Viana, M. , Shirima, G. M., John, K. S., Fitzpatrick, J., Kazwala, R. R., Buza, J. J., Cleaveland, S. , Haydon, D. T. and Halliday, J. (2016) Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study. Parasitology, 143(7), pp. 821-834. (doi: 10.1017/S0031182016000044) (PMID:26935267) (PMCID:PMC4873909)
|
Text
113245.pdf - Published Version Available under License Creative Commons Attribution. 605kB |
Abstract
Epidemiological data are often fragmented, partial, and/or ambiguous and unable to yield the desired level of understanding of infectious disease dynamics to adequately inform control measures. Here, we show how the information contained in widely available serology data can be enhanced by integration with less common type-specific data, to improve the understanding of the transmission dynamics of complex multi-species pathogens and host communities. Using brucellosis in Northern Tanzania as a case-study, we developed a latent process model based on serology data obtained from the field, to reconstruct Brucella transmission dynamics. We were able to identify sheep and goats as a more likely source of human and animal infection than cattle; however, the highly cross-reactive nature of Brucella spp. meant that it was not possible to determine which Brucella species (B. abortus or B. melitensis) is responsible for human infection. We extended our model to integrate simulated serology and typing data, and show that although serology alone can identify the host source of human infection under certain restrictive conditions, the integration of even small amounts (5%) of typing data can improve understanding of complex epidemiological dynamics. We show that data integration will often be essential when more than one pathogen is present and when the distinction between exposed and infectious individuals is not clear from serology data. With increasing epidemiological complexity, serology data become less informative. However, we show how this weakness can be mitigated by integrating such data with typing data, thereby enhancing the inference from these data and improving understanding of the underlying dynamics.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Haydon, Professor Daniel and Viana, Dr Mafalda and Halliday, Dr Jo and Cleaveland, Professor Sarah |
| Authors: | Viana, M., Shirima, G. M., John, K. S., Fitzpatrick, J., Kazwala, R. R., Buza, J. J., Cleaveland, S., Haydon, D. T., and Halliday, J. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine |
| Journal Name: | Parasitology |
| Publisher: | Cambridge University Press |
| ISSN: | 0031-1820 |
| ISSN (Online): | 1469-8161 |
| Published Online: | 03 March 2016 |
| Copyright Holders: | Copyright © 2016 Cambridge University Press |
| First Published: | First published in Parasitology 143(7): 821-834 |
| Publisher Policy: | Reproduced under a Creative Commons License |
University Staff: Request a correction | Enlighten Editors: Update this record
Funder and Project Information
Funder and Project Information