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The use of network models to study the spread of infectious diseases is gaining increasing interests. They allow the flexibility to represent epidemic systems as networks of components with complex and interconnected structures. However, most of previous studies are based on networks of individuals as nodes and their social relationships (e.g., friendship, workplace connections) as links during the virus spread process. Notably, the transmission and spread of infectious viruses are more pertinent to human dynamics (e.g., their movements and interactions with others) in the spatial environment. This paper presents a novel network-based simulation model of human traffic and virus spread in community networks. We represent spatial points of interests (POI) as nodes where human subjects interact and perform activities, while edges connect these POIs to form a community network. Specifically, we derive the spatial network from the geographical information systems (GIS) data to provide a detailed representation of the underlying community network, on which human subjects perform activities and form traffics that impact the process of virus transmission and spread. The proposed framework is evaluated and validated in a community of university campus. Experimental results showed that the proposed simulation model is capable of describing interactive human activities at an individual level, as well as capturing the spread dynamics of infectious diseases. This framework can be extended to a wide variety of infectious diseases and shows strong potentials to aid the design of intervention policies for epidemic control. 

Since the pandemic of COVID-19 began in January 2020, the world has witnessed drastic social-economic changes. To harness the virus spread, several studies have been done to study contributing factors that are pertinent to COVID-19 transmission risks. However, little has been done to investigate how human activities on the spatial network are correlated to the virus transmission and spread. This paper performs a statistical analysis to examine interrelationships between spatial network characteristics and cumulative cases of COVID-19 in US counties. Specifically, both county-level transportation profiles (e.g., the total number of commute workers, route miles of freight railroad) and road network characteristics of US counties are considered. Then, the lasso regression model is utilized to identify a sparse set of significant variables that are sensitive to the response variable of COVID-19 cases. Finally, the fixed-effect model is built to capture the relationship between the selected set of predictors and the response variable. This work helps identify and determine salient features from spatial network characteristics and transportation profiles, thereby improving the understanding of COVID-19 spread dynamics. These significant variables can also be utilized to develop simulation models for the prediction of real-time positions of virus spread and the optimization of intervention strategies. 

The COVID-19 preparedness plans by the Centers for Disease Control and Prevention strongly underscores the need for efficient and effective testing strategies. This, in turn, calls upon the design and development of statistical sampling and testing of COVID-19 strategies. However, the evaluation of operational details requires a detailed representation of human behaviors in epidemic simulation models. Traditional epidemic simulations are mainly based upon system dynamic models, which use differential equations to study macro-level and aggregated behaviors of population subgroups. As such, individual behaviors (e.g., personal protection, commute conditions, social patterns) can’t be adequately modeled and tracked for the evaluation of health policies and action strategies. Therefore, this paper presents a network-based simulation model to optimize COVID-19 testing strategies for effective identifications of virus carriers in a spatial area. Specifically, we design a data-driven risk scoring system for statistical sampling and testing of COVID-19. This system collects real-time data from simulated networked behaviors of individuals in the spatial network to support decision-making during the virus spread process. Experimental results showed that this framework has superior performance in optimizing COVID-19 testing decisions and effectively identifying virus carriers from the population.