Challenges in forming inferences from limited data: a case study of malaria parasite maturation

Inferring biological processes from population dynamics is a common challenge in ecology, particularly when faced with incomplete data. This challenge extends to inferring parasite traits from within-host infection dynamics. We focus on rodent malaria infections (Plasmodium berghei), a system for which previous work inferred an immune-mediated extension in the length of the parasite development cycle within red blood cells. By developing a system of delay-differential equations to describe within-host infection dynamics and simulating data, we demonstrate the potential to obtain biased estimates of parasite (and host) traits when key biological processes are not considered. Despite generating infection dynamics using a fixed parasite developmental cycle length, we find that known sources of measurement bias in parasite stage and abundance data can affect estimates of parasite developmental duration, with stage misclassification driving inferences about extended cycle length. We discuss alternative protocols and statistical methods that can mitigate such misestimation.

Automated summary

Inferring biological processes from population dynamics is a challenge in ecology, especially when faced with incomplete data. In this study on rodent malaria infections, researchers found that measurement bias can affect estimates of parasite developmental duration, leading to inferences about extended cycle length. Alternative protocols and statistical methods are discussed to mitigate such misestimation.