Abstract

Substantial progress has been made globally to control malaria, however there is a growing need for innovative new tools to ensure continued progress. One approach is to harness genetic sequencing and accompanying methodological approaches as have been used in the control of other infectious diseases. However, to utilize these methodologies for malaria, we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment, which all impact the level of genetic diversity and relatedness of malaria parasites. We develop an individual-based transmission model to simulate malaria parasite genetics parameterized using estimated relationships between complexity of infection and age from five 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 characterize the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The model predicted malaria prevalence with a mean absolute error of 0.055. Different assumptions about the availability of sample metadata were considered, with the most accurate predictions of malaria prevalence made when the clinical status and age of sampled individuals is known. Parasite genetics may provide a novel surveillance tool for estimating the prevalence of malaria in areas in which prevalence surveys are not feasible. However, the findings presented here reinforce the need for patient metadata to be recorded and made available within all future attempts to use parasite genetics for surveillance.

中文翻译：

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使用传播模型评估疟疾遗传学的性能，以推断传播强度的变化。

 抽象的

全球在控制疟疾方面取得了实质性进展，但越来越需要创新的新工具来确保持续取得进展。一种方法是利用已用于控制其他传染病的基因测序和随附的方法。然而，为了利用这些方法治疗疟疾，我们首先需要扩展这些方法来捕获寄生虫、人类和媒介宿主以及环境之间复杂的相互作用，这些相互作用都会影响疟疾寄生虫的遗传多样性和相关性水平。我们开发了一种基于个体的传播模型来模拟疟疾寄生虫遗传学，该模型使用乌干达和肯尼亚五个地区感染复杂性与年龄之间的估计关系进行参数化。我们预测，共传播和重复感染对宿主内寄生虫遗传多样性的贡献相同，PCR 患病率为 11.5%，高于该值，重复感染占主导地位。最后，我们描述了寄生虫遗传学的六个指标的预测能力，用于检测传播强度的变化，然后将它们分组到整体统计模型中。该模型预测疟疾患病率的平均绝对误差为 0.055。考虑了关于样本元数据可用性的不同假设，当已知样本个体的临床状态和年龄时，可以对疟疾患病率做出最准确的预测。寄生虫遗传学可能提供一种新的监测工具，用于估计无法进行流行率调查的地区的疟疾流行率。然而，这里提出的研究结果强调了记录患者元数据的必要性，并在未来使用寄生虫遗传学进行监测的所有尝试中提供这些元数据。