About: This work constructs, analyzes, and simulates a new SEIR-type model for the dynamics and potential control of the current coronavirus (COVID-19) pandemic. It has a compartmental structure and a differential inclusion for a variable infection rate. The novelty in this work is three-fold. First, the population is separated, according to compliance with the disease control directives (shelter-in-place, masks/face coverings, physical distancing, etc.), into those who fully follow the directives and those who only partially comply with the directives or are necessarily mobile. This allows the assessment of the overall effectiveness of the control measures and the impact of their relaxing or tightening on the disease spread. Second, the model treats the infection rate as an unknown and keeps track of how it changes, due to virus mutations and saturation effects, via a differential inclusion. Third, by introducing randomness to some of the system coefficients, we study the model's sensitivity to these parameters and provide bounds and intervals of confidence for the model simulations. As a case study, the pandemic outbreak in South Korea is simulated. The model parameters were found by minimizing the deviation of the model prediction from the reported data. The simulations show that the model captures the pandemic dynamics in South Korea accurately, which provides confidence in the model predictions and its future use.   Goto Sponge  NotDistinct  Permalink

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  • This work constructs, analyzes, and simulates a new SEIR-type model for the dynamics and potential control of the current coronavirus (COVID-19) pandemic. It has a compartmental structure and a differential inclusion for a variable infection rate. The novelty in this work is three-fold. First, the population is separated, according to compliance with the disease control directives (shelter-in-place, masks/face coverings, physical distancing, etc.), into those who fully follow the directives and those who only partially comply with the directives or are necessarily mobile. This allows the assessment of the overall effectiveness of the control measures and the impact of their relaxing or tightening on the disease spread. Second, the model treats the infection rate as an unknown and keeps track of how it changes, due to virus mutations and saturation effects, via a differential inclusion. Third, by introducing randomness to some of the system coefficients, we study the model's sensitivity to these parameters and provide bounds and intervals of confidence for the model simulations. As a case study, the pandemic outbreak in South Korea is simulated. The model parameters were found by minimizing the deviation of the model prediction from the reported data. The simulations show that the model captures the pandemic dynamics in South Korea accurately, which provides confidence in the model predictions and its future use.
Subject
  • Virology
  • Epidemiology
  • Evaluation methods
  • 2019 disasters in China
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