Improved Detectability of Plasmodium falciparum Clones with Repeated Sampling in Incident and Chronic Infections in Burkina Faso

We evaluated the detectability of Plasmodium falciparum clones when assessed on 3 consecutive days in incident and chronic infections in naturally exposed children living in an area of intense malaria transmission in Burkina Faso. The median number of clones by merozoite surface protein 2 (MSP2) genotyping was 3 (interquartile range [IQR] 2–5) in incident infections compared with 6 (IQR 4–8) in chronic infections (P < 0.0001). When all clones detected on days 1-3 were considered as true complexity of infection, sampling on day 1 detected only 69.4% (109/157) or 68.3% (228/334) of all clones in incident and chronic infections, respectively. Our findings demonstrate that a large proportion of clones are missed by single time-point sampling. In addition, because of the high complexity of infection early in incident infections, our data suggest many infections may be caused by genetically complex inocula.

Evaluating the performance of malaria genomics for inferring changes in transmission intensity using transmission modelling

Advances in genetic sequencing and accompanying methodological approaches have resulted in pathogen genetics being used in the control of infectious diseases. To utilise these methodologies for malaria we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment. Here we develop an individual-based transmission model to simulate malaria parasite genetics parameterised using estimated relationships between complexity of infection and age from 5 regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterise the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The best performing model successfully predicted malaria prevalence with mean absolute error of 0.055, suggesting genetic tools could be used for monitoring the impact of malaria interventions.