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Found 15,827 Documents (Results 1–100)

On first passage times in discrete skeletons and uniformized versions of a continuous-time Markov chain. (English) Zbl 1497.60103

Balakrishnan, Narayanaswamy (ed.) et al., Trends in mathematical, information and data sciences. A tribute to Leandro Pardo. Based on the presentations at the symposium on information theory with applications to statistical inference, Madrid, Spain, December 2, 2019. Cham: Springer. Stud. Syst. Decis. Control 445, 29-37 (2023).
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COVID-19 and optimal lockdown strategies: the effect of new and more virulent strains. (English) Zbl 07619978

del Carmen Boado-Penas, María (ed.) et al., Pandemics: insurance and social protection. Cham: Springer. Springer Actuar., 163-190 (2022).
MSC:  92D30 91B62
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A quantum field formulation for a pandemic propagation. (English) Zbl 07616594

Lobo Marques, Joao Alexandre (ed.) et al., Epidemic analytics for decision supports in COVID19 crisis. Cham: Springer. 141-158 (2022).
MSC:  92D30
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The application of supervised and unsupervised computational predictive models to simulate the COVID19 pandemic. (English) Zbl 07616593

Lobo Marques, Joao Alexandre (ed.) et al., Epidemic analytics for decision supports in COVID19 crisis. Cham: Springer. 103-139 (2022).
MSC:  92D30
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Probabilistic forecasting model for the COVID-19 pandemic based on the composite Monte Carlo model integrated with deep learning and fuzzy system. (English) Zbl 07616592

Lobo Marques, Joao Alexandre (ed.) et al., Epidemic analytics for decision supports in COVID19 crisis. Cham: Springer. 83-102 (2022).
MSC:  92D30
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The comparison of different linear and nonlinear models using preliminary data to efficiently analyze the COVID-19 outbreak. (English) Zbl 07616591

Lobo Marques, Joao Alexandre (ed.) et al., Epidemic analytics for decision supports in COVID19 crisis. Cham: Springer. 65-81 (2022).
MSC:  92D30
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Analysis of the COVID19 pandemic behaviour based on the compartmental SEAIRD and adaptive SVEAIRD epidemiologic models. (English) Zbl 07616590

Lobo Marques, Joao Alexandre (ed.) et al., Epidemic analytics for decision supports in COVID19 crisis. Cham: Springer. 17-64 (2022).
MSC:  92D30
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Research and technology development achievements during the COVID-19 pandemic – an overview. (English) Zbl 07616589

Lobo Marques, Joao Alexandre (ed.) et al., Epidemic analytics for decision supports in COVID19 crisis. Cham: Springer. 1-15 (2022).
MSC:  92D30
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Multiscale derivation of a time-dependent SEIRD reaction-diffusion system for COVID-19. (English) Zbl 07615916

Bellomo, Nicola (ed.) et al., Predicting pandemics in a globally connected world. Volume 1. Toward a multiscale, multidisciplinary framework through modeling and simulation. Cham: Birkhäuser. Model. Simul. Sci. Eng. Technol., 285-306 (2022).
MSC:  92D30
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A 2D kinetic model for crowd dynamics with disease contagion. (English) Zbl 07615915

Bellomo, Nicola (ed.) et al., Predicting pandemics in a globally connected world. Volume 1. Toward a multiscale, multidisciplinary framework through modeling and simulation. Cham: Birkhäuser. Model. Simul. Sci. Eng. Technol., 265-284 (2022).
MSC:  92D30
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Transmission dynamics and quarantine control of COVID-19 in cluster community. (English) Zbl 07615914

Bellomo, Nicola (ed.) et al., Predicting pandemics in a globally connected world. Volume 1. Toward a multiscale, multidisciplinary framework through modeling and simulation. Cham: Birkhäuser. Model. Simul. Sci. Eng. Technol., 233-264 (2022).
MSC:  92D30
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Productivity in times of Covid-19: an agent-based model approach. (English) Zbl 07615913

Bellomo, Nicola (ed.) et al., Predicting pandemics in a globally connected world. Volume 1. Toward a multiscale, multidisciplinary framework through modeling and simulation. Cham: Birkhäuser. Model. Simul. Sci. Eng. Technol., 213-231 (2022).
MSC:  92D30
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Multiscale aspects of virus dynamics. (English) Zbl 07615912

Bellomo, Nicola (ed.) et al., Predicting pandemics in a globally connected world. Volume 1. Toward a multiscale, multidisciplinary framework through modeling and simulation. Cham: Birkhäuser. Model. Simul. Sci. Eng. Technol., 183-212 (2022).
MSC:  92D30
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A novel point process model for COVID-19: multivariate recursive Hawkes process. (English) Zbl 07615911

Bellomo, Nicola (ed.) et al., Predicting pandemics in a globally connected world. Volume 1. Toward a multiscale, multidisciplinary framework through modeling and simulation. Cham: Birkhäuser. Model. Simul. Sci. Eng. Technol., 141-182 (2022).
MSC:  92D30
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The COVID-19 pandemic evolution in Hawai‘i and New Jersey: a lesson on infection transmissibility and the role of human behavior. (English) Zbl 07615910

Bellomo, Nicola (ed.) et al., Predicting pandemics in a globally connected world. Volume 1. Toward a multiscale, multidisciplinary framework through modeling and simulation. Cham: Birkhäuser. Model. Simul. Sci. Eng. Technol., 109-140 (2022).
MSC:  92D30
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Kinetic modelling of epidemic dynamics: social contacts, control with uncertain data, and multiscale spatial dynamics. (English) Zbl 07615909

Bellomo, Nicola (ed.) et al., Predicting pandemics in a globally connected world. Volume 1. Toward a multiscale, multidisciplinary framework through modeling and simulation. Cham: Birkhäuser. Model. Simul. Sci. Eng. Technol., 43-108 (2022).
MSC:  92D30
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Understanding COVID-19 epidemics: a multi-scale modeling approach. (English) Zbl 07615908

Bellomo, Nicola (ed.) et al., Predicting pandemics in a globally connected world. Volume 1. Toward a multiscale, multidisciplinary framework through modeling and simulation. Cham: Birkhäuser. Model. Simul. Sci. Eng. Technol., 11-42 (2022).
MSC:  92D30
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Modelling, simulations, and social impact of evolutionary virus pandemics. (English) Zbl 07615907

Bellomo, Nicola (ed.) et al., Predicting pandemics in a globally connected world. Volume 1. Toward a multiscale, multidisciplinary framework through modeling and simulation. Cham: Birkhäuser. Model. Simul. Sci. Eng. Technol., 1-10 (2022).
MSC:  92D30
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