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ItemUSING WIFI MOBILITY DATA FOR MODELING COVID-19 ON UNIVERSITY CAMPUSES(Georgia Institute of Technology, 2021-05-05) Xie, JiajiaInfectious diseases, like mumps, flu, or measles, can cause devastating impacts on universities. To protect the community's health, schools must learn how to operate during epidemics. In the light of Covid-19, for instance, the universities in the U.S. have struggled to bring students, staff, and faculties back to campuses. On the one hand, these schools are often hotspots for outbreaks. On the other hand, a long-term local lockdown will likely incur losses of financial income for the school and the local businesses due to diminishing enrollment and limited visits to campuses and their surrounding neighborhoods To meet the reopening goal, the schools must assess the ongoing epidemic of Covid-19 on campus and design operational plans with more robust and accurate information than the data provided by other local agencies as support. Frequently asked questions from the perspective of campus officials are: How can we predict potential outcomes of disease spread? How can we evaluate strategies to control the epidemic? Which groups of individuals and locations are particularly vulnerable to Covid-19? How can we prioritize the testing program among individuals active on campus? Answering those questions typically involves disease modeling since models help us abstract the disease dynamics and reason more about the mechanism of disease transmissions among the community. This thesis targets several natural and fundamental problems for universities during the Covid-19 pandemic using human mobility data. We propose using the on-campus WiFi infrastructure to understand human mobility and approximate contact networks among individuals on campus. When an individual accesses the WiFi on campus, their device sends a request to a WiFi access point which creates a record that the device was connected to the WiFi network. From these logs, we can determine when and for how long that individual was connected to the WiFi through a particular access point and infer the location of that individual to the level of a room on campus. More formally, the logs give us a bipartite network between users and WiFi access points across different time-stamps, defined as WiFi Mobility data. Each connection of a user to a log at any time will be recorded as an edge in the bipartite network. Using a projection of this bipartite network, we can infer which individuals come into close proximity of each other on campus. Each connection of a user to a log at any time will be recorded as an edge in the network. We construct and validate a network-based simulation model of Covid-19 on university campuses using Wifi mobility data to approximate the contact network among individuals. Then, we design and evaluate two novel methods for improving decision-making powered by the WiFi mobility data with the model constructed. The first method outputs a more granular and localized closure policy, causing more effective disease intervention outcomes but less burdensome to individuals and schools. The second can discover likely chains of transmission among individuals and missing infections on campus given the current testing report data.
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ItemA METHODOLOGY FOR THE PREDICTION AND ANALYSIS OF PRECURSORS TO FLIGHT ADVERSE EVENTS(Georgia Institute of Technology, 2021-05-04) Bleu-Laine, Marc-HenriAir transportation is known to be the safest mean of transportation nowadays. The drastic improvements in aviation safety since its gain in popularity are undeniably a factor in the industry's growth over the last several decades. This growth brought social and economic benefits throughout the world and was expected to keep its momentum pre-COVID-19. Stakeholders such as the National Aeronautics and Space Administration (NASA), the Federal Aviation Administration (FAA), the National Transportation Safety Board (NTSB), aircraft manufactures, and airlines have developed systems, techniques, and technologies that are to thank for today's overall safety improvements and the reduction of accidents. The industry's maintained growth is welcomed, but current safety performances have been observed to stagnate instead of declining. With safety initiatives such as the Flight Operational Quality Assurance (FOQA) program and the growing number of aviation data, many of the previous techniques used to understand the causes of accidents are not scalable. These reasons led to the development of novel methods leveraging advanced analytical tools such as machine learning and deep learning. However, current use cases have focused mainly on anomaly detection and system health monitoring, which does not bring enough reaction time to deal with an imminent event. This research proposes the improvement of aviation safety through precursor mining. Precursors are defined as events that are highly correlated to the adverse event that they precede. Therefore, they provide predictive capabilities and can be used to explain pre-defined events. This thesis uses publicly available flight data to 1) develop a novel deep learning method to identify and rank precursors of multiple adverse events, 2) use unsupervised learning algorithms to group flights based on their precursors to identify potential causes for these events at a fleet-level, and finally 3) detect novelty to ensure that the developed precursor models operate within their limits and that new non pre-defined adverse events could be detected.