Prediction of COVID-19 spreading profiles in South Korea, Italy and Iran by data-driven coding

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Author(s)

  • Choujun Zhan
  • Chi K. Tse
  • Zhikang Lai
  • Tianyong Hao
  • Jingjing Su

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article numbere0234763
Journal / PublicationPLoS ONE
Volume15
Issue number7
Online published6 Jul 2020
Publication statusPublished - 2020

Link(s)

Abstract

This work applies a data-driven coding method for prediction of the COVID-19 spreading profile in any given population that shows an initial phase of epidemic progression. Based on the historical data collected for COVID-19 spreading in 367 cities in China and the set of parameters of the augmented Susceptible-Exposed-Infected-Removed (SEIR) model obtained for each city, a set of profile codes representing a variety of transmission mechanisms and contact topologies is formed. By comparing the data of an early outbreak of a given population with the complete set of historical profiles, the best fit profiles are selected and the corresponding sets of profile codes are used for prediction of the future progression of the epidemic in that population. Application of the method to the data collected for South Korea, Italy and Iran shows that peaks of infection cases are expected to occur before mid April, the end of March and the end of May 2020, and that the percentage of population infected in each city or region will be less than 0.01%, 0.5% and 0.5%, for South Korea, Italy and Iran, respectively.

Research Area(s)

  • COVID-19, modelling, prediction

Citation Format(s)

Prediction of COVID-19 spreading profiles in South Korea, Italy and Iran by data-driven coding. / Zhan, Choujun; Tse, Chi K.; Lai, Zhikang; Hao, Tianyong; Su, Jingjing.

In: PLoS ONE, Vol. 15, No. 7, e0234763, 2020.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal