Peters Madeline A E, Greischar Megan A, Mideo Nicole
Department of Ecology and Evolutionary Biology, The University of Toronto, Toronto Ontario, Canada.
Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA.
J R Soc Interface. 2021 Apr;18(177):20210065. doi: 10.1098/rsif.2021.0065. Epub 2021 Apr 28.
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 (), 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.
从种群动态中推断生物过程是生态学中的一个常见挑战，尤其是在面对不完整数据时。这一挑战延伸到从宿主内感染动态中推断寄生虫特征。我们专注于啮齿动物疟疾感染（），此前的研究推断该系统中存在免疫介导的红细胞内寄生虫发育周期延长现象。通过建立一个延迟微分方程系统来描述宿主内感染动态并模拟数据，我们证明了在不考虑关键生物过程时，有可能获得对寄生虫（和宿主）特征的有偏差估计。尽管使用固定的寄生虫发育周期长度来生成感染动态，但我们发现寄生虫阶段和丰度数据中已知的测量偏差来源会影响对寄生虫发育持续时间的估计，阶段错误分类会导致关于延长周期长度的推断。我们讨论了可以减轻这种错误估计的替代方案和统计方法。
