Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models.
It is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts, and there is growing interest in the health implications of such pathogen interactions. Although community ecology approaches have been used to identify pathogen int...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1007492 |
| _version_ | 1818582234067632128 |
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| author | Colette Mair Sema Nickbakhsh Richard Reeve Jim McMenamin Arlene Reynolds Rory N Gunson Pablo R Murcia Louise Matthews |
| author_facet | Colette Mair Sema Nickbakhsh Richard Reeve Jim McMenamin Arlene Reynolds Rory N Gunson Pablo R Murcia Louise Matthews |
| author_sort | Colette Mair |
| collection | DOAJ |
| description | It is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts, and there is growing interest in the health implications of such pathogen interactions. Although community ecology approaches have been used to identify pathogen interactions at the within-host scale, methodologies enabling robust identification of interactions from population-scale data such as that available from health authorities are lacking. To address this gap, we developed a statistical framework that jointly identifies interactions between multiple viruses from contemporaneous non-stationary infection time series. Our conceptual approach is derived from a Bayesian multivariate disease mapping framework. Importantly, our approach captures within- and between-year dependencies in infection risk while controlling for confounding factors such as seasonality, demographics and infection frequencies, allowing genuine pathogen interactions to be distinguished from simple correlations. We validated our framework using a broad range of synthetic data. We then applied it to diagnostic data available for five respiratory viruses co-circulating in a major urban population between 2005 and 2013: adenovirus, human coronavirus, human metapneumovirus, influenza B virus and respiratory syncytial virus. We found positive and negative covariances indicative of epidemiological interactions among specific virus pairs. This statistical framework enables a community ecology perspective to be applied to infectious disease epidemiology with important utility for public health planning and preparedness. |
| first_indexed | 2024-12-16T07:46:08Z |
| format | Article |
| id | doaj.art-278facd6b2c94a9c866fac770f6cfe26 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-16T07:46:08Z |
| publishDate | 2019-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-278facd6b2c94a9c866fac770f6cfe262022-12-21T22:38:57ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-12-011512e100749210.1371/journal.pcbi.1007492Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models.Colette MairSema NickbakhshRichard ReeveJim McMenaminArlene ReynoldsRory N GunsonPablo R MurciaLouise MatthewsIt is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts, and there is growing interest in the health implications of such pathogen interactions. Although community ecology approaches have been used to identify pathogen interactions at the within-host scale, methodologies enabling robust identification of interactions from population-scale data such as that available from health authorities are lacking. To address this gap, we developed a statistical framework that jointly identifies interactions between multiple viruses from contemporaneous non-stationary infection time series. Our conceptual approach is derived from a Bayesian multivariate disease mapping framework. Importantly, our approach captures within- and between-year dependencies in infection risk while controlling for confounding factors such as seasonality, demographics and infection frequencies, allowing genuine pathogen interactions to be distinguished from simple correlations. We validated our framework using a broad range of synthetic data. We then applied it to diagnostic data available for five respiratory viruses co-circulating in a major urban population between 2005 and 2013: adenovirus, human coronavirus, human metapneumovirus, influenza B virus and respiratory syncytial virus. We found positive and negative covariances indicative of epidemiological interactions among specific virus pairs. This statistical framework enables a community ecology perspective to be applied to infectious disease epidemiology with important utility for public health planning and preparedness.https://doi.org/10.1371/journal.pcbi.1007492 |
| spellingShingle | Colette Mair Sema Nickbakhsh Richard Reeve Jim McMenamin Arlene Reynolds Rory N Gunson Pablo R Murcia Louise Matthews Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models. PLoS Computational Biology |
| title | Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models. |
| title_full | Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models. |
| title_fullStr | Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models. |
| title_full_unstemmed | Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models. |
| title_short | Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models. |
| title_sort | estimation of temporal covariances in pathogen dynamics using bayesian multivariate autoregressive models |
| url | https://doi.org/10.1371/journal.pcbi.1007492 |
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