The consequences of uncertainty for the prediction of the effects of schistosomiasis control programmes

Progress in the development of schistosomiasis models for use in control programmes is limited by the considerable uncertainty in many of the biological parameters. In this paper, this problem is addressed by a comprehensive sensitivity analysis of a schistosomiasis model using the Latin Hypercube method. Fifty simulations with different parameter contributions are run for 50 years with treatment during the first 20 years and reinfection thereafter. The analysis shows only a relatively small divergence between simulations during the chemotherapy treatment programme but considerable divergence in reinfection levels after treatment is stopped. A skewed distribution of outcomes was seen with most simulations showing effective control and a few where control had less impact. The most important uncertainty source was due to the unknown levels of acquired immunity and also uncertainty in the true worm burden. In particular, the strength of the immune response was most important in determining whether control was effective with higher immunity leading to less effective control. Among those simulations in which control was not very effective, those in which the mean worm burden was high showed the least effective control. Since both these are areas of genuine uncertainty, it is proposed that uncertainty analysis should be an integral part of any projection of control programmes.