Estimating the extrinsic incubation period of malaria using a mechanistic model of sporogony.
Estimating the extrinsic incubation period of malaria using a mechanistic model of sporogony.
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用孢子生殖机制模型估计疟疾的外在潜伏期。
DOI：
10.1371/journal.pcbi.1008658
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
Lambert B
中科院分区：
文献类型：
--
作者：
Stopard IJ;Churcher TS;Lambert B
在产孢过程中，引起疟疾的寄生虫感染蚊子，繁殖并迁移到蚊子的唾液腺，在那里它们可以在下一次吸血时传播。孢子形成所需的时间，即外在潜伏期（EIP），是疟疾传播强度的一个重要决定因素。EIP通常估计为给定百分位数x的受感染蚊子发育唾液腺孢子体（感染性寄生虫生命阶段）的时间，用EIPx表示。然而，影响观察到的孢子虫流行率的机制有很多，包括人-蚊传播概率，以及不同感染状态下蚊子死亡率的可能差异。为了解释这些不同的机制，我们提出了一个机制数学模型，该模型明确地模拟了寄生虫，蚊子和观测尺度上的关键过程。将该模型拟合到实验数据中，我们发现EIP的变化比之前认为的要大：我们估计EIP10和EIP90（27°C）之间的范围为4.5天，而使用现有的统计方法为0.9天。这种模式在数据集中包含的研究温度范围内保持不变。温度从21℃升高到34℃，EIP50从16.1天降低到8.8天。我们的工作强调了孢子囊的机制建模对于以下方面的重要性：(1)提高对不同环境条件或疾病控制方案下疟疾传播的估计；(2)评估针对疟原虫蚊子生命阶段的新干预措施。按蚊在吸食传染性宿主的血液时感染了引起疟疾的寄生虫。然后，寄生虫通过几个生命阶段进行繁殖，从蚊子的肠道开始，到唾液腺结束，在那里，新形成的传染性寄生虫可以在蚊子下一次吸血时传播给另一个宿主。这种蚊子变得具有传染性的延迟，被称为外在潜伏期（EIP），相对于蚊子的预期寿命来说是很长的。因此，EIP对于确定蚊子是否能够传播疟疾非常重要。EIP通常是通过将统计模型拟合到来自大量蚊子解剖的寄生虫数据来估计的。寄生虫、蚊子和环境之间存在的发育时间和寄生虫数量的巨大差异意味着很难估计EIP。在这里，我们引入了一个模拟寄生虫蚊子生命阶段种群动态的数学模型，该模型模拟了生物学的关键特征。我们证明模型的参数是可以拟合的，因此它的预测与实验观测相一致。我们的工作是朝着蚊内寄生虫动态的现实模型迈出的一步，该模型可用于帮助理解疟疾传播的异质性。
During sporogony, malaria-causing parasites infect a mosquito, reproduce and migrate to the mosquito salivary glands where they can be transmitted the next time blood feeding occurs. The time required for sporogony, known as the extrinsic incubation period (EIP), is an important determinant of malaria transmission intensity. The EIP is typically estimated as the time for a given percentile, x, of infected mosquitoes to develop salivary gland sporozoites (the infectious parasite life stage), which is denoted by EIPx. Many mechanisms, however, affect the observed sporozoite prevalence including the human-to-mosquito transmission probability and possibly differences in mosquito mortality according to infection status. To account for these various mechanisms, we present a mechanistic mathematical model, which explicitly models key processes at the parasite, mosquito and observational scales. Fitting this model to experimental data, we find greater variation in the EIP than previously thought: we estimated the range between EIP10 and EIP90 (at 27°C) as 4.5 days compared to 0.9 days using existing statistical methods. This pattern holds over the range of study temperatures included in the dataset. Increasing temperature from 21°C to 34°C decreased the EIP50 from 16.1 to 8.8 days. Our work highlights the importance of mechanistic modelling of sporogony to (1) improve estimates of malaria transmission under different environmental conditions or disease control programs and (2) evaluate novel interventions that target the mosquito life stages of the parasite. Anopheles mosquitoes become infected with malaria-causing parasites when blood feeding on an infectious host. The parasites then reproduce via a number of life stages, which begin in the mosquito gut and end in the salivary glands, where the newly formed infectious parasites can be transmitted to another host the next time a mosquito blood feeds. This delay in the mosquito becoming infectious, known as the extrinsic incubation period (EIP), is long relative to mosquito life expectancy. Consequently, the EIP is important in determining whether a mosquito is able to transmit malaria. The EIP is typically estimated by fitting a statistical model to parasite data from the dissection of numerous mosquitoes. The large variability in development times and parasite numbers that exists between parasites, mosquitoes and environments means that estimating the EIP is difficult. Here, we introduce a mathematical model of the population dynamics of the mosquito life stages of the parasite, which mimics key characteristics of the biology. We show that the model’s parameters can be fit so that its predictions correspond with experimental observations. Our work is a step towards a realistic model of within-mosquito parasite dynamics, which can be applied to help understand heterogeneity in malaria transmission.
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