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Fig. 1 | Parasites & Vectors

Fig. 1

From: Refined stratified-worm-burden models that incorporate specific biological features of human and snail hosts provide better estimates of Schistosoma diagnosis, transmission, and control

Fig. 1

Comparison of equilibrium worm burden distributions. The SWB distribution {h k } of Schistosoma worm burden can be viewed as probability distribution function (PDF) representing an ensemble of stochastic agents (human hosts) having a prescribed mean rate of worm accumulation λΔw and worm resolution (death) rate γ, yielding an equilibrium level of infection over time. In Panel a, we used stochastic individual-agent simulation to repeatedly follow an ensemble of 200 hosts with prescribed mean λ,γ, to determine their progression from no infection to an equilibrium endemic state. The graph shows the multiple ensemble histories and their mean (thick line) which closely follows relaxation dynamics of earlier deterministic models [8], i.e. \( \frac{dw}{dt}=\lambda -\gamma w \), approaching equilibrium w* = λ/γ. In Panel b, the PDF of stochastic simulation equilibrium values (blue line) is compared to a fitted negative binomial curve, NB(k, w*) (gray line) and to an ensemble of equilibrium SWB model predictions {h k (λ/γ)} (red line). We observe close proximity of the three curves, justifying the view that SWB approximates a stochastic agent model in terms of ensemble PDF, given identical λ,γ. The resulting worm distribution patterns are highly aggregated (k = 231 for fitted NB) and close to a Poisson distribution, in contrast to the highly overdispersed patterns seen for patient egg-count data [16]

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