Leveraging environmental drivers to predict vector-borne disease transmission

艾琳·末底改 NIGMS R35 ESI Mira 摘要 利用环境驱动因素预测病媒传播疾病 媒介传播疾病是世界范围内日益紧迫的公共卫生危机。传统生物医学 仅靠疫苗和药物等方法不能可持续地控制媒介传播的疾病或预防 未来的崛起。更主动、更生态的方法，发现和破坏环境 媒介传播的驱动因素对于了解和可持续地控制疾病流行至关重要。 从气候和土地利用等生态驱动因素预测传染病动态很有吸引力，因为 这些驱动因素很容易观察到，而且往往是可以预测的，它们对疾病传播的影响是 有机械论假说的支持。然而，媒介传播的疾病，像其他生态系统一样，是 非线性、复杂和动态，使得预测在一个随机和不断变化的世界中具有挑战性。我的 研究用途引入了数量生态学、统计学、数学、计量经济学和 地理以及新可用的数据源，以了解和预测媒介传播疾病的动态 以应对全球变化。我们的初步工作表明，气候和土地利用是强大的 疾病传播的地理和季节模式的预测因子。我现在建议将这项工作扩展到 使用尖端定量技术和时间序列数据了解疾病动态。具体来说，我们 将研究气候、栖息地、行为和免疫如何相互作用来确定疾病动态 疟疾、寨卡病毒、登革热和其他媒介传播病原体的空间和时间，建立了证据组合 以及来自多个互补的量化方法的预测工具。这些措施包括增加试穿 时间序列数据的复杂动态模型，应用经验动态建模进行推断，而不是 假设与生态驱动因素的机械关系，并将计量经济面板分析应用于远程 用于评估疾病与人类土地之间双向因果关系的证据的遥感和地理数据 使用活动。 近几十年来，病媒传播的疾病和 技术和计算能力。作为回应，媒介传播疾病的建模研究正在迅速进行 加速，目标是改善前瞻性预测，从而为积极主动地提供机会 控制力。通过开发和测试新的理论，这个项目最终将使我们能够利用环境 病媒传播疾病的驱动因素，以了解复杂疾病动力学背后的机制， 并在变化的环境中预测未来的疾病风险。

Erin Mordecai NIGMS R35 ESI MIRA Summary Leveraging environmental drivers to predict vector-borne disease transmission Vector-borne diseases are an increasingly urgent public health crisis worldwide. Traditional biomedical approaches such as vaccines and drugs alone will not sustainably control vector-borne diseases or prevent future emergence. More proactive, ecological approaches that discover and disrupt the environmental drivers of vector transmission are critical for understanding and sustainably controlling disease epidemics. Predicting infectious disease dynamics from ecological drivers like climate and land use is appealing because these drivers are readily observable and often predictable, and their impacts on disease transmission are supported by mechanistic hypotheses. However, vector-borne diseases, like other ecological systems, are nonlinear, complex, and dynamic, making prediction challenging in a stochastic and changing world. My research uses brings in techniques from quantitative ecology, statistics, mathematics, econometrics, and geography as well as newly available data sources to understand and predict vector-borne disease dynamics in response to global change. Our preliminary work has shown that climate and land use are powerful predictors of geographical and seasonal patterns of disease transmission. I now propose to extend this work to understand disease dynamics using cutting edge quantitative techniques and time series data. Specifically, we will investigate how climate, habitat, behavior, and immunity interact to determine disease dynamics over space and time for malaria, Zika, dengue, and other vector-borne pathogens, building a portfolio of evidence and predictive tools from multiple complementary quantitative approaches. These include fitting increasingly sophisticated dynamic models to time series data, applying empirical dynamic modeling to infer, rather than assume, mechanistic relationships with ecological drivers, and applying econometric panel analysis to remotely sensed and geographic data to evaluate evidence for bidirectional causation between disease and human land use activities. Recent decades have witnessed both unprecedented expansions in both vector-borne disease and technological and computational capacity. In response, vector-borne disease modeling research is rapidly accelerating, with the goal of improving prospective prediction and thereby opening opportunities for proactive control. By developing and testing new theory, this project will finally allow us to leverage environmental drivers of vector-borne disease to understand the mechanisms underlying complex disease dynamics, and to predict future disease risk in changing environments.