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10.1109-JBHI.2021.3114180 |
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|a 21682194 (ISSN)
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|a A Vaccination Simulator for COVID-19: Effective and Sterilizing Immunization Cases
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|b Institute of Electrical and Electronics Engineers Inc.
|c 2021
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|z View Fulltext in Publisher
|u https://doi.org/10.1109/JBHI.2021.3114180
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|a In this work, we present a particle-based SEIR epidemic simulator as a tool to assess the impact of different vaccination strategies on viral propagation and to model sterilizing and effective immunization outcomes. The simulator includes modules to support contact tracing of the interactions amongst individuals and epidemiological testing of the general population. The particles are distinguished by age to represent more accurately the infection and mortality rates. The tool can be calibrated by region of interest and for different vaccination strategies to enable locality-sensitive virus mitigation policy measures and resource allocation. Moreover, the vaccination policy can be simulated based on the prioritization of certain age groups or randomly vaccinating individuals across all age groups. The results based on the experience of the province of Lecco, Italy, indicate that the simulator can evaluate vaccination strategies in a way that incorporates local circumstances of viral propagation and demographic susceptibilities. Further, the simulator accounts for modeling the distinction between sterilizing immunization, where immunized people are no longer contagious, and effective immunization, where the individuals can transmit the virus even after getting immunized. The parametric simulation results showed that the sterilizing-age-based vaccination scenario results in the least number of deaths. Furthermore, it revealed that older people should be vaccinated first to decrease the overall mortality rate. Also, the results showed that as the vaccination rate increases, the mortality rate between the scenarios shrinks. © 2013 IEEE.
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|a adolescent
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|a adult
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|a Age groups
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|a aged
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|a Aged
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|a Article
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|a Behavioral research
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|a child
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|a compartment model
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|a computer simulation
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|a Computer Simulation
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|a contact examination
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|a Contact tracing
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|a coronavirus disease 2019
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|a COVID-19
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|a COVID-19
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|a COVID-19
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|a Decision making
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|a disease severity
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|a Effective immunization
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|a Effective immunization
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|a epidemic
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|a epidemic
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|a Epidemic contact tracing
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|a Epidemic contact tracing
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|a Epidemics
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|a Epidemiology
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|a health care system
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|a human
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|a Humans
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|a Image segmentation
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|a immunity
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|a Immunization
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|a infant
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|a infection sensitivity
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|a lockdown
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|a mathematical analysis
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|a middle aged
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|a mortality
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|a mortality rate
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|a newborn
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|a Particle-based epidemic simulator
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|a Particle-based epidemic simulator
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|a Population statistics
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|a SARS-CoV-2
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|a SEIR model
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|a SEIR model
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|a simulation
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|a Simulators
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|a Sterilizing immunization
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|a Sterilizing immunization
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|a vaccination
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|a vaccination
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|a Vaccination
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|a Vacci-nation
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|a Vaccination simulator
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|a Vaccination simulator
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|a Vaccines
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|a very elderly
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|a virus transmission
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|a Viruses
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|a Karabay, A.
|e author
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|a Kuzdeuov, A.
|e author
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|a Lewis, M.
|e author
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|a Ospanova, S.
|e author
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|a Varol, H.A.
|e author
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|t IEEE Journal of Biomedical and Health Informatics
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