Skip to main content
×
Home

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

  • MAFALDA VIANA (a1), GABRIEL M. SHIRIMA (a2), KUNDA S. JOHN (a3), JULIE FITZPATRICK (a4), RUDOVICK R. KAZWALA (a5), JORAM J. BUZA (a2), SARAH CLEAVELAND (a1), DANIEL T. HAYDON (a1) and JO E. B. HALLIDAY (a1)...
Summary
SUMMARY

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.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about sending content to Dropbox.

      Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about sending content to Google Drive.

      Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study
      Available formats
      ×
Copyright
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Corresponding author
*Corresponding author: Daniel T. Haydon, Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK. E-mail: Daniel.Haydon@glasgow.ac.uk
References
Hide All
Basanez M. G., Marshall C., Carabin H., Gyorkos T. and Joseph L. (2004). Bayesian statistics for parasitologists. Trends in Parasitology 20, 8591.
Bonfoh B., Kasymbekov J., Durr S., Toktobaev N., Doherr M. G., Schueth T., Zinsstag J. and Schelling E. (2012). Representative seroprevalences of brucellosis in humans and livestock in Kyrgyzstan. EcoHealth 9, 132138.
Bouley A. J., Biggs H. M., Stoddard R. A., Morrissey A. B., Bartlett J. A., Afwamba I. A., Maro V. P., Kinabo G. D., Saganda W., Cleaveland S. and Crump J. A. (2012). Brucellosis among hospitalized febrile patients in Northern Tanzania. American Journal of Tropical Medicine and Hygiene 87, 11051111.
Broemeling L. D. (2014). Bayesian Methods in Epidemiology. CRC Press, Taylor & Francis Group, Boca Raton.
Burnham K. P. and Anderson D. R. (2002). Model Selection and Multimodal Inference: a Practical Information-Theoretic Approach, 2nd Edn. Springer, New York.
Cottam E. M., Wadsworth J., Shaw A. E., Rowlands R. J., Goatley L., Maan S., Maan N. S., Mertens P. P., Ebert K., Li Y., Ryan E. D., Juleff N., Ferris N. P., Wilesmith J. W., Haydon D. T., King D. P., Paton D. J. and Knowles N. J. (2008). Transmission pathways of foot-and-mouth disease virus in the United Kingdom in 2007. PLoS Pathogens 4, e1000050.
Dean A. S., Crump L., Greter H., Hattendorf J., Schelling E. and Zinsstag J. (2012 a). Clinical manifestations of human brucellosis: a systematic review and meta-analysis. PLoS Neglected Tropical Diseases 6, e1929.
Dean A. S., Crump L., Greter H., Schelling E. and Zinsstag J. (2012 b). Global burden of human brucellosis: a systematic review of disease frequency. PLoS Neglected Tropical Diseases 6, e1865.
Dohoo I., Martin W. and Stryhn H. (2003). Veterinary Epidemiologic Research. Atlantic Veterinary College Inc., Prince Edward Island, Canada.
Ebrahimi A., Milan J. S., Mahzoonieh M. R. and Khaksar K. (2014). Shedding rates and Seroprevalence of Brucella melitensis in lactating goats of Shahrekord, Iran. Jundishapur Journal of Microbiology 7, e9394.
Gelman A. (2004). Bayesian Data Analysis, 2nd Edn. Chapman & Hall/CRC, Boca Raton, USA.
Gilbert A. T., Fooks A. R., Hayman D. T., Horton D. L., Muller T., Plowright R., Peel A. J., Bowen R., Wood J. L., Mills J., Cunningham A. A. and Rupprecht C. E. (2013). Deciphering serology to understand the ecology of infectious diseases in wildlife. EcoHealth 10, 298313.
Godfroid J., Scholz H. C., Barbier T., Nicolas C., Wattiau P., Fretin D., Whatmore A. M., Cloeckaert A., Blasco J. M., Moriyon I., Saegerman C., Muma J. B., Al Dahouk S., Neubauer H. and Letesson J. J. (2011). Brucellosis at the animal/ecosystem/human interface at the beginning of the 21st century. Preventive Veterinary Medicine 102, 118131.
Haydon D. T., Chase-Topping M., Shaw D. J., Matthews L., Friar J. K., Wilesmith J. and Woolhouse M. E. (2003). The construction and analysis of epidemic trees with reference to the 2001 UK foot-and-mouth outbreak. Proceedings. Biological sciences/The Royal Society 270, 121127.
Holdo R. M., Sinclair A. R., Dobson A. P., Metzger K. L., Bolker B. M., Ritchie M. E. and Holt R. D. (2009). A disease-mediated trophic cascade in the Serengeti and its implications for ecosystem C. PLoS Biology 7, e1000210.
Hooker G., Ellner S. P., Roditi Lde V. and Earn D. J. (2011). Parameterizing state-space models for infectious disease dynamics by generalized profiling: measles in Ontario. Journal of the Royal Society Interface 8, 961974.
Jombart T., Cori A., Didelot X., Cauchemez S., Fraser C. and Ferguson N. (2014). Bayesian reconstruction of disease outbreaks by combining epidemiologic and genomic data. PLoS Computational Biology 10, e1003457.
Jones G., Johnson W. O. and Vink W. D. (2009). Evaluating a continuous biomarker for infection by using observed disease status with covariate effects on disease. Journal of the Royal Statistical Society Series C – Applied Statistics 58, 705717.
Jones G., Johnson W. O., Vink W. D. and French N. (2012). A framework for the joint modeling of longitudinal diagnostic outcome data and latent infection status: application to investigating the temporal relationship between infection and disease. Biometrics 68, 371379.
