Leveraging environmental drivers to predict vector-borne disease transmission

艾琳·莫迪凯 NIGMS R35 总结 利用环境驱动因素预测病媒传播疾病的传播 病媒传播疾病是全世界日益紧迫的公共卫生危机。传统生物医学 单靠疫苗和药物等方法无法可持续地控制病媒传播的疾病或预防 未来的崛起。更积极主动的生态方法，发现和破坏环境 病媒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，而且往往是可预测的，它们对疾病传播的影响是 由机械假说支持。然而，病媒传播的疾病与其他生态系统一样， 非线性、复杂性和动态性，使得预测在随机和变化的世界中具有挑战性。我 研究使用的技术来自定量生态学，统计学，数学，计量经济学， 地理以及新的可用数据来源，以了解和预测病媒传播疾病的动态 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 预测疾病传播的地理和季节模式。我现在建议把这项工作扩展到 利用最先进的定量技术和时间序列数据了解疾病动态。我们特别 将研究气候，栖息地，行为和免疫力如何相互作用，以确定疾病动态， 疟疾、寨卡病毒、登革热和其他病媒传播病原体的空间和时间，建立证据组合 和多种互补定量方法的预测工具。其中包括越来越多地适应 复杂的动态模型，以时间序列数据，应用经验动态模型来推断，而不是 假设，与生态驱动因素的机械关系，并应用计量经济学面板分析， 传感和地理数据，以评估疾病和人类土地之间双向因果关系的证据 使用活动。 近几十年来，媒介传播疾病和 技术和计算能力。作为回应，媒介传播疾病建模研究正在迅速 加速，目标是改善前瞻性预测，从而为积极主动地 控制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 媒介传播疾病的驱动因素，以了解复杂疾病动态的潜在机制， 并预测在不断变化的环境中未来的疾病风险。

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.