Drawing transmission graphs for COVID-19 in the perspective of network science

Gursakal N., Batmaz B., Aktuna G.

EPIDEMIOLOGY AND INFECTION, vol.148, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 148
  • Publication Date: 2020
  • Doi Number: 10.1017/s0950268820002654
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, CINAHL, Educational research abstracts (ERA), EMBASE, Environment Index, Food Science & Technology Abstracts, Hospitality & Tourism Complete, Hospitality & Tourism Index, MEDLINE, Veterinary Science Database
  • Keywords: COVID-19, network science, reproduction number, super-spreader, transmission graphs
  • Anadolu University Affiliated: Yes


When we consider a probability distribution about how many COVID-19-infected people will transmit the disease, two points become important. First, there could be super-spreaders in these distributions/networks and second, the Pareto principle could be valid in these distributions/networks regarding estimation that 20% of cases were responsible for 80% of local transmission. When we accept that these two points are valid, the distribution of transmission becomes a discrete Pareto distribution, which is a kind of power law. Having such a transmission distribution, then we can simulate COVID-19 networks and find super-spreaders using the centricity measurements in these networks. In this research, in the first we transformed a transmission distribution of statistics and epidemiology into a transmission network of network science and second we try to determine who the super-spreaders are by using this network and eigenvalue centrality measure. We underline that determination of transmission probability distribution is a very important point in the analysis of the epidemic and determining the precautions to be taken.