coiaf: Directly estimating complexity of infection with allele frequencies

In malaria, individuals are often infected with different parasite strains; the complexity of infection (COI) is defined as the number of genetically distinct parasite strains in an individual. Changes in the mean COI in a population have been shown to be informative of changes in transmission intensity with a number of probabilistic likelihood and Bayesian models now developed to estimate the COI. However, rapid, direct measures based on heterozygosity or FwS do not properly represent the COI. In this work, we present two new methods that use easily calculated measures to directly estimate the COI from allele frequency data. Using a simulation framework, we show that our methods are computationally efficient and comparably accurate to current methods in the literature. Through a sensitivity analysis, we characterize how the bias and accuracy of our two methods are impacted by the distribution of parasite densities and the assumed sequencing depth and number of sampled loci. We further estimate the COI globally from Plasmodium falciparum sequencing data using our developed methods and compare the results against the literature. We show significant differences in estimated COI globally between continents and a weak relationship between malaria prevalence and COI. Author summary Computational models, used in conjunction with rapidly advancing sequencing technologies, are increasingly being used to help inform surveillance efforts and understand the epidemiological dynamics of malaria. One such important metric, the complexity of infection (COI), indirectly quantifies the level of transmission. Existing “gold-standard” COI measures rely on complex probabilistic likelihood and Bayesian models. As an alternative, we have developed the statistics and software package coiaf, which features two rapid, direct measures to estimate of the number of genetically distinct parasite strains in an individual (the COI). Our methods were evaluated using simulated data and subsequently compared to current “state-of-the-art” methods, yielding comparable results. Lastly, we examined the distribution of the COI in several locations across the world, identifying significant differences in COI between continents. coiaf, therefore, provides a new, promising framework for rapidly characterizing polyclonal infections.