The effect of ongoing exposure dynamics in dose response relationships.
The effect of ongoing exposure dynamics in dose response relationships.
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DOI：
10.1371/journal.pcbi.1000399
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发表时间：
2009-06
影响因子：
4.3
通讯作者：
Koopman JS
中科院分区：
文献类型：
--
作者：
Pujol JM;Eisenberg JE;Haas CN;Koopman JS
将感染性表征为病原体剂量的函数是微生物风险评估的组成部分。剂量反应实验通常一次给受试者服用剂量。所得数据的现象学模型，如指数模型和β-泊松模型，忽略了剂量定时，并假设每种病原体的独立风险。然而，真实的世界暴露于病原体是一系列离散事件，其中同时或先前的病原体到达影响免疫效应物接合并杀死新到达的病原体的能力。我们在感染建立之前的一段时间内模拟免疫效应子和病原体的相互作用，以捕获产生剂量时序效应的动态。模型分析显示，暴露累积的时间与感染风险之间存在反比关系。因此，来自一次性剂量实验的数据将高估真实的世界暴露的每种病原体感染风险。例如，将我们的模型拟合到一次性给药数据显示，313种隐孢子虫病原体的风险为0.66。当时间暴露窗口增加100倍时，使用我们的模型拟合到一次性剂量数据的相同参数，感染风险降低到0.09。确认这一风险预测需要来自不同时间给予剂量的实验数据。我们的模型表明，剂量定时可以显着改变空气传播与污染物传播的病原体所产生的风险。我们建立了人体内病原体暴露和感染起飞的时间模式之间的关系模型。由于不同的传播途径（例如，空气传播途径与地面传播途径）可能导致不同的接触时间模式，这一模式有助于更好地比较通过这些不同途径从一个人传播到另一个人的风险。以前的模型假设，无论病原体是一次接种还是在一天内接种，感染的风险都是相同的。相比之下，我们的模型捕捉了一种病原体如何影响免疫力的潜力，以阻止同时或随后到达的颗粒引发感染。由于空气传播和表面传播的病原体到达的时间模式不同，我们的模型表明，每种空气传播的病原体比每种表面传播的病原体的风险更小。不幸的是，目前还没有完全符合我们模型的数据。因此，必须进行新的实验，在不同的时间窗口给予剂量。
Characterizing infectivity as a function of pathogen dose is integral to microbial risk assessment. Dose-response experiments usually administer doses to subjects at one time. Phenomenological models of the resulting data, such as the exponential and the Beta-Poisson models, ignore dose timing and assume independent risks from each pathogen. Real world exposure to pathogens, however, is a sequence of discrete events where concurrent or prior pathogen arrival affects the capacity of immune effectors to engage and kill newly arriving pathogens. We model immune effector and pathogen interactions during the period before infection becomes established in order to capture the dynamics generating dose timing effects. Model analysis reveals an inverse relationship between the time over which exposures accumulate and the risk of infection. Data from one time dose experiments will thus overestimate per pathogen infection risks of real world exposures. For instance, fitting our model to one time dosing data reveals a risk of 0.66 from 313 Cryptosporidium parvum pathogens. When the temporal exposure window is increased 100-fold using the same parameters fitted by our model to the one time dose data, the risk of infection is reduced to 0.09. Confirmation of this risk prediction requires data from experiments administering doses with different timings. Our model demonstrates that dose timing could markedly alter the risks generated by airborne versus fomite transmitted pathogens. We model the relationship between the temporal patterns of pathogen exposure and infection take off within people. Since different routes of transmission (e.g., airborne versus surface transfer routes) may result in different temporal patterns of exposure, this model helps to better compare the risks of transmission from one person to another through these different routes. Previous models assumed that the risk of infection is the same whether pathogens are inoculated all at once or over one day. Our model, in contrast, captures how one pathogen affects the potential of immunity to keep concurrently or subsequently arriving particles from initiating an infection. Since the pattern of timing of airborne and surface spread pathogen arrivals differ, our model shows that each airborne pathogen could carry less risk than each surface transmitted pathogen. Unfortunately, data to fully fit our model are not currently available. Therefore new experiments will have to be conducted where doses are given across different temporal windows.
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影响因子：
5
作者：
KOOPMAN, JS;MONTO, AS
通讯作者：
MONTO, AS
影响因子：
4.3
作者：
Tuckwell, Henry C.;Shipman, Patrick D.;Perelson, Alan S.
通讯作者：
Perelson, Alan S.
影响因子：
5
作者：
Eisenberg, JNS;Lei, XD;Colford, JM
通讯作者：
Colford, JM
影响因子：
158.5
作者：
DUPONT, HL;CHAPPELL, CL;JAKUBOWSKI, W
通讯作者：
JAKUBOWSKI, W
影响因子：
3.7
作者：
Ellenberger, Dennis;Otten, Ronald A.;Butera, Salvatore
通讯作者：
Butera, Salvatore