This work proposes an agent-based model to analyze the spread processes of the COVID-19 epidemics in open regions and based on hypothetical social scenarios of viral transmissibility. Differently from other previous models, we consider the environment to be a multi-region space in which the epidemic spreads according to the dynamics and the concentration of agents in such regions. This paper suggests that software agents can provide a more suitable model for individuals, and their features, thus showing the influence of civil society in the context of pandemic management. This is achieved by modeling an individual as an agent with a wide range of features (health condition, purchasing power, awareness, mobility, professional activity, age, and gender). The model supports the design of populations and interactions akin to real-life scenarios. Simulation results show that the proposed model can be applied in several ways to support decision-makers to better understand the epidemic spread and the actions that can be taken against the pandemic.

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This work presents a framework for correlating numerical and experimental mode shapes with low spatial resolution using the Local Correspondence of Modes and Modal Coordinates (LCMC). As a case study, an operational modal analysis of a catamaran was performed. Thirteen global natural frequencies and their respective modes were identified in the range between 1 Hz and 50 Hz, using only six sensors placed on its main deck. Thus, due to experimental modes' spatial incompleteness, this required its full numerical model to be assembled and reduced using the System Equivalent Reduction Expansion Process (SEREP) to its main deck. Consequently, the reduced numerical modal matrix was used to fill in the gaps in the sparse set of the experimental modes, creating the mixed modal matrix. Then, this matrix was correlated and expanded using LCMC to the degrees of freedom (DOFs) that have not been measured. As a result, the spatial resolution of the catamaran's experimental mode shapes was successfully increased from 6 to 174 DOFs. In general, the use of LCMC improved the Modal Assurance Criteria (MAC) of the correlated modes matrix compared to the MAC of the mixed modal matrix.

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