Replicating superspreader dynamics with compartmental models
Abstract Infectious disease outbreaks often exhibit superspreader dynamics, where most infected people generate no, or few secondary cases, and only a small fraction of individuals are responsible for a large proportion of transmission. Although capturing this heterogeneity is critical for estimatin...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-42567-3 |
| _version_ | 1797576929634680832 |
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| author | Michael T. Meehan Angus Hughes Romain R. Ragonnet Adeshina I. Adekunle James M. Trauer Pavithra Jayasundara Emma S. McBryde Alec S. Henderson |
| author_facet | Michael T. Meehan Angus Hughes Romain R. Ragonnet Adeshina I. Adekunle James M. Trauer Pavithra Jayasundara Emma S. McBryde Alec S. Henderson |
| author_sort | Michael T. Meehan |
| collection | DOAJ |
| description | Abstract Infectious disease outbreaks often exhibit superspreader dynamics, where most infected people generate no, or few secondary cases, and only a small fraction of individuals are responsible for a large proportion of transmission. Although capturing this heterogeneity is critical for estimating outbreak risk and the effectiveness of group-specific interventions, it is typically neglected in compartmental models of infectious disease transmission—which constitute the most common transmission dynamic modeling framework. In this study we propose different classes of compartmental epidemic models that incorporate transmission heterogeneity, fit them to a number of real outbreak datasets, and benchmark their performance against the canonical superspreader model (i.e., the negative binomial branching process model). We find that properly constructed compartmental models can capably reproduce observed superspreader dynamics and we provide the pathogen-specific parameter settings required to do so. As a consequence, we also show that compartmental models parameterized according to a binary clinical classification have limited support. |
| first_indexed | 2024-03-10T22:01:46Z |
| format | Article |
| id | doaj.art-cdd98e9b60fe4936b35bd2a64998b24a |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T22:01:46Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-cdd98e9b60fe4936b35bd2a64998b24a2023-11-19T12:57:04ZengNature PortfolioScientific Reports2045-23222023-09-0113111110.1038/s41598-023-42567-3Replicating superspreader dynamics with compartmental modelsMichael T. Meehan0Angus Hughes1Romain R. Ragonnet2Adeshina I. Adekunle3James M. Trauer4Pavithra Jayasundara5Emma S. McBryde6Alec S. Henderson7Australian Institute of Tropical Health and Medicine, James Cook UniversitySchool of Public Health and Preventive Medicine, Monash UniversitySchool of Public Health and Preventive Medicine, Monash UniversityDefence Science and Technology Group, Department of DefenceSchool of Public Health and Preventive Medicine, Monash UniversitySchool of Public Health and Preventive Medicine, Monash UniversityAustralian Institute of Tropical Health and Medicine, James Cook UniversityAustralian Institute of Tropical Health and Medicine, James Cook UniversityAbstract Infectious disease outbreaks often exhibit superspreader dynamics, where most infected people generate no, or few secondary cases, and only a small fraction of individuals are responsible for a large proportion of transmission. Although capturing this heterogeneity is critical for estimating outbreak risk and the effectiveness of group-specific interventions, it is typically neglected in compartmental models of infectious disease transmission—which constitute the most common transmission dynamic modeling framework. In this study we propose different classes of compartmental epidemic models that incorporate transmission heterogeneity, fit them to a number of real outbreak datasets, and benchmark their performance against the canonical superspreader model (i.e., the negative binomial branching process model). We find that properly constructed compartmental models can capably reproduce observed superspreader dynamics and we provide the pathogen-specific parameter settings required to do so. As a consequence, we also show that compartmental models parameterized according to a binary clinical classification have limited support.https://doi.org/10.1038/s41598-023-42567-3 |
| spellingShingle | Michael T. Meehan Angus Hughes Romain R. Ragonnet Adeshina I. Adekunle James M. Trauer Pavithra Jayasundara Emma S. McBryde Alec S. Henderson Replicating superspreader dynamics with compartmental models Scientific Reports |
| title | Replicating superspreader dynamics with compartmental models |
| title_full | Replicating superspreader dynamics with compartmental models |
| title_fullStr | Replicating superspreader dynamics with compartmental models |
| title_full_unstemmed | Replicating superspreader dynamics with compartmental models |
| title_short | Replicating superspreader dynamics with compartmental models |
| title_sort | replicating superspreader dynamics with compartmental models |
| url | https://doi.org/10.1038/s41598-023-42567-3 |
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