tetano
Editor, Senior Moderator
Optim Control Appl Methods
. 2021 Oct 21;10.1002/oca.2806.
doi: 10.1002/oca.2806. Online ahead of print.
Prediction of asymptomatic COVID-19 infections based on complex network
Yili Chen[SUP] 1 [/SUP], Haoming He[SUP] 2 3 [/SUP], Dakang Liu[SUP] 4 [/SUP], Xie Zhang[SUP] 5 [/SUP], Jingpei Wang[SUP] 6 [/SUP], Yixiao Yang[SUP] 7 [/SUP]
Affiliations
Abstract
Novel coronavirus pneumonia (COVID-19) epidemic outbreak at the end of 2019 and threaten global public health, social stability, and economic development, which is characterized by highly contagious and asymptomatic infections. At present, governments around the world are taking decisive action to limit the human and economic impact of COVID-19, but very few interventions have been made to target the transmission of asymptomatic infected individuals. Thus, it is a quite crucial and complex problem to make accurate forecasts of epidemic trends, which many types of research dedicated to deal with it. In this article, we set up a novel COVID-19 transmission model by introducing traditional SEIR (susceptible-exposed-infected-removed) disease transmission models into complex network and propose an effective prediction algorithm based on the traditional machine learning algorithm TrustRank, which can predict asymptomatic infected individuals in a population contact network. Our simulation results show that our method largely outperforms the graph neural network algorithm for new coronary pneumonia prediction and our method is also robust and gives good results even if the network information is incomplete.
Keywords: COVID‐19; Trustrank algorithm; complex network; machine learning.
. 2021 Oct 21;10.1002/oca.2806.
doi: 10.1002/oca.2806. Online ahead of print.
Prediction of asymptomatic COVID-19 infections based on complex network
Yili Chen[SUP] 1 [/SUP], Haoming He[SUP] 2 3 [/SUP], Dakang Liu[SUP] 4 [/SUP], Xie Zhang[SUP] 5 [/SUP], Jingpei Wang[SUP] 6 [/SUP], Yixiao Yang[SUP] 7 [/SUP]
Affiliations
- PMID: 34908628
- PMCID: PMC8661857
- DOI: 10.1002/oca.2806
Abstract
Novel coronavirus pneumonia (COVID-19) epidemic outbreak at the end of 2019 and threaten global public health, social stability, and economic development, which is characterized by highly contagious and asymptomatic infections. At present, governments around the world are taking decisive action to limit the human and economic impact of COVID-19, but very few interventions have been made to target the transmission of asymptomatic infected individuals. Thus, it is a quite crucial and complex problem to make accurate forecasts of epidemic trends, which many types of research dedicated to deal with it. In this article, we set up a novel COVID-19 transmission model by introducing traditional SEIR (susceptible-exposed-infected-removed) disease transmission models into complex network and propose an effective prediction algorithm based on the traditional machine learning algorithm TrustRank, which can predict asymptomatic infected individuals in a population contact network. Our simulation results show that our method largely outperforms the graph neural network algorithm for new coronary pneumonia prediction and our method is also robust and gives good results even if the network information is incomplete.
Keywords: COVID‐19; Trustrank algorithm; complex network; machine learning.