Modeling the impact of twitter on influenza epidemics
Influenza remains a serious public-health problem worldwide. Therising popularity and scale of social networking sites such asTwitter may play an important role in detecting, affecting, andpredicting influenza epidemics. In this paper, we develop a simplemathematical model including the dynamics of...
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| Format: | Article |
| Language: | English |
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AIMS Press
2014-08-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.1337 |
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| author | Kasia A. Pawelek Anne Oeldorf-Hirsch Libin Rong |
| author_facet | Kasia A. Pawelek Anne Oeldorf-Hirsch Libin Rong |
| author_sort | Kasia A. Pawelek |
| collection | DOAJ |
| description | Influenza remains a serious public-health problem worldwide. Therising popularity and scale of social networking sites such asTwitter may play an important role in detecting, affecting, andpredicting influenza epidemics. In this paper, we develop a simplemathematical model including the dynamics of ``tweets'' --- short,140-character Twitter messages that may enhance the awareness ofdisease, change individual's behavior, and reduce the transmissionof disease among a population during an influenza season. We analyzethe model by deriving the basic reproductive number and proving thestability of the steady states. A Hopf bifurcation occurs when athreshold curve is crossed, which suggests the possibility ofmultiple outbreaks of influenza. We also perform numericalsimulations, conduct sensitivity test on a few parameters related totweets, and compare modeling predictions with surveillance data ofinfluenza-like illness reported cases and the percentage of tweetsself-reporting flu during the 2009 H1N1 flu outbreak in England andWales. These results show that social media programs like Twittermay serve as a good indicator of seasonal influenza epidemics andinfluence the emergence and spread of the disease. |
| format | Article |
| id | doaj-art-1df704d022714491b7fadc989154b402 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2014-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-1df704d022714491b7fadc989154b4022025-01-24T02:29:00ZengAIMS PressMathematical Biosciences and Engineering1551-00182014-08-011161337135610.3934/mbe.2014.11.1337Modeling the impact of twitter on influenza epidemicsKasia A. Pawelek0Anne Oeldorf-Hirsch1Libin Rong2Department of Mathematics and Computational Science, University of South Carolina Beaufort, Bluffton, SC 29909Department of Communication, University of Connecticut, Storrs, CT 06269Department of Mathematics and Statistics, Oakland University, Rochester, MI 48309Influenza remains a serious public-health problem worldwide. Therising popularity and scale of social networking sites such asTwitter may play an important role in detecting, affecting, andpredicting influenza epidemics. In this paper, we develop a simplemathematical model including the dynamics of ``tweets'' --- short,140-character Twitter messages that may enhance the awareness ofdisease, change individual's behavior, and reduce the transmissionof disease among a population during an influenza season. We analyzethe model by deriving the basic reproductive number and proving thestability of the steady states. A Hopf bifurcation occurs when athreshold curve is crossed, which suggests the possibility ofmultiple outbreaks of influenza. We also perform numericalsimulations, conduct sensitivity test on a few parameters related totweets, and compare modeling predictions with surveillance data ofinfluenza-like illness reported cases and the percentage of tweetsself-reporting flu during the 2009 H1N1 flu outbreak in England andWales. These results show that social media programs like Twittermay serve as a good indicator of seasonal influenza epidemics andinfluence the emergence and spread of the disease.https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.1337stabilitymathematical modelepidemiologytwitterdata fitting.hopf bifurcationsocial mediainfluenza |
| spellingShingle | Kasia A. Pawelek Anne Oeldorf-Hirsch Libin Rong Modeling the impact of twitter on influenza epidemics Mathematical Biosciences and Engineering stability mathematical model epidemiology data fitting. hopf bifurcation social media influenza |
| title | Modeling the impact of twitter on influenza epidemics |
| title_full | Modeling the impact of twitter on influenza epidemics |
| title_fullStr | Modeling the impact of twitter on influenza epidemics |
| title_full_unstemmed | Modeling the impact of twitter on influenza epidemics |
| title_short | Modeling the impact of twitter on influenza epidemics |
| title_sort | modeling the impact of twitter on influenza epidemics |
| topic | stability mathematical model epidemiology data fitting. hopf bifurcation social media influenza |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.1337 |
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