A method has been developed to track infectious diseases by using data mining of active Twitter accounts and its efficacy was demonstrated during the West African Ebola outbreak of 2014. Using a meme based n-gram semantic usage model to search the Twitter database for indications of illness, flight and death from the spread of Ebola in Africa, principally from Guinea, Sierra Leone and Liberia. Memes of interest relate disease to location and severity and are coupled to the density of Tweets and re-Tweets. The meme spreads through the community of social users in a fashion similar to nonlinear wave propagation- like a shock wave, visualized as a spike in Tweet activity. The spreading was modeled as a system isomorphic to a modified SIR (Susceptible, Infected, Removed disease model) system of three coupled nonlinear differential equations using Twitter variables. The nonlinear terms in this model lead to feedback mechanisms that result in unusual behavior that does not always reduce the spread of the disease. The resulting geographic Tweet densities are coupled to geographic maps of the region. These maps have specific threat levels that are ported to a mobile application (app) and can be used by travelers to assess the relative safety of the region they will be in.
","abstract_html":"<p>A method has been developed to track infectious diseases by using data mining of active Twitter accounts and its efficacy was demonstrated during the West African Ebola outbreak of 2014. Using a meme based n-gram semantic usage model to search the Twitter database for indications of illness, flight and death from the spread of Ebola in Africa, principally from Guinea, Sierra Leone and Liberia. Memes of interest relate disease to location and severity and are coupled to the density of Tweets and re-Tweets. The meme spreads through the community of social users in a fashion similar to nonlinear wave propagation- like a shock wave, visualized as a spike in Tweet activity. The spreading was modeled as a system isomorphic to a modified SIR (Susceptible, Infected, Removed disease model) system of three coupled nonlinear differential equations using Twitter variables. The nonlinear terms in this model lead to feedback mechanisms that result in unusual behavior that does not always reduce the spread of the disease. The resulting geographic Tweet densities are coupled to geographic maps of the region. These maps have specific threat levels that are ported to a mobile application (app) and can be used by travelers to assess the relative safety of the region they will be in.</p>","abstract_has_math":false,"creators":["Smailhodzic, Armin"],"institution":null,"degree_name":"Master of Science","degree_level":null,"degree_discipline":"Department of Physics and Astronomy","degree_department":null,"school":null,"contributors":["Keith Andrew (Director), Phillip Womble, Lance Hahn"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-05-01T07:00:00Z","date_published":"2015-05-01T07:00:00Z","updated_at":"2026-07-24T06:08:52Z","subjects":["Ebola","Twitter","SIR","Memes","Health Information Technology","Immunology and Infectious Disease","Parasitic Diseases","Virus Diseases"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.wku.edu/theses/1465","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Keith Andrew (Director), Phillip Womble, Lance Hahn"]},{"key":"dc:creator","label":"Author","values":["Smailhodzic, Armin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Department of Physics and Astronomy"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Ebola","Twitter","SIR","Memes","Health Information Technology","Immunology and Infectious Disease","Parasitic Diseases","Virus Diseases"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.wku.edu/theses/1465"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A method has been developed to track infectious diseases by using data mining of active Twitter accounts and its efficacy was demonstrated during the West African Ebola outbreak of 2014. Using a meme based n-gram semantic usage model to search the Twitter database for indications of illness, flight and death from the spread of Ebola in Africa, principally from Guinea, Sierra Leone and Liberia. Memes of interest relate disease to location and severity and are coupled to the density of Tweets and re-Tweets. The meme spreads through the community of social users in a fashion similar to nonlinear wave propagation- like a shock wave, visualized as a spike in Tweet activity. The spreading was modeled as a system isomorphic to a modified SIR (Susceptible, Infected, Removed disease model) system of three coupled nonlinear differential equations using Twitter variables. The nonlinear terms in this model lead to feedback mechanisms that result in unusual behavior that does not always reduce the spread of the disease. The resulting geographic Tweet densities are coupled to geographic maps of the region. These maps have specific threat levels that are ported to a mobile application (app) and can be used by travelers to assess the relative safety of the region they will be in.
"]},{"key":"dc:title","label":"Title","values":["Adapting the Standard SIR Disease Model in Order to Track and Predict the Spreading of the EBOLA Virus Using Twitter Data"]}]}],"canonical_facts":{"dc:contributor":["Keith Andrew (Director), Phillip Womble, Lance Hahn"],"dc:creator":["Smailhodzic, Armin"],"dc:description.abstract":["A method has been developed to track infectious diseases by using data mining of active Twitter accounts and its efficacy was demonstrated during the West African Ebola outbreak of 2014. Using a meme based n-gram semantic usage model to search the Twitter database for indications of illness, flight and death from the spread of Ebola in Africa, principally from Guinea, Sierra Leone and Liberia. Memes of interest relate disease to location and severity and are coupled to the density of Tweets and re-Tweets. The meme spreads through the community of social users in a fashion similar to nonlinear wave propagation- like a shock wave, visualized as a spike in Tweet activity. The spreading was modeled as a system isomorphic to a modified SIR (Susceptible, Infected, Removed disease model) system of three coupled nonlinear differential equations using Twitter variables. The nonlinear terms in this model lead to feedback mechanisms that result in unusual behavior that does not always reduce the spread of the disease. The resulting geographic Tweet densities are coupled to geographic maps of the region. These maps have specific threat levels that are ported to a mobile application (app) and can be used by travelers to assess the relative safety of the region they will be in.
"],"dc:identifier":["https://digitalcommons.wku.edu/theses/1465"],"dc:subject":["Ebola","Twitter","SIR","Memes","Health Information Technology","Immunology and Infectious Disease","Parasitic Diseases","Virus Diseases"],"dc:title":["Adapting the Standard SIR Disease Model in Order to Track and Predict the Spreading of the EBOLA Virus Using Twitter Data"],"dc:type":["Thesis"],"thesis:degree_discipline":["Department of Physics and Astronomy"],"thesis:degree_name":["Master of Science"]},"updated_at":"2026-07-24T06:08:52Z"}