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AI and Immunology Considerations in Pandemics and SARS-CoV-2 COVID-19
Published in Louis J. Catania, AI for Immunology, 2021
The first influenza pandemic of the 21st century occurred in 2009–2010 and was caused by an influenza A(H1N1) virus. This H1N1 pandemic was a reprise of the “Spanish flu” pandemic from 1918, but with far less devastating consequences, thanks to improved epidemiology and public health measures,. Suspected as a re-assortment of bird, swine, and human flu viruses, it was coined the “swine flu.”11 For the first time, a pandemic vaccine was developed, produced, and deployed in multiple countries during the first year of the pandemic. While most cases of pandemic H1N1 were mild, globally it is estimated that this 2009 pandemic caused between 100,000 and 400,000 deaths in the first year alone.12 Other prominent epidemics and pandemics that occurred in the early 21st century included Ebola, Lassa fever, Middle East respiratory syndrome coronavirus (MERS-CoV), Nipah and henipa virus diseases, Zika, and others.13
Analysis of user influence in Sina Microblog
Published in Lin Liu, Automotive, Mechanical and Electrical Engineering, 2017
Jun Wang, Zewen Cao, Peiteng Shi, Wensen Liu
Effective distance is proposed to describe the connection between two places in a Global Mobility Network (GMN) by Dirk Brockmann and Dirk Helbing (Brockmann D et al., 2013). They applied the measurement of effective distance to the study for spread of epidemic (2009 H1 N1 influenza pandemic and 2003 SARS epidemic) and approved the method effectively. In their research, they constructed the GMN with the data of worldwide air traffic which contains 4,069 airports with 25,453 direct connections. In the GMN, countries are treated as nodes of network while the flow between nodes represents number of passengers between countries. Then they built up a probability transition matrix P between nodes in the network, which is denoted as follows: P={pij}N×N;i,j∈{1,2,…,N}
Ebola and other emerging infectious diseases
Published in Saleem H. Ali, Kathryn Sturman, Nina Collins, Africa’s Mineral Fortune, 2018
Osman A. Dar, Francesca Viliani, Hisham Tariq, Emmeline Buckley, Abbas Omaar, Eloghene Otobo, David L. Heymann
According to the review, influenza pandemic risk generated the most collaborative preparedness efforts. Furthermore, while most mining companies mentioned in their annual sustainability reports that their projects had developed emergency response plans, these plans primarily focused on accidents and natural disasters. Although they included some preparedness for disease outbreak, the extent to which they considered outbreaks was often not clear from the annual sustainability reports. The review also found the main drivers for company policy changes were business continuity of their operations, duty of care toward their workers, and the social responsibility agenda.
Nonlinear Robust Adaptive Sliding Mode Control Strategy for Innate Immune Response to Influenza Virus
Published in IETE Journal of Research, 2022
Z. Abbasi, I. Zamani, S. H. Nosrati, A. H. Amiri Mehra, M. Shafieirad, A. Ibeas
An influenza pandemic happens when an influenza virus that had not formerly spread among humans and most do not have immunity against it appears and transmits among humans. These viruses may pull out, circulate and cause extensive outbreaks beyond the typical influenza season [1]. Therefore, infection by a pathogen is one of the main threats to any alive organism [2]. Different mathematical models have been proposed to provide a quantitative understanding of counteracting strategies in the course of influenza virus infection. The first mathematical model to describe influenza A virus (IAV) dynamics was developed in 1976 by Larson et al. [3]. The model includes seven compartments with five associated rate parameters and a new approach to studying the pathogenesis of infections. After thirty years, a work that described the IAV infection dynamics was introduced by Baccam et al. [4]. This work adopted a simple model of influenza A virus kinetics in humans. This model is very similar to previously presented models of the human immunodeficiency virus (HIV), hepatitis C virus (HCV), and hepatitis B virus (HBV) in the upper respiratory tracts of experimentally infected adults. Also, they used a more complex model consisting of the antiviral effects of interferon (IFN). Moreover, Boianelli et al. [5] have illustrated the developed mathematical model of IAV infection that consists of the cells in the eclipse phase in addition to infected and healthy cells and viruses and the methods utilised for parameter estimation.