RAPID: Data driven mathematical modeling of the shared epidemiology of Zika and other arboviruses across the globe

这一快速奖励将对寨卡病毒的传播能力和潜在的全球感染负担进行估计。迫切需要这些估计，但由于血液测试的不准确和感染报告的不足，很难获得这些估计。利用现有的血清样本和数学模型，该项目将填补关于i)寨卡病毒传播参数、ii)监测方法的一致性以及iii)寨卡病毒传播的全球风险的知识空白。这一方法还将获取有关其他病毒感染(例如基孔肯雅热和登革热)的信息以及未来传播的可能性。该项目的结果将与寨卡突发公共卫生事件相关，研究人员已经建立了机制，尽可能快速和广泛地分享有质量保证的中期和最终数据，包括与公共卫生和研究团体。该项目将使用样本血清学提供的数学模型来估计寨卡病毒在全球的传播潜力。目前，推断的用途仅限于监测报告和传统的血清学方法，后者是因为虫媒病毒之间的交叉反应。该项目将使用一种新的低成本、高通量的检测方法来测试寨卡病毒和其他虫媒病毒不同抗体的历史暴露情况。它将比较随时间推移感染不同虫媒病毒的社区中易感个体的比率。根据这些结果，将开发预测感染力的地统计学模型，并将验证寨卡病毒和其他虫媒病毒的风险图。这一知识将被用来描述全世界虫媒病毒疾病的共同流行病学。

This RAPID award will develop estimates of the transmission capability and potential global burden of infection of the Zika virus. These estimates are urgently needed but difficult to obtain because of inaccuracies in blood tests and under reporting of infections. Using existing serum samples together with mathematical modeling, the project will fill knowledge gaps about i) transmission parameters for Zika, ii) the consistency of surveillance approaches, and iii) the global risk of Zika transmission. This approach will also capture information about other viral infections (e.g., chikungunya and dengue) and the potential for future spread. Results from this project will be relevant to the Zika public health emergency, and the researchers have set in place mechanisms to share quality-assured interim and final data as rapidly and widely as possible, including with public health and research communities.This project will use mathematical models informed by sample serology to estimate the transmission potential for Zika across the globe. Currently, the utility of inference is limited with surveillance reports alone and traditional serological methods, the latter because of cross-reactivity between arboviruses. This project will use a new low-cost, high throughput assay to test for the historic exposure of different antibodies for Zika and other arboviruses. It will compare the rate susceptible individuals in communities that acquire different arboviruses over time. From these results, geostatistical models to predict the force of infection will be developed and risk maps for Zika and other arboviruses will be validated. This knowledge will be used to characterize the shared epidemiology of arboviral diseases around the world.