for evaluating parasite transmission dynamics and predicting the outcomes of control strategies.