Abstract
Many microparasites infect new hosts with specialized life stages, requiring a subset of the parasite population to forgo proliferation and develop into transmission forms. Transmission stage production influences infectivity, host exploitation, and the impact of medical interventions like drug treatment. Predicting how parasites will respond to public health efforts on both epidemiological and evolutionary timescales requires understanding transmission strategies. These strategies can rarely be observed directly and must typically be inferred from infection dynamics. Using malaria as a case study, we test previously described methods for inferring transmission stage investment against simulated data generated with a model of within-host infection dynamics, where the true transmission investment is known. We show that existing methods are inadequate and potentially very misleading. The key difficulty lies in separating transmission stages produced by different generations of parasites. We develop a new approach that performs much better on simulated data. Applying this approach to real data from mice infected with a single Plasmodium chabaudi strain, we estimate that transmission investment varies from zero to 20%, with evidence for variable investment over time in some hosts, but not others. These patterns suggest that, even in experimental infections where host genetics and other environmental factors are controlled, parasites may exhibit remarkably different patterns of transmission investment.
Highlights
Parasite life cycles involve both proliferation within-hosts and transmission to new hosts
We focus on the comparatively simple case of P. chabaudi to show that even modest gametocyte carryover can severely bias estimates of transmission investment
Even when the true level of transmission investment is fixed at 5%, the estimated value rises as parasite numbers increase, making it appear as though parasites are modulating their investment in response to changing environmental conditions
Summary
Parasite life cycles involve both proliferation within-hosts and transmission to new hosts. Information is needed on the range of strategies parasites can employ, what cues in the within-host environment (if any) trigger changes in allocation, and how quickly the parasite population can respond to perturbations, such as drug treatment of the host. None of this is attainable without robust methods to estimate transmission investment from time series data. We use simulated data—where the true pattern of transmission investment is known—to show that current methods for estimating allocation [3,4,5] can be seriously misleading, inferring complicated strategies where none exist. We develop a better inferential method by expanding recent regression methods [6] and apply this method to real data, revealing unexpected diversity in the transmission investment strategies of malaria parasites in a highly-controlled setting of rodent malaria infections
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