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In this paper, we present a discrete-time networked SEIR model using population flow, its derivation, and assumptions under which this model is well defined. We identify properties of the system’s equilibria, namely the healthy states. We show that the set of healthy states is asymptotically stable, and that the value of the equilibria becomes equal across all sub-populations as a result of the network flow model. Furthermore, we explore closed-loop feedback control of the system by limiting flow between sub-populations as a function of the current infected states. These results are illustrated via simulation based on flight traffic between major airports in the United States. We find that a flow restriction strategy combined with a vaccine roll-out significantly reduces the total number of infections over the course of an epidemic, given that the initial flow restriction response is not delayed. 

In this paper we present a deterministic discrete-time networked SEIR model that includes a number of transportation networks, and present assumptions under which it is well defined. We analyze the limiting behavior of the model and present necessary and sufficient conditions for estimating the spreading parameters from data. We illustrate these results via simulation and with real COVID-19 data from the Northeast United States, integrating transportation data into the results. 

Outdoor ambient acoustical environments may be predicted through supervised machine learning using geospatial features as inputs. However, collecting sufficient training data is an expensive process, particularly when attempting to improve the accuracy of models based on supervised learning methods over large, geospatially diverse regions. Unsupervised machine learning methods, such as K-Means clustering analysis, enable a statistical comparison between the geospatial diversity represented in the current training dataset versus the predictor locations. In this case, the geospatial features that represent the regions of western North Carolina and Utah have been simultaneously clustered to examine the common clusters between the two locations. Initial results show that most geospatial clusters group themselves according to a relatively small number of prominent geospatial features, and that Utah requires appreciably more clusters to represent its geospace. Additionally, the training dataset has a relatively low geospatial diversity because most of the current training data sites reside in a small number of clusters. This analysis informs a choice of new site locations for data acquisition that maximize the statistical similarity of the training and input datasets. 

Machine learning refers to a collection of computational techniques for identifying or learning patterns in data. Although existing techniques are most effective on large data sets, there is growing interest in applying methods on smaller ones. We consider the application of machine learning to predicting ambient sound levels in the contiguous United States from GIS data. The challenge is limited availability of training data from which to construct a model--data collection in this case is both cost and time expensive. This leads us to consider two questions: First, how to best validate a machine learning model with limited training data and two, given additional data can we measurably improve the accuracy of the model. We create an ensemble of models that perform equally well as measured by leave-one-out cross validation on our initial training set. However, these models give wildly different predictions for areas in the central region of the country. By collecting additional data in cropland areas in Utah, we were able to improve the predictions of our machine learning model to other, geographically similar regions of the country. *National Science Foundation Grant 1557998 Brigham Young University Physics and Astronomy