Selected article for: "air traffic and short period"

Author: Muhammad Qasim; Waqas Ahmad; Minami Yoshida; Maree Gould; Muhammad Yasir
Title: Analysis of the Worldwide Corona Virus (COVID-19) Pandemic Trend;A Modelling Study to Predict Its Spread
  • Document date: 2020_4_1
  • ID: 8lku99jc_1_1
    Snippet: considering the governmental actions, individual behavioural response, zoonotic transmission and the emigration of a large proportion of the population over a short time period was developed and compared with influenza pandemic of UK in 1918. 20 Similarly the risk of importation cases of COVID-19 to Africa from China was estimated based on flight data that originated from China and classified all of Africa into three clusters based on calculated .....
    Document: considering the governmental actions, individual behavioural response, zoonotic transmission and the emigration of a large proportion of the population over a short time period was developed and compared with influenza pandemic of UK in 1918. 20 Similarly the risk of importation cases of COVID-19 to Africa from China was estimated based on flight data that originated from China and classified all of Africa into three clusters based on calculated risk. 21 But most of these studies, have used air traffic as a base line for modelling or they consider data from only one city to predict the size of the outbreak and the ability of the country to contain the outbreak. All these different mathematical and statistical models proposed for COVID-19 have different strengths and weaknesses. In particular, some of these model specifications are better than others in distinct scenarios and in distinguishing prediction targets such as different measures of timing or the severity of the COVID-19. Additionally, there is always room for enhancing the efficiency of quantitative analyses and their results to make communication of data interpretation easy to public health concerned authorities. An accurate predictive model can help in resource mobilization and current and future disease management.

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