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Network spreading among areas: A dynamical complex network modeling approach.
Li, Qin; Chen, Hongkai; Li, Yuhan; Feng, Minyu; Kurths, Jürgen.
  • Li Q; School of Public Policy and Administration, Chongqing University, Chongqing 400044, People's Republic of China.
  • Chen H; College of Artificial Intelligence, Southwest University, Chongqing 400715, People's Republic of China.
  • Li Y; College of Artificial Intelligence, Southwest University, Chongqing 400715, People's Republic of China.
  • Feng M; College of Artificial Intelligence, Southwest University, Chongqing 400715, People's Republic of China.
  • Kurths J; Potsdam Institute for Climate Impact Research, 14437 Potsdam, Germany.
Chaos ; 32(10): 103102, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-2077214
ABSTRACT
With the outbreak of COVID-19, great loss and damage were brought to human society, making the study of epidemic spreading become a significant topic nowadays. To analyze the spread of infectious diseases among different areas, e.g., communities, cities, or countries, we construct a network, based on the epidemic model and the network coupling, whose nodes denote areas, and edges represent population migrations between two areas. Each node follows its dynamic, which describes an epidemic spreading among individuals in an area, and the node also interacts with other nodes, which indicates the spreading among different areas. By giving mathematical proof, we deduce that our model has a stable solution despite the network structure. We propose the peak infected ratio (PIR) as a property of infectious diseases in a certain area, which is not independent of the network structure. We find that increasing the population mobility or the disease infectiousness both cause higher peak infected population all over different by simulation. Furthermore, we apply our model to real-world data on COVID-19 and after properly adjusting the parameters of our model, the distribution of the peak infection ratio in different areas can be well fitted.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Communicable Diseases / Epidemics / COVID-19 Type of study: Observational study Limits: Humans Language: English Journal: Chaos Journal subject: Science Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Communicable Diseases / Epidemics / COVID-19 Type of study: Observational study Limits: Humans Language: English Journal: Chaos Journal subject: Science Year: 2022 Document Type: Article