A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak.
In recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level th...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-10-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1005798 |
| _version_ | 1818348840193884160 |
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| author | Max S Y Lau Max S Y Lau Gavin J Gibson Hola Adrakey Amanda McClelland Steven Riley Jon Zelner George Streftaris Sebastian Funk Jessica Metcalf Benjamin D Dalziel Bryan T Grenfell |
| author_facet | Max S Y Lau Max S Y Lau Gavin J Gibson Hola Adrakey Amanda McClelland Steven Riley Jon Zelner George Streftaris Sebastian Funk Jessica Metcalf Benjamin D Dalziel Bryan T Grenfell |
| author_sort | Max S Y Lau |
| collection | DOAJ |
| description | In recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level than previously. However, there remains a lack of statistically sound frameworks to model the underlying transmission dynamic in a mechanistic manner. Such a development is particularly crucial for enabling a general epidemic predictive framework at the individual level. In this paper we propose a new statistical framework for mechanistically modelling individual-to-individual disease transmission in a landscape with heterogeneous population density. Our methodology is first tested using simulated datasets, validating our inferential machinery. The methodology is subsequently applied to data that describes a regional Ebola outbreak in Western Africa (2014-2015). Our results show that the methods are able to obtain estimates of key epidemiological parameters that are broadly consistent with the literature, while revealing a significantly shorter distance of transmission. More importantly, in contrast to existing approaches, we are able to perform a more general model prediction that takes into account the susceptible population. Finally, our results show that, given reasonable scenarios, the framework can be an effective surrogate for susceptible-explicit individual models which are often computationally challenging. |
| first_indexed | 2024-12-13T17:56:27Z |
| format | Article |
| id | doaj.art-55f8213ef28e4cc8ab0169a36c71f29d |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-13T17:56:27Z |
| publishDate | 2017-10-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-55f8213ef28e4cc8ab0169a36c71f29d2022-12-21T23:36:22ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-10-011310e100579810.1371/journal.pcbi.1005798A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak.Max S Y LauMax S Y LauGavin J GibsonHola AdrakeyAmanda McClellandSteven RileyJon ZelnerGeorge StreftarisSebastian FunkJessica MetcalfBenjamin D DalzielBryan T GrenfellIn recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level than previously. However, there remains a lack of statistically sound frameworks to model the underlying transmission dynamic in a mechanistic manner. Such a development is particularly crucial for enabling a general epidemic predictive framework at the individual level. In this paper we propose a new statistical framework for mechanistically modelling individual-to-individual disease transmission in a landscape with heterogeneous population density. Our methodology is first tested using simulated datasets, validating our inferential machinery. The methodology is subsequently applied to data that describes a regional Ebola outbreak in Western Africa (2014-2015). Our results show that the methods are able to obtain estimates of key epidemiological parameters that are broadly consistent with the literature, while revealing a significantly shorter distance of transmission. More importantly, in contrast to existing approaches, we are able to perform a more general model prediction that takes into account the susceptible population. Finally, our results show that, given reasonable scenarios, the framework can be an effective surrogate for susceptible-explicit individual models which are often computationally challenging.https://doi.org/10.1371/journal.pcbi.1005798 |
| spellingShingle | Max S Y Lau Max S Y Lau Gavin J Gibson Hola Adrakey Amanda McClelland Steven Riley Jon Zelner George Streftaris Sebastian Funk Jessica Metcalf Benjamin D Dalziel Bryan T Grenfell A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak. PLoS Computational Biology |
| title | A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak. |
| title_full | A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak. |
| title_fullStr | A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak. |
| title_full_unstemmed | A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak. |
| title_short | A mechanistic spatio-temporal framework for modelling individual-to-individual transmission-With an application to the 2014-2015 West Africa Ebola outbreak. |
| title_sort | mechanistic spatio temporal framework for modelling individual to individual transmission with an application to the 2014 2015 west africa ebola outbreak |
| url | https://doi.org/10.1371/journal.pcbi.1005798 |
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