Network Modeling and Analysis in Health Informatics and Bioinformatics最新文献

In this work, we developed an integrated simulation framework for pandemic prevention and mitigation of pandemics caused by airborne pathogens, incorporating three sub-models, namely the spatial model, the mobility model, and the propagation model, to create a realistic simulation environment for the evaluation of the effectiveness of different countermeasures on the epidemic dynamics. The spatial model converts images of real cities obtained from Google Maps into undirected weighted graphs that capture the spatial arrangement of the streets utilized next for the mobility of individuals. The mobility model implements a stochastic agent-based approach, developed to assign specific routes to individuals moving in the city, through the use of stochastic processes, utilizing the weights of the underlying graph to deploy shortest path algorithms. The propagation model implements both the epidemiological model and the physical substance of the transmission of an airborne pathogen (in our approach, we investigate the transmission parameters of SARS-CoV-2). The deployment of a set of countermeasures was investigated in reducing the spread of the pathogen, where, through a series of repetitive simulation experiments, we evaluated the effectiveness of each countermeasure in pandemic prevention.

The novel coronavirus SARS-Cov-2 is a pandemic condition and poses a massive menace to health. The governments of different countries and their various prohibitory steps to restrict the virus's expanse have changed individuals' communication processes. Due to physical and financial factors, the population's density is more likely to interact and spread the virus. We establish a mathematical model to present the spread of the COVID-19 in India and worldwide. By the simulation process, we find the infected cases, infected fatality rate, and recovery rate of the COVID-19. We validate the model by the rough set method. In the method, we obtain the accuracy for the infected case is 90.19%, an infection-fatality of COVID-19 is 94%, and the recovery is 85.57%, approximately the same as the actual situation reported WHO. This paper uses the generalized simulation process to predict the outbreak of COVID-19 for different continents. It gives the way of future trends of the COVID-19 outbreak till December 2021 and casts enlightenment about learning the drifts of the outbreak worldwide.

Cyber security encompasses a variety of financial, political, and social aspects with significant implications for the safety of individuals and organisations. Hospitals are among the least secure and most vulnerable organisations in terms of cybersecurity. Protecting medical records from cyberattacks is critical for protecting personal and financial records of those involved in medical institutions. Attack graphs, like in other systems, can be used to protect medical and hospital records from cyberattacks. In the current study, a total of 352 real-life cyberattacks on healthcare institutions using common vulnerability scoring system (CVSS) data were statistically examined to determine important trends and specifications in regard to those attacks. Following that, several machine learning techniques and an artificial neural network model were used to model industrial control systems (ICS) vulnerability data of those attacks. The average vulnerability score for attacks on healthcare IT systems was found to be very high. Moreover, this score was found to be higher in healthcare institutions which have experienced cyberattacks in the past and no mitigation actions were implemented. Using Python programming software, the most successful model that can be used in modelling cyberattacks on IT systems of healthcare institutions was found to be the K-nearest neighbours (KNN) algorithm. The model was then enhanced further and then it was tried to make predictions for future cyberattacks on IT systems of healthcare institutions. Results indicate that the overall score is critical indicating that medical records are, in general, at high risk and that there is a high risk of cyberattacks on medical records in healthcare institutions. It is recommended, therefore, that those institutions should take urgent precautionary measures to mitigate such a high risk of cyberattacks and to make them more secure, reliable, and robust.