Ensemble inference of unobserved infections in networks using partial observations.
Undetected infections fuel the dissemination of many infectious agents. However, identification of unobserved infectious individuals remains challenging due to limited observations of infections and imperfect knowledge of key transmission parameters. Here, we use an ensemble Bayesian inference metho...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-08-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1011355 |
| _version_ | 1827572652006440960 |
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| author | Renquan Zhang Jilei Tai Sen Pei |
| author_facet | Renquan Zhang Jilei Tai Sen Pei |
| author_sort | Renquan Zhang |
| collection | DOAJ |
| description | Undetected infections fuel the dissemination of many infectious agents. However, identification of unobserved infectious individuals remains challenging due to limited observations of infections and imperfect knowledge of key transmission parameters. Here, we use an ensemble Bayesian inference method to infer unobserved infections using partial observations. The ensemble inference method can represent uncertainty in model parameters and update model states using all ensemble members collectively. We perform extensive experiments in both model-generated and real-world networks in which individuals have differential but unknown transmission rates. The ensemble method outperforms several alternative approaches for a variety of network structures and observation rates, despite that the model is mis-specified. Additionally, the computational complexity of this algorithm scales almost linearly with the number of nodes in the network and the number of observations, respectively, exhibiting the potential to apply to large-scale networks. The inference method may support decision-making under uncertainty and be adapted for use for other dynamical models in networks. |
| first_indexed | 2024-03-07T19:08:01Z |
| format | Article |
| id | doaj.art-08b766b9184d487293b2a664fc3ee685 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-07T19:08:01Z |
| publishDate | 2023-08-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-08b766b9184d487293b2a664fc3ee6852024-03-01T05:31:11ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-08-01198e101135510.1371/journal.pcbi.1011355Ensemble inference of unobserved infections in networks using partial observations.Renquan ZhangJilei TaiSen PeiUndetected infections fuel the dissemination of many infectious agents. However, identification of unobserved infectious individuals remains challenging due to limited observations of infections and imperfect knowledge of key transmission parameters. Here, we use an ensemble Bayesian inference method to infer unobserved infections using partial observations. The ensemble inference method can represent uncertainty in model parameters and update model states using all ensemble members collectively. We perform extensive experiments in both model-generated and real-world networks in which individuals have differential but unknown transmission rates. The ensemble method outperforms several alternative approaches for a variety of network structures and observation rates, despite that the model is mis-specified. Additionally, the computational complexity of this algorithm scales almost linearly with the number of nodes in the network and the number of observations, respectively, exhibiting the potential to apply to large-scale networks. The inference method may support decision-making under uncertainty and be adapted for use for other dynamical models in networks.https://doi.org/10.1371/journal.pcbi.1011355 |
| spellingShingle | Renquan Zhang Jilei Tai Sen Pei Ensemble inference of unobserved infections in networks using partial observations. PLoS Computational Biology |
| title | Ensemble inference of unobserved infections in networks using partial observations. |
| title_full | Ensemble inference of unobserved infections in networks using partial observations. |
| title_fullStr | Ensemble inference of unobserved infections in networks using partial observations. |
| title_full_unstemmed | Ensemble inference of unobserved infections in networks using partial observations. |
| title_short | Ensemble inference of unobserved infections in networks using partial observations. |
| title_sort | ensemble inference of unobserved infections in networks using partial observations |
| url | https://doi.org/10.1371/journal.pcbi.1011355 |
| work_keys_str_mv | AT renquanzhang ensembleinferenceofunobservedinfectionsinnetworksusingpartialobservations AT jileitai ensembleinferenceofunobservedinfectionsinnetworksusingpartialobservations AT senpei ensembleinferenceofunobservedinfectionsinnetworksusingpartialobservations |