Author: Choujun Zhan; Chi K. Tse; Zhikang Lai; Tianyong Hao; Jingjing Su
Title: Prediction of COVID-19 Spreading Profiles in South Korea, Italy and Iran by Data-Driven Coding Document date: 2020_3_10
ID: mr8z65o5_10
Snippet: where 0 ≤ |X j (t)| ≤ 1. Since the above model has taken into account the human migration effect as well as the necessary transmission mechanism, we may consider the basic set of parameters to represent the characteristics of the propagation profile of city j. The complete set of parameters have been identified for 367 cities in China [4] , which will serve as a set of codes for various propagation profiles of COVID-19 so far obtained. For br.....
Document: where 0 ≤ |X j (t)| ≤ 1. Since the above model has taken into account the human migration effect as well as the necessary transmission mechanism, we may consider the basic set of parameters to represent the characteristics of the propagation profile of city j. The complete set of parameters have been identified for 367 cities in China [4] , which will serve as a set of codes for various propagation profiles of COVID-19 so far obtained. For brevity, we do not repeat the results here. While two different cities may have different population size and percentage of eventual infected population, the rates of infection and recovery should be similar across a group of cities, i.e., µ i ≈ µ j . Thus, in normalized form, we have
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