Monitoring real-time transmission heterogeneity from incidence data.
The transmission heterogeneity of an epidemic is associated with a complex mixture of host, pathogen and environmental factors. And it may indicate superspreading events to reduce the efficiency of population-level control measures and to sustain the epidemic over a larger scale and a longer duratio...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010078 |
| _version_ | 1797974013486563328 |
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| author | Yunjun Zhang Tom Britton Xiaohua Zhou |
| author_facet | Yunjun Zhang Tom Britton Xiaohua Zhou |
| author_sort | Yunjun Zhang |
| collection | DOAJ |
| description | The transmission heterogeneity of an epidemic is associated with a complex mixture of host, pathogen and environmental factors. And it may indicate superspreading events to reduce the efficiency of population-level control measures and to sustain the epidemic over a larger scale and a longer duration. Methods have been proposed to identify significant transmission heterogeneity in historic epidemics based on several data sources, such as contact history, viral genomes and spatial information, which may not be available, and more importantly ignore the temporal trend of transmission heterogeneity. Here we attempted to establish a convenient method to estimate real-time heterogeneity over an epidemic. Within the branching process framework, we introduced an instant-individualheterogenous infectiousness model to jointly characterize the variation in infectiousness both between individuals and among different times. With this model, we could simultaneously estimate the transmission heterogeneity and the reproduction number from incidence time series. We validated the model with data of both simulated and real outbreaks. Our estimates of the overall and real-time heterogeneities of the six epidemics were consistent with those presented in the literature. Additionally, our model is robust to the ubiquitous bias of under-reporting and misspecification of serial interval. By analyzing recent data from South Africa, we found evidence that the Omicron might be of more significant transmission heterogeneity than Delta. Our model based on incidence data was proved to be reliable in estimating the real-time transmission heterogeneity. |
| first_indexed | 2024-04-11T04:12:16Z |
| format | Article |
| id | doaj.art-ccc27c5ee2ae4cc199f6eafa0cd147b8 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-11T04:12:16Z |
| publishDate | 2022-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-ccc27c5ee2ae4cc199f6eafa0cd147b82023-01-01T05:31:10ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-12-011812e101007810.1371/journal.pcbi.1010078Monitoring real-time transmission heterogeneity from incidence data.Yunjun ZhangTom BrittonXiaohua ZhouThe transmission heterogeneity of an epidemic is associated with a complex mixture of host, pathogen and environmental factors. And it may indicate superspreading events to reduce the efficiency of population-level control measures and to sustain the epidemic over a larger scale and a longer duration. Methods have been proposed to identify significant transmission heterogeneity in historic epidemics based on several data sources, such as contact history, viral genomes and spatial information, which may not be available, and more importantly ignore the temporal trend of transmission heterogeneity. Here we attempted to establish a convenient method to estimate real-time heterogeneity over an epidemic. Within the branching process framework, we introduced an instant-individualheterogenous infectiousness model to jointly characterize the variation in infectiousness both between individuals and among different times. With this model, we could simultaneously estimate the transmission heterogeneity and the reproduction number from incidence time series. We validated the model with data of both simulated and real outbreaks. Our estimates of the overall and real-time heterogeneities of the six epidemics were consistent with those presented in the literature. Additionally, our model is robust to the ubiquitous bias of under-reporting and misspecification of serial interval. By analyzing recent data from South Africa, we found evidence that the Omicron might be of more significant transmission heterogeneity than Delta. Our model based on incidence data was proved to be reliable in estimating the real-time transmission heterogeneity.https://doi.org/10.1371/journal.pcbi.1010078 |
| spellingShingle | Yunjun Zhang Tom Britton Xiaohua Zhou Monitoring real-time transmission heterogeneity from incidence data. PLoS Computational Biology |
| title | Monitoring real-time transmission heterogeneity from incidence data. |
| title_full | Monitoring real-time transmission heterogeneity from incidence data. |
| title_fullStr | Monitoring real-time transmission heterogeneity from incidence data. |
| title_full_unstemmed | Monitoring real-time transmission heterogeneity from incidence data. |
| title_short | Monitoring real-time transmission heterogeneity from incidence data. |
| title_sort | monitoring real time transmission heterogeneity from incidence data |
| url | https://doi.org/10.1371/journal.pcbi.1010078 |
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