Hidden reservoirs: how host genetic heterogeneity defeats symptom-driven malaria surveillance

Abstract

Malaria surveillance in endemic settings relies predominantly on symptom-driven testing. In populations carrying sickle cell trait (HbAS), this strategy has a structural blind spot: HbAS carriers experience reduced clinical severity while remaining infectious, making them largely invisible to symptom-triggered diagnostics. We use a rule-based stochastic model implemented in the Kappa language to characterise the interaction between host genetic heterogeneity and surveillance bias. A systematic parameter sweep across reservoir size, test bias, and test rate reveals that the interaction is multiplicative: at realistic HbAS prevalence (20–25%), purely symptomatic testing requires roughly double the effort of random testing to achieve epidemic suppression, and at higher prevalence fails entirely regardless of intensity. Sensitivity analysis confirms that this effect is driven by observation bias — who gets tested — rather than by differential recovery rates. Quantified comparisons across testing strategies show that hybrid approaches allocating a substantial fraction of tests randomly outperform both purely symptomatic and purely random strategies. The test count analysis demonstrates that symptom-biased testing is a false economy: it consumes fewer tests but fails to control transmission, while random testing costs more but succeeds. These results suggest that incorporating random screening into malaria surveillance programmes in HbAS-prevalent regions could substantially improve epidemic control at modest additional cost.

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ORCID for William Waites: orcid.org/0000-0002-7759-6805

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