a, Parasite density distributions of the three infection groups defined in the statistical analysis section. b, Proportion of infections detected for each infection group for a range of diagnostic thresholds between 0.001 and 107 parasites per microlitre. For each value of the x-axis we calculate the proportion of each density distribution (from a) that would be detected. c, The proportion of the infectious reservoir of the whole population that would be detected for each diagnostic threshold. This is the combined infectivity to mosquitoes of all individuals with asexual parasite densities above the diagnostic threshold weighted by body size. The dashed vertical lines show the three detection thresholds considered: 200, 20 and 2 parasites per microlitre. d, Proportion of the infectious reservoir detected. Comparison of the observed data as shown in c to a simulation-based approach using OpenMalaria. We simulated the Burkina Faso setting using OpenMalaria to see how well the distributions of parasite density and the contribution to the infectious reservoir could be captured. We assumed that the seasonality followed that reported in Burkina Faso48 with 30 infectious bites per person per year in the village of Laye and 300 in the village of Dapélogo, and we assumed that the coverage of treatment for malaria fevers was low. The assumptions about transmission intensity and case management were not crucial to predictions in this range. We weighted each individual's contribution to the infectious reservoir by their body surface area for both the observed data and predictions to account for differential biting rates. The simulated individuals have the same age- and village-distribution as the observed data. The asexual densities were measured using quanitative nucleic acid sequence based amplification (QT-NASBA) whereas OpenMalaria output densities were calibrated to microscopy using the standard method of counting parasite against leukocytes and converting them, assuming 8,000 leukoytes per microlitre49. The agreement between microscopy and QT-NASBA densities is not perfect; however, we assume for the purposes of this validation that they are similar because they are two imperfect methods to measure the density of infection.