Social network analysis for better understanding of influenza

Branimir Ljubic; Djordje Gligorijevic; Jelena Gligorijevic; Martin Pavlovski; Zoran Obradovic

doi:10.1016/j.jbi.2019.103161

Social network analysis for better understanding of influenza

J Biomed Inform. 2019 May:93:103161. doi: 10.1016/j.jbi.2019.103161. Epub 2019 Mar 30.

Authors

Branimir Ljubic¹, Djordje Gligorijevic², Jelena Gligorijevic³, Martin Pavlovski⁴, Zoran Obradovic⁵

Affiliations

¹ Temple University, Center for Data Analytics and Biomedical Informatics (DABI), Philadelphia, PA, USA. Electronic address: branimir.ljubic@temple.edu.
² Temple University, Center for Data Analytics and Biomedical Informatics (DABI), Philadelphia, PA, USA. Electronic address: gligorijevic@temple.edu.
³ Temple University, Center for Data Analytics and Biomedical Informatics (DABI), Philadelphia, PA, USA. Electronic address: jelena.stojanovic@temple.edu.
⁴ Temple University, Center for Data Analytics and Biomedical Informatics (DABI), Philadelphia, PA, USA. Electronic address: tuh27103@temple.edu.
⁵ Temple University, Center for Data Analytics and Biomedical Informatics (DABI), Philadelphia, PA, USA. Electronic address: zoran.obradovic@temple.edu.

PMID: 30940598
DOI: 10.1016/j.jbi.2019.103161

Abstract

Introduction: The objective of this study is to improve the understanding of spatial spreading of complicated cases of influenza that required hospitalizations, by creating heatmaps and social networks. They will allow to identify critical hubs and routes of spreading of Influenza, in specific geographic locations, in order to contain infections and prevent complications, that require hospitalizations.

Material and methods: Data were downloaded from the Healthcare Cost and Utilization Project (HCUP) - SID, New York State database. Patients hospitalized with flu complications, between 2003 and 2012 were included in the research (30,380 cases). A novel approach was designed, by constructing heatmaps for specific geographic regions in New York state and power law networks, in order to analyze distribution of hospitalized flu cases.

Results: Heatmaps revealed that distributions of patients follow urban areas and big roads, indicating that flu spreads along routes, that people use to travel. A scale-free network, created from correlations among zip codes, discovered that, the highest populated zip codes didn't have the largest number of patients with flu complications. Among the top five most affected zip codes, four were in Bronx. Demographics of top affected zip codes were presented in results. Normalized numbers of cases per population revealed that, none of zip codes from Bronx were in the top 20. All zip codes with the highest node degrees were in New York City area.

Discussion: Heatmaps identified geographic distribution of hospitalized flu patients and network analysis identified hubs of the infection. Our results will enable better estimation of resources for prevention and treatment of hospitalized patients with complications of Influenza.

Conclusion: Analyses of geographic distribution of hospitalized patients with Influenza and demographic characteristics of populations, help us to make better planning and management of resources for Influenza patients, that require hospitalization. Obtained results could potentially help to save many lives and improve the health of the population.

Keywords: Biomedical informatics; Demographics; Heatmaps; Infectious disease spreading; Influenza; Social networks.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Hospitalization
Humans
Influenza, Human / epidemiology*
New York / epidemiology
Social Networking*
Travel