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q-bio.PEMar 15, 2020
202
citations (OpenAlex)
authors
  • Lixiang Li
  • Zihang Yang
  • Zhongkai Dang
  • Cui Meng
  • Jingze Huang
  • Hao Tian Meng
  • Deyu Wang
  • Guanhua Chen
  • Jiaxuan Zhang
  • Haipeng Peng
arXiv abstractPDF
paper

Propagation analysis and prediction of the COVID-19

arXiv:2003.06846

Abstract

Based on the official data modeling, this paper studies the transmission process of the Corona Virus Disease 2019 (COVID-19). The error between the model and the official data curve is within 3%. At the same time, it realized forward prediction and backward inference of the epidemic situation, and the relevant analysis help relevant countries to make decisions.

Cited by in corpus (10)

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  • Population Density and Spreading of COVID-19 in England and Wales
  • Epidemiological study of novel coronavirus (COVID-19)
  • "Stay Nearby or Get Checked": A Covid-19 Lockdown Exit Strategy
  • Time Series Analysis and Correlation of Subway Turnstile Usage and COVID-19 Prevalence in New York City
  • The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges
  • Projections for COVID-19 spread in India and its worst affected five states using the Modified SEIRD and LSTM models
  • An empirical model on the dynamics of Covid-19 spread in human population
  • COVID-19 cases prediction using regression and novel SSM model for non-converged countries
  • A Data-driven Understanding of COVID-19 Dynamics Using Sequential Genetic Algorithm Based Probabilistic Cellular Automata
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