A network SIRX model for the spreading of COVID-19

Stability analysis of a SAIR epidemic model on scale-free community networks

The presence of asymptomatic carriers, often unrecognized as infectious disease vectors, complicates epidemic management, particularly when inter-community migrations are involved. We introduced a SAIR (susceptible-asymptomatic-infected-recovered) infectious disease model within a network framework to explore the dynamics of disease transmission amid asymptomatic carriers. This model facilitated an in-depth analysis of outbreak control strategies in scenarios with active community migrations. Key contributions included determining the basic reproduction number, $ R_0 $, and analyzing two equilibrium states. Local asymptotic stability of the disease-free equilibrium is confirmed through characteristic equation analysis, while its global asymptotic stability is investigated using the decomposition theorem. Additionally, the global stability of the endemic equilibrium is established using the Lyapunov functional theory.

Complexity in the dengue spreading: A network analysis approach

In an increasingly interconnected society, preventing epidemics has become a major challenge. Numerous infectious diseases spread between individuals by a vector, creating bipartite networks of infection with the characteristics of complex networks. In the case of dengue, a mosquito-borne disease, these infection networks include a vector-the Aedes aegypti mosquito-which has expanded its endemic area due to climate change. In this scenario, innovative approaches are essential to help public agents in the fight against the disease. Using an agent-based model, we investigated the network morphology of a dengue endemic region considering four different serotypes and a small population. The degree, betweenness, and closeness distributions are evaluated for the bipartite networks, considering the interactions up to the second order for each serotype. We observed scale-free features and heavy tails in the degree distribution and betweenness and quantified the decay of the degree distribution with a q-Gaussian fit function. The simulation results indicate that the spread of dengue is primarily driven by human-to-human and human-to-mosquito interaction, reinforcing the importance of controlling the vector to prevent episodes of epidemic outbreaks.

Converting data into knowledge with RCA methodology improved for inverters fault analysis

In the last years, the knowledge management methodology increased the perspective and deeply analysis in the energy evaluation, with great emphasis in the training of the maintenance teams and early detection of failure modes; these inefficiencies detection is associated to patterns recognition with expert systems. Several energy brands, utilities, universities, and design companies investigated about this problem with limits in the integration between maintenance team knowledge and the degradation of the energy equipment. Therefore, our findings are a new approach of the root cause analysis (RCA) improved with the knowledge management perspective, associated to the failure mode analysis for 164 inverters in photo-voltaic solar plant by using twenty-one failures modes; by incorporate the graph theory called Erdös–Rényi graphs with a quantitative methodology and qualitative evaluation with the knowledge management method in the root cause analysis; the dataset evaluated has 120,561 signals associated to 3,014,025 patterns, during the period from 2018 to 2021 in a PV solar plant. In this new root cause analysis method, the knowledge management is analyzed as a complement for the solution for sudden failure modes and early degradation.