Infectious disease control as network interventions
Abstract During the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) were implemented globally to mitigate the spread of the infection. Most NPIs can be classified using Thomas Valente’s network intervention framework and its four categories: individuals, segmentation, induction, and alter...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Social Science and Health |
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| Online Access: | https://doi.org/10.1007/s44155-025-00217-1 |
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| author | Akihiro Nishi George Dewey Michael Mengual Hiroyasu Ando Nicholas Cassol-Pawson Akira Endo |
| author_facet | Akihiro Nishi George Dewey Michael Mengual Hiroyasu Ando Nicholas Cassol-Pawson Akira Endo |
| author_sort | Akihiro Nishi |
| collection | DOAJ |
| description | Abstract During the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) were implemented globally to mitigate the spread of the infection. Most NPIs can be classified using Thomas Valente’s network intervention framework and its four categories: individuals, segmentation, induction, and alteration. However, the relationship of this framework and NPIs in the context of epidemics has not been thoroughly discussed. Therefore, we visualized NPI strategies operating during an epidemic using social network graphs and characterized each NPI with a corresponding hypothetical reproductive number to address this knowledge gap. We complement these visual aids with a glossary of technical terms used in infectious disease modeling that may be useful for scientists and policymakers. This discussion provides a concise summary that non-technical audiences can use to disseminate information to the public about the mechanism of action of various types of NPIs used during epidemics. |
| format | Article |
| id | doaj-art-46dc47b9c7ea4f7abf52443d11ecaa32 |
| institution | DOAJ |
| issn | 2731-0469 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Social Science and Health |
| spelling | doaj-art-46dc47b9c7ea4f7abf52443d11ecaa322025-08-20T03:10:31ZengSpringerDiscover Social Science and Health2731-04692025-05-01511710.1007/s44155-025-00217-1Infectious disease control as network interventionsAkihiro Nishi0George Dewey1Michael Mengual2Hiroyasu Ando3Nicholas Cassol-Pawson4Akira Endo5Department of Epidemiology, Los Angeles Fielding School of Public Health, University of CaliforniaMachine Intelligence Group for the betterment of Health and the Environment, Network Science Institute, Northeastern UniversityDepartment of Epidemiology, Los Angeles Fielding School of Public Health, University of CaliforniaDepartment of Epidemiology, Los Angeles Fielding School of Public Health, University of CaliforniaDepartment of Epidemiology, Los Angeles Fielding School of Public Health, University of CaliforniaSaw Swee Hock School of Public Health, National University of SingaporeAbstract During the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) were implemented globally to mitigate the spread of the infection. Most NPIs can be classified using Thomas Valente’s network intervention framework and its four categories: individuals, segmentation, induction, and alteration. However, the relationship of this framework and NPIs in the context of epidemics has not been thoroughly discussed. Therefore, we visualized NPI strategies operating during an epidemic using social network graphs and characterized each NPI with a corresponding hypothetical reproductive number to address this knowledge gap. We complement these visual aids with a glossary of technical terms used in infectious disease modeling that may be useful for scientists and policymakers. This discussion provides a concise summary that non-technical audiences can use to disseminate information to the public about the mechanism of action of various types of NPIs used during epidemics.https://doi.org/10.1007/s44155-025-00217-1Network interventionsSocial network analysisNon-pharmaceutical interventionsCOVID-19Respiratory infections |
| spellingShingle | Akihiro Nishi George Dewey Michael Mengual Hiroyasu Ando Nicholas Cassol-Pawson Akira Endo Infectious disease control as network interventions Discover Social Science and Health Network interventions Social network analysis Non-pharmaceutical interventions COVID-19 Respiratory infections |
| title | Infectious disease control as network interventions |
| title_full | Infectious disease control as network interventions |
| title_fullStr | Infectious disease control as network interventions |
| title_full_unstemmed | Infectious disease control as network interventions |
| title_short | Infectious disease control as network interventions |
| title_sort | infectious disease control as network interventions |
| topic | Network interventions Social network analysis Non-pharmaceutical interventions COVID-19 Respiratory infections |
| url | https://doi.org/10.1007/s44155-025-00217-1 |
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