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_19
Snippet: where N is the number of Chinese cities in the historical archive, w I and w J are weighting coefficients, µ L and µ U are the lower and upper bounds of the searching space, respectively. By solving the the nonlinear optimization problem, we can find the most closely resembling growth curve from the historical profiles, e.g., city i. Then, we apply the the augmented SEIR model with the profile code given in the parameter set for city i to predi.....
Document: where N is the number of Chinese cities in the historical archive, w I and w J are weighting coefficients, µ L and µ U are the lower and upper bounds of the searching space, respectively. By solving the the nonlinear optimization problem, we can find the most closely resembling growth curve from the historical profiles, e.g., city i. Then, we apply the the augmented SEIR model with the profile code given in the parameter set for city i to predict the future spreading trend of city o. Furthermore, we can choose the top n best candidates with the smallest error as the candidate set for prediction, giving an average predicted propagation profile and a deviation range based on n best-fit profile codes.
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