Infectious disease phylodynamics with occurrence data
Abstract Phylodynamic models use pathogen genome sequence data to infer epidemiological dynamics. With the increasing genomic surveillance of pathogens, especially during the SARS‐CoV‐2 pandemic, new practical questions about their use are emerging. One such question focuses on the inclusion of un‐s...
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| Format: | Article |
| Language: | English |
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Wiley
2021-08-01
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| Series: | Methods in Ecology and Evolution |
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| Online Access: | https://doi.org/10.1111/2041-210X.13620 |
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| author | Leo A. Featherstone Francesca Di Giallonardo Edward C. Holmes Timothy G. Vaughan Sebastián Duchêne |
| author_facet | Leo A. Featherstone Francesca Di Giallonardo Edward C. Holmes Timothy G. Vaughan Sebastián Duchêne |
| author_sort | Leo A. Featherstone |
| collection | DOAJ |
| description | Abstract Phylodynamic models use pathogen genome sequence data to infer epidemiological dynamics. With the increasing genomic surveillance of pathogens, especially during the SARS‐CoV‐2 pandemic, new practical questions about their use are emerging. One such question focuses on the inclusion of un‐sequenced case occurrence data alongside sequenced data to improve phylodynamic analyses. This approach can be particularly valuable if sequencing efforts vary over time. Using simulations, we demonstrate that birth–death phylodynamic models can employ occurrence data to eliminate bias in estimates of the basic reproductive number due to misspecification of the sampling process. In contrast, the coalescent exponential model is robust to such sampling biases, but in the absence of a sampling model it cannot exploit occurrence data. Subsequent analysis of the SARS‐CoV‐2 epidemic in the northwest USA supports these results. We conclude that occurrence data are a valuable source of information in combination with birth–death models. These data should be used to bolster phylodynamic analyses of infectious diseases and other rapidly spreading species in the future. |
| format | Article |
| id | doaj-art-86ebbbdd474742c3b58bd63877626215 |
| institution | Kabale University |
| issn | 2041-210X |
| language | English |
| publishDate | 2021-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj-art-86ebbbdd474742c3b58bd638776262152025-02-07T06:21:05ZengWileyMethods in Ecology and Evolution2041-210X2021-08-011281498150710.1111/2041-210X.13620Infectious disease phylodynamics with occurrence dataLeo A. Featherstone0Francesca Di Giallonardo1Edward C. Holmes2Timothy G. Vaughan3Sebastián Duchêne4Department of Microbiology and Immunology Peter Doherty Institute for Infection and Immunity University of Melbourne Melbourne Vic. AustraliaThe Kirby InstituteUNSW Sydney Sydney NSW AustraliaMarie Bashir Institute for Infectious Diseases and BiosecurityThe University of Sydney Sydney NSW AustraliaDepartment of Biosystems Science and Engineering ETH Zurich Basel SwitzerlandDepartment of Microbiology and Immunology Peter Doherty Institute for Infection and Immunity University of Melbourne Melbourne Vic. AustraliaAbstract Phylodynamic models use pathogen genome sequence data to infer epidemiological dynamics. With the increasing genomic surveillance of pathogens, especially during the SARS‐CoV‐2 pandemic, new practical questions about their use are emerging. One such question focuses on the inclusion of un‐sequenced case occurrence data alongside sequenced data to improve phylodynamic analyses. This approach can be particularly valuable if sequencing efforts vary over time. Using simulations, we demonstrate that birth–death phylodynamic models can employ occurrence data to eliminate bias in estimates of the basic reproductive number due to misspecification of the sampling process. In contrast, the coalescent exponential model is robust to such sampling biases, but in the absence of a sampling model it cannot exploit occurrence data. Subsequent analysis of the SARS‐CoV‐2 epidemic in the northwest USA supports these results. We conclude that occurrence data are a valuable source of information in combination with birth–death models. These data should be used to bolster phylodynamic analyses of infectious diseases and other rapidly spreading species in the future.https://doi.org/10.1111/2041-210X.13620Bayesian statisticsbirth–deathcoalescentpathogensphylodynamics |
| spellingShingle | Leo A. Featherstone Francesca Di Giallonardo Edward C. Holmes Timothy G. Vaughan Sebastián Duchêne Infectious disease phylodynamics with occurrence data Methods in Ecology and Evolution Bayesian statistics birth–death coalescent pathogens phylodynamics |
| title | Infectious disease phylodynamics with occurrence data |
| title_full | Infectious disease phylodynamics with occurrence data |
| title_fullStr | Infectious disease phylodynamics with occurrence data |
| title_full_unstemmed | Infectious disease phylodynamics with occurrence data |
| title_short | Infectious disease phylodynamics with occurrence data |
| title_sort | infectious disease phylodynamics with occurrence data |
| topic | Bayesian statistics birth–death coalescent pathogens phylodynamics |
| url | https://doi.org/10.1111/2041-210X.13620 |
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