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

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dc.contributor.author Viana, Mafalda
dc.contributor.author Shirima, Gabriel M.
dc.contributor.author John, Kunda S.
dc.contributor.author Fitzpatrick, Julie
dc.contributor.author Kazwala, Rudovick R.
dc.contributor.author Buza, Joram
dc.contributor.author Cleaveland, Sarah
dc.contributor.author Haydon, Daniel T.
dc.contributor.author Halliday, Joe. B.
dc.date.accessioned 2019-05-22T11:31:51Z
dc.date.available 2019-05-22T11:31:51Z
dc.date.issued 2016-03-03
dc.identifier.uri https://doi.org/10.1017/S0031182016000044
dc.identifier.uri http://dspace.nm-aist.ac.tz/handle/123456789/156
dc.description Research Article published by Cambridge University Press en_US
dc.description.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. en_US
dc.language.iso en_US en_US
dc.publisher Cambridge University Press en_US
dc.subject data integration en_US
dc.subject epidemiological modelling en_US
dc.subject Bayesian modelling en_US
dc.subject state-space models en_US
dc.title Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study en_US
dc.type Article en_US


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