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首页> 外文期刊>PLoS Computational Biology >Elucidating relationships between P.falciparum prevalence and measures of genetic diversity with a combined genetic-epidemiological model of malaria
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Elucidating relationships between P.falciparum prevalence and measures of genetic diversity with a combined genetic-epidemiological model of malaria

机译：阐明P.Falciparum患病率与遗传多样性措施与疟疾结合遗传流行病学模型的关系

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There is an abundance of malaria genetic data being collected from the field, yet using these data to understand the drivers of regional epidemiology remains a challenge. A key issue is the lack of models that relate parasite genetic diversity to epidemiological parameters. Classical models in population genetics characterize changes in genetic diversity in relation to demographic parameters, but fail to account for the unique features of the malaria life cycle. In contrast, epidemiological models, such as the Ross-Macdonald model, capture malaria transmission dynamics but do not consider genetics. Here, we have developed an integrated model encompassing both parasite evolution and regional epidemiology. We achieve this by combining the Ross-Macdonald model with an intra-host continuous-time Moran model, thus explicitly representing the evolution of individual parasite genomes in a traditional epidemiological framework. Implemented as a stochastic simulation, we use the model to explore relationships between measures of parasite genetic diversity and parasite prevalence, a widely-used metric of transmission intensity. First, we explore how varying parasite prevalence influences genetic diversity at equilibrium. We find that multiple genetic diversity statistics are correlated with prevalence, but the strength of the relationships depends on whether variation in prevalence is driven by host- or vector-related factors. Next, we assess the responsiveness of a variety of statistics to malaria control interventions, finding that those related to mixed infections respond quickly (～months) whereas other statistics, such as nucleotide diversity, may take decades to respond. These findings provide insights into the opportunities and challenges associated with using genetic data to monitor malaria epidemiology.
机译：从场上收集丰富的疟疾遗传数据，但使用这些数据来了解区域流行病学的驱动程序仍然是一个挑战。关键问题是缺乏与流行病学参数相关的寄生虫遗传多样性的模型。人口遗传学中的古典模型表征了与人口统计参数相关的遗传多样性变化，但未能考虑疟疾生命周期的独特特征。相比之下，流行病学模型，如罗斯 - 麦克唐纳模型，捕获疟疾传输动态但不考虑遗传。在这里，我们开发了一种综合模型，包括寄生虫进化和区域流行病学。我们通过将Ross-MacDonald模型与主宿主连续时间莫兰模型组合来实现这一点，从而显着地表示传统流行病学框架中个体寄生虫基因组的演变。实施作为随机仿真，我们使用该模型探讨寄生虫遗传多样性和寄生虫普及措施之间的关系，广泛使用的传输强度。首先，我们探讨了寄生虫患病率如何影响均衡时的遗传多样性。我们发现多个遗传多样性统计数据与普遍性相关，但关系的强度取决于患有载体或矢量相关因素的患病率的变化。接下来，我们评估各种统计数据对疟疾控制干预的响应性，发现与混合感染有关的那些迅速（〜个月），而其他统计数据如核苷酸多样性，则可能需要几十年来回应。这些调查结果提供了与使用遗传数据相关的机会和挑战来监测疟疾流行病学的洞察力。

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