A climate and host community driven Ixodes population and infection dynamics model

项目摘要 莱姆病是一种新发传染病，每年约有30万人发病。 美国。这种疾病通过扁虱从动物储藏室传播给人类。的确有 目前还没有疫苗可用，因此莱姆病的预防和控制是最重要的 重要性。莱姆病控制方法(例如，以鹿或啮齿动物为靶标的杀螨剂，清除宿主， 以啮齿动物为目标的疫苗等)在研究环境中的小空间和时间尺度上是有效的。 但大规模的扁虱和莱姆病的控制一直不成功。其中一个原因是 控制策略的有效性取决于应用该策略的气候和扁虱宿主群落。一种工具 评估潜在的TICK策略是必要的。扁虱种群和感染动力学模型将 提供这样一个工具。该模型可以为对照预测受感染的扁虱密度的降低。 在特定环境背景下的战略。在这里，我们建议开发、参数化和验证 这样一种模式。这个模型将能够测试蚊虫控制策略；它将提供一个有价值的工具 用来控制蚊子。 为了使模型准确，需要用真实的 驱动扁虱种群和感染动态的机制。壁虱的数量很大程度上是由 气候和扁虱宿主群落。我们将构建模型并进行实证研究。 被设计成将其参数化。这种理论和经验主义的紧密结合是非常强大的。我们会 使用一种新的基于现场的气候操纵方法对模型进行参数化。我们将编码一个 在模型中现实地勾选主持人社区结构。以前的扁虱种群模型过于简单化 寄主群落结构的代表性，这限制了它们预测扁虱密度的能力。最后， 验证-根据独立的数据集测试模型的预测-是 然而，任何建模研究都没有得到以前的壁虱种群模型对壁虱密度的预测 已验证。在这里，我们将验证我们的模型对扁虱密度和感染的预测。 独立收集全国硬蜱密度和感染数据集。 最终的参数化和验证的模型将是一个强大的工具设计和 实施按蚊控制策略。这将对莱姆病的控制产生关键的人类健康益处 疾病和其他扁虱传播的疾病。我们将编制公开运行该模型的计算机代码 可用。对于扁虱传播疾病的研究人员来说，这将是一个很好的资源。

Project Summary Lyme disease is an emerging infectious disease with an estimated 300,000 cases every year in the United States. The disease is transmitted from its animal reservoirs to humans by ticks. There is currently no vaccine available, as such Lyme disease prevention and control are of the utmost importance. Lyme disease control methods (e.g., deer or rodent-targeted acaricide, host removal, rodent-targeted vaccines, etc.) are effective over small spatial and temporal scales in research settings. But largescale tick and Lyme disease control have been unsuccessful. One reason for this is that the efficacy of a control strategy depends on the climate and tick-host community where it is applied. A tool to evaluate potential tick strategies is needed. A tick population and infection dynamics model would provide such a tool. This model could predict the reduction in the infected tick density for a control strategy in a given environmental context. Here we propose to develop, parameterize, and validate such a model. This model will then be able to test tick control strategies; it will provide a valuable tool for tick control. For the model to be accurate it needs to be encoded with a realistic representation of the mechanisms which drive tick population and infection dynamics. Tick populations are largely driven by climate and the tick-host community. We will construct the model together with an empirical study designed to parameterize it. This tight coupling of theory and empiricism is very powerful. We will parameterize the model using a novel field-based, climate-manipulation method. We will encode a realistic tick host community structure into the model. Previous tick population models had a simplistic representation of host community structure, which limited their ability to predict tick density. Finally, validation — testing a model's prediction against an independent data set — is a necessary step for any modeling study, however previous tick population models' predictions of tick density have not been validated. Here we will validate our model's prediction of tick density and infection against an independently collected nation-wide tick density and infection data set. The final parameterized and validated model will be a powerful tool when designing and implementing tick control strategies. This will have critical human health benefits for the control of Lyme disease and other tick-borne diseases. We will make the computer code to run the model publicly available. This will be a great resource to tick-borne disease researchers.