Kadohira M., McDermott J. J., Shoukri M. M. and Kyule M. N. (1997). Variations in the prevalence of antibody to Brucella infection in cattle by farm, area and district in Kenya. Epidemiology and Infection 118, 3541.
Kunda J. (2006). The Burden of Zoonoses with Specific Emphasis on Brucellosis in the Northern Regions of Tanzania. University of Edinburgh, Edinburgh.
Martin S. W., Meek A. H. and Willeberg P. (1987). Veterinary Epidemiology: Principles and Methods, 1st Edn. Iowa State University Press, Ames.
Mathew C., Stokstad M., Johansen T. B., Klevar S., Mdegela R. H., Mwamengele G., Michel P., Escobar L., Fretin D. and Godfroid J. (2015). First isolation, identification, phenotypic and genotypic characterization of Brucella abortus biovar 3 from dairy cattle in Tanzania. BMC Veterinary Research 11, 156.
McDermott J. J. and Arimi S. M. (2002). Brucellosis in sub-Saharan Africa: epidemiology, control and impact. Veterinary Microbiology 90, 111134.
McGiven J. A. (2013). New developments in the immunodiagnosis of brucellosis in livestock and wildlife. Revue scientifique et technique/Office international des épizooties 32, 163176.
McGiven J. A., Tucker J. D., Perrett L. L., Stack J. A., Brew S. D. and MacMillan A. P. (2003). Validation of FPA and cELISA for the detection of antibodies to Brucella abortus in cattle sera and comparison to SAT, CFT, and iELISA. Journal of Immunological Methods 278, 171178.
Mollentze N., Nel L. H., Townsend S., le Roux K., Hampson K., Haydon D. T. and Soubeyrand S. (2014). A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data. Proceedings Biological Sciences/The Royal Society 281, 20133251.
Morelli M. J., Thebaud G., Chadoeuf J., King D. P., Haydon D. T. and Soubeyrand S. (2012). A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data. PLoS Computational Biology 8, e1002768.
Norris M., Johnson W. O. and Gardner I. A. (2009). Modeling bivariate longitudinal diagnostic outcome data in the absence of a gold standard. Statistics and Its Interface 2, 171185.
Osoro E. M., Munyua P., Omulo S., Ogola E., Ade F., Mbatha P., Mbabu M., Ng'ang'a Z., Kairu S., Maritim M., Thumbi S. M., Bitek A., Gaichugi S., Rubin C., Njenga K. and Guerra M. (2015). Strong association between human and animal Brucella seropositivity in a linked study in Kenya, 2012–2013. American Journal of Tropical Medicine and Hygiene 93, 224231.
Patterson T. A., Thomas L., Wilcox C., Ovaskainen O. and Matthiopoulos J. (2008). State-space models of individual animal movement. Trends in Ecology & Evolution 23, 8794.
Perrett L. L., McGiven J. A., Brew S. D. and Stack J. A. (2010). Evaluation of competitive ELISA for detection of antibodies to Brucella infection in domestic animals. Croatian Medical Journal 51, 314319.
Searls D. B. (2005). Data integration: challenges for drug discovery. Nature Reviews. Drug Discovery 4, 4558.
Shirima G. M. (2005). The Epidemiology of Brucellosis in Animals and Humans in Arusha and Manyara regions in Tanzania. University of Glasgow, Glasgow.
Strelioff C. C., Vijaykrishna D., Riley S., Guan Y., Peiris J. S. M. and Lloyd-Smith J. O. (2013). Inferring patterns of influenza transmission in swine from multiple streams of surveillance data. Proceedings of the Royal Society B – Biological Sciences 280.
Viana M., Mancy R., Biek R., Cleaveland S., Cross P. C., Lloyd-Smith J. O. and Haydon D. T. (2014). Assembling evidence for identifying reservoirs of infection. Trends in Ecology & Evolution 29, 270279.
Viana M., Cleaveland S., Matthiopoulos J., Halliday J., Packer C., Craft M. E., Hampson K., Czupryna A., Dobson A. P., Dubovi E. J., Ernest E., Fyumagwa R., Hoare R., Hopcraft J. G., Horton D. L., Kaare M. T., Kanellos T., Lankester F., Mentzel C., Mlengeya T., Mzimbiri I., Takahashi E., Willett B., Haydon D. T. and Lembo T. (2015). Dynamics of a morbillivirus at the domestic-wildlife interface: canine distemper virus in domestic dogs and lions. Proceedings of the National Academy of Sciences of the United States of America 112, 14641469.
World Health Organization, Food and Agriculture Organization of the United Nations and World Organisation for Animal Health (2006). Brucellosis in Humans and Animals. World Health Organization, Geneva.
Zolzaya B., Selenge T., Narangarav T., Gantsetseg D., Erdenechimeg D., Zinsstag J. and Schelling E. (2014). Representative seroprevalences of human and livestock brucellosis in two Mongolian provinces. EcoHealth 11, 356371.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Parasitology
  • ISSN: 0031-1820
  • EISSN: 1469-8161
  • URL: /core/journals/parasitology
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Type Description Title
PDF
Supplementary Materials

Viana supplementary material
Viana supplementary material 1

 PDF (203 KB)
203 KB

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 14
Total number of PDF views: 135 *
Loading metrics...

Abstract views

Total abstract views: 349 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 18th November 2017. This data will be updated every 24 hours.