Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study

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)

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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 Cleaveland, Professor Sarah and Viana, Dr Mafalda and Halliday, Dr Joanna
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 > Institute of Biodiversity Animal Health and Comparative 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

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
628321Molecular epidemology of brucellosis in northern TanzaniaDaniel HaydonBiotechnology and Biological Sciences Research Council (BBSRC)BB/L018845/1RI BIODIVERSITY ANIMAL HEALTH & COMPMED
568221Impact, ecology and social determinants of bacterial zoonoses in northern TanzaniaSarah CleavelandBiotechnology and Biological Sciences Research Council (BBSRC)BB/J010367/1RI BIODIVERSITY ANIMAL HEALTH & COMPMED
627871Social, economic and environmental drivers of zoonoses in Tanzania (SEEDZ)Sarah CleavelandBiotechnology and Biological Sciences Research Council (BBSRC)BB/L018926/1RI BIODIVERSITY ANIMAL HEALTH & COMPMED
660521A One-Health approach to dissecting the diverse zoonotic causes of non-malaria febrile illnessDaniel HaydonThe Royal Society (ROYSOC)AA130131RI BIODIVERSITY ANIMAL HEALTH & COMPMED