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. I. INTRODUCTIONGlobal interconnectivity has proven to be a key factor in the propagation of infectious diseases [1], [2]. Most recently, we have seen evidence of such connectivity through the rapid spread of the COVID-19 pandemic, which propagated from its origin in Wuhan, China to every major population center globally in a matter of weeks [3]. Given the implications of global population flow on disease spread, it becomes important to accurately model this flow, as reliable modeling is an essential step to developing effective and efficient mitigation strategies. Various infection models have been proposed based on characteristics of individual pathogens and studied in the literature, including susceptible-infected-susceptible (SIS), susceptible-infected-removed (SIR), and susceptible-infected-removed-susceptible (SIRS) [4], [5]. For this paper, we consider the recent COVID-19 pandemic as a motivating case for the model selection and construction. Due to the delay in onset of COVID-19 symptoms [6]-[9] and large asymptomatic populations estimated between 17 − 81% [10]-[13], we choose the susceptible-exposed-infected-removed (SEIR) model as the foundation of our model development. Previous work involving the incorporation of population flows in epidemic process models include analysis of a networked SIS model with flows [14] as well as using a networked SIR model with flows to predict arrival times for various epidemics using global flight data [15], where both models are developed in continuous time. This paper uses similar derivation techniques to define our discrete-time epidemic model. However, we contribute to the development of such models by including the exposed state in our model formulation, as well as provide analysis of the discrete time dynamics. While other work has considered capturing the effect of transportation on the spread of COVID-19 using the SEIR model [16], the key distinction in this work is that infection propagation over the network is modeled by the relocation of infected individuals
This paper describes using both supervised and unsupervised machine learning (ML) methods to improve automatic classification of crowd responses to events at collegiate basketball games. This work builds on recent investigations by the research team where the two ML approaches were treated separately. In one case, crowd response events (cheers, applause, etc.) were manually labeled, and then, a subset of the labeled events were used as a training set for supervised-ML event classification. In the other, (unsupervised) k-means clustering was used to divide a game’s one-twelfth octave spectrogram into six distinct clusters. A comparison of the two approaches shows that the manually labeled crowd responses are grouped into only one or two of the six unsupervised clusters. This paper describes how the supervised ML labels guide improvements to the k-means clustering analysis, such as determining which additional audio features are required as inputs and how both approaches can be used in tandem to improve automated classification of crowd noise at basketball games.
Ambient soundscape data are a critical component of numerous applications such as evaluating the environmental noise impacts of proposed activities and studying the physical and psychological impacts of a person’s acoustic environment. The current approach to characterize ambient soundscapes requires intensive field measurement programs. A soundscape prediction tool capable of modeling the soundscape over large spatial regions and timeframes is needed to provide an alternative to intensive field measurements. To address this need, a machine learning based soundscape model trained with acoustic data and geospatial layers has been developed. The model’s capability to generate A and flat-weighted exceedance levels across space, time (hourly, daytime, and nighttime), and frequency (one-third octave bands) will be demonstrated. [Work funded by an Army SBIR.]
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