Selected article for: "acceptance vaccine and logistic regression"
Author: Yao Yu Yeo; Yao-Rui Yeo; Wan-Jin Yeo
Title: A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coasts
  • Document date: 2020_3_27
    • ID: 8g64u3ux_46
    Snippet: As COVID-19 has escalated into a pandemic, we also considered that a greater number of people would fall ill due to the lack of prior immunity. Considering the previous pandemic, the 2009 Influenza A (H1N1) pandemic where around 24% of people were infected [14] , as well as the nonstatic nature of our variables, we ran another simulation on the entire US population with approximately 22% to 26% of the population infected (based on randomness) to .....
    Document: As COVID-19 has escalated into a pandemic, we also considered that a greater number of people would fall ill due to the lack of prior immunity. Considering the previous pandemic, the 2009 Influenza A (H1N1) pandemic where around 24% of people were infected [14] , as well as the nonstatic nature of our variables, we ran another simulation on the entire US population with approximately 22% to 26% of the population infected (based on randomness) to predict such a scenario. For this simulation experiment, we used values for and starting days of the phases that was a rough weighted estimate of the two coasts, but used the same values for the other variables as described in the previous section. We also assumed five infected individuals were introduced into the population.

    Search related documents:
    Co phrase search for related documents
    • entire population and ill fall: 1
    • entire population and infected individual: 1, 2, 3, 4, 5, 6, 7, 8, 9
    • entire population and population introduce: 1, 2
    • entire population and previous pandemic: 1, 2
    • entire population and previous section: 1
    • great number and ill fall: 1
    • great number and infected individual: 1
    • great number and people great number: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
    • great number and previous pandemic: 1
    • infected individual and previous section: 1
    • infected individual and prior immunity: 1, 2