Novel Analytical and Computational Approaches for Fusion and Analysis of Multi-Level and Multi-Scale Networks Data

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COVID-19 has claimed nearly 6.6 million lives and made many prosperous nations with well-run healthcare systems weaker. One important lesson learned from this pandemic is that non-pharmaceutical public health interventions are critical to suppress the epidemic curve at the beginning of the epidemic breakout. Mild interventions with minimal impact on normal life that are still capable to effectively reduce the epidemic spread are highly desirable. Such interventions as, for example, social distancing and case isolation are very effective strategies to suppress the pandemic. However, in the U.S., such mitigation measures rely on individuals' self-reporting mechanisms, which are time-consuming to collect and error-prone. The current project aims to develop more accurate and computationally efficient statistical tools to enhance efficiency of mitigation measures at a broader front. This project offers multiple unique opportunities for students to participate in cutting-edge and interdisciplinary research at the interface of statistics and bio-surveillance.In this project, by analyzing mobility data, the investigators aim to develop a suite of analytical and computational approaches that enables the early detection of the epidemic outbreak and accurate identification of infected individuals. Compared to self-reporting mechanisms, mobility data contains non-continuous individualized information and can be easily obtained from the public domain. Both the contact and mobility data can be naturally represented as networks (graphs), where the individual node is a location or a person (or a group of people), and its edges (connections) correspond to measures of contact or mobility between the nodes. The project will develop a series of novel statistical and machine learning methods for reconstructing pseudo-transmission time, identifying the infected individuals, detecting potential connections related to transmission pathways and infectious individuals using large-scale mobility data, as well as hypothesis testing for the differences between networks under various interventions. The results of the project will be applicable to a wide range of bio-surveillance tasks and will contribute to the wellbeing of our society as a whole.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.