tetano
Editor, Senior Moderator
PeerJ
. 2021 Jun 29;9:e11603.
doi: 10.7717/peerj.11603. eCollection 2021.
Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network
Min Dong[SUP] 1 [/SUP], Xuhang Zhang[SUP] 1 [/SUP], Kun Yang[SUP] 1 [/SUP], Rui Liu[SUP] 2 3 [/SUP], Pei Chen[SUP] 2 [/SUP]
Affiliations
Abstract
Background: Italy surpassed 1.5 million confirmed Coronavirus Disease 2019 (COVID-19) infections on November 26, as its death toll rose rapidly in the second wave of COVID-19 outbreak which is a heavy burden on hospitals. Therefore, it is necessary to forecast and early warn the potential outbreak of COVID-19 in the future, which facilitates the timely implementation of appropriate control measures. However, real-time prediction of COVID-19 transmission and outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems.
Methods: By mining the dynamical information from region networks and the short-term time series data, we developed a data-driven model, the minimum-spanning-tree-based dynamical network marker (MST-DNM), to quantitatively analyze and monitor the dynamical process of COVID-19 spreading. Specifically, we collected the historical information of daily cases caused by COVID-19 infection in Italy from February 24, 2020 to November 28, 2020. When applied to the region network of Italy, the MST-DNM model has the ability to monitor the whole process of COVID-19 transmission and successfully identify the early-warning signals. The interpretability and practical significance of our model are explained in detail in this study.
Results: The study on the dynamical changes of Italian region networks reveals the dynamic of COVID-19 transmission at the network level. It is noteworthy that the driving force of MST-DNM only relies on small samples rather than years of time series data. Therefore, it is of great potential in public surveillance for emerging infectious diseases.
Keywords: Coronavirus Disease 2019 (COVID-19); Dynamic network marker (DNM); Early warning signals; Italy; Minimum spanning tree (MST); Region network.
. 2021 Jun 29;9:e11603.
doi: 10.7717/peerj.11603. eCollection 2021.
Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network
Min Dong[SUP] 1 [/SUP], Xuhang Zhang[SUP] 1 [/SUP], Kun Yang[SUP] 1 [/SUP], Rui Liu[SUP] 2 3 [/SUP], Pei Chen[SUP] 2 [/SUP]
Affiliations
- PMID: 34249495
- PMCID: PMC8253113
- DOI: 10.7717/peerj.11603
Abstract
Background: Italy surpassed 1.5 million confirmed Coronavirus Disease 2019 (COVID-19) infections on November 26, as its death toll rose rapidly in the second wave of COVID-19 outbreak which is a heavy burden on hospitals. Therefore, it is necessary to forecast and early warn the potential outbreak of COVID-19 in the future, which facilitates the timely implementation of appropriate control measures. However, real-time prediction of COVID-19 transmission and outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems.
Methods: By mining the dynamical information from region networks and the short-term time series data, we developed a data-driven model, the minimum-spanning-tree-based dynamical network marker (MST-DNM), to quantitatively analyze and monitor the dynamical process of COVID-19 spreading. Specifically, we collected the historical information of daily cases caused by COVID-19 infection in Italy from February 24, 2020 to November 28, 2020. When applied to the region network of Italy, the MST-DNM model has the ability to monitor the whole process of COVID-19 transmission and successfully identify the early-warning signals. The interpretability and practical significance of our model are explained in detail in this study.
Results: The study on the dynamical changes of Italian region networks reveals the dynamic of COVID-19 transmission at the network level. It is noteworthy that the driving force of MST-DNM only relies on small samples rather than years of time series data. Therefore, it is of great potential in public surveillance for emerging infectious diseases.
Keywords: Coronavirus Disease 2019 (COVID-19); Dynamic network marker (DNM); Early warning signals; Italy; Minimum spanning tree (MST); Region network.