Heterogeneous shedding of Escherichia coli O157 in cattle and its implications for control.
Journal: 2006/February - Proceedings of the National Academy of Sciences of the United States of America
ISSN: 0027-8424
Abstract:
Identification of the relative importance of within- and between-host variability in infectiousness and the impact of these heterogeneities on the transmission dynamics of infectious agents can enable efficient targeting of control measures. Cattle, a major reservoir host for the zoonotic pathogen Escherichia coli O157, are known to exhibit a high degree of heterogeneity in bacterial shedding densities. By relating bacterial count to infectiousness and fitting dynamic epidemiological models to prevalence data from a cross-sectional survey of cattle farms in Scotland, we identify a robust pattern: approximately 80% of the transmission arises from the 20% most infectious individuals. We examine potential control options under a range of assumptions about within- and between-host variability in infection dynamics. Our results show that the within-herd basic reproduction ratio, R(0), could be reduced to <1 with targeted measures aimed at preventing infection in the 5% of individuals with the highest overall infectiousness. Alternatively, interventions such as vaccination or the use of probiotics that aim to reduce bacterial carriage could produce dramatic reductions in R(0) by preventing carriage at concentrations corresponding to the top few percent of the observed range of counts. We conclude that a greater understanding of the cause of the heterogeneity in bacterial carriage could lead to highly efficient control measures to reduce the prevalence of E. coli O157.
Relations:
Content
Citations
(73)
References
(49)
Diseases
(1)
Drugs
(1)
Organisms
(3)
Affiliates
(3)
Similar articles
Articles by the same authors
Discussion board
Proc Natl Acad Sci U S A 103(3): 547-552

Heterogeneous shedding of <em>Escherichia coli</em> O157 in cattle and its implications for control

+3 authors
Centre for Infectious Diseases, College of Medicine and Veterinary Medicine, University of Edinburgh, Easter Bush, Roslin, Midlothian EH25 9RG, United Kingdom; Scottish Agricultural College Animal Health Group, Scottish Agricultural College, King's Buildings, West Mains Road, Edinburgh EH9 3JG, United Kingdom; Zoonotic and Animal Pathogens Research Laboratory, Medical Microbiology, Edinburgh University, Edinburgh EH8 9AG, United Kingdom; Institute of Comparative Medicine, Faculty of Veterinary Medicine, University of Glasgow, Bearsden Road, Glasgow G61 1QH, United Kingdom; Theoretical Epidemiology, Department of Farm Animal Health, University of Utrecht, Yalelaan 7, 3584 CL, Utrecht, The Netherlands; and Veterinary Clinical Studies, R(D)SVS, University of Edinburgh, Easter Bush, Roslin, Midlothian EH25 9RG, United Kingdom
To whom correspondence should be sent at the present address: Faculty of Veterinary Medicine, University of Glasgow, Bearsden Road, Glasgow G61 1QH, United Kingdom. E-mail: ku.ca.alg.tev@swehttam.l.
Edited by Roy Curtiss, Arizona State University, Tempe, AZ, and approved November 27, 2005
Edited by Roy Curtiss, Arizona State University, Tempe, AZ, and approved November 27, 2005
Received 2005 May 6

Freely available online through the PNAS open access option.

Abstract

Identification of the relative importance of within- and between-host variability in infectiousness and the impact of these heterogeneities on the transmission dynamics of infectious agents can enable efficient targeting of control measures. Cattle, a major reservoir host for the zoonotic pathogen Escherichia coli O157, are known to exhibit a high degree of heterogeneity in bacterial shedding densities. By relating bacterial count to infectiousness and fitting dynamic epidemiological models to prevalence data from a cross-sectional survey of cattle farms in Scotland, we identify a robust pattern: ≈80% of the transmission arises from the 20% most infectious individuals. We examine potential control options under a range of assumptions about within- and between-host variability in infection dynamics. Our results show that the within-herd basic reproduction ratio, R0, could be reduced to <1 with targeted measures aimed at preventing infection in the 5% of individuals with the highest overall infectiousness. Alternatively, interventions such as vaccination or the use of probiotics that aim to reduce bacterial carriage could produce dramatic reductions in R0 by preventing carriage at concentrations corresponding to the top few percent of the observed range of counts. We conclude that a greater understanding of the cause of the heterogeneity in bacterial carriage could lead to highly efficient control measures to reduce the prevalence of E. coli O157.

Keywords: bacterial count, core groups, super shedder, superspreading, targeted control
Abstract

The role of heterogeneous infectiousness on the course of disease outbreaks was highlighted during the recent severe acute respiratory syndrome outbreak (1), in which a few individuals were responsible for a disproportionate number of transmission events. Awareness of heterogeneities in transmission dynamics can be important for the effective implementation of disease control measures and can lead to efficient targeting of interventions at a subset of the population (25). Factors that might lead to such heterogeneities include variability in infectiousness, exposure, genetic susceptibility, contact rates, and behavior (610). Quantifying their impact on the transmission dynamics can be achieved through direct methods, such as contact tracing and outbreak reconstruction (1, 11), or indirectly through their effect on the distribution of infected cases (12).

Escherichia coli O157 is an important zoonosis with a known reservoir in cattle (13, 14). Prevalences of infection are generally low, usually reported to be <10% of animals carrying the pathogen (14). Typically, however, the distribution of prevalences is highly skewed (15); at any one time, shedding is not detected in the majority of cattle groups, but a small proportion of groups contains high numbers of individuals shedding bacteria in their feces.

The range of prevalences of an infectious agent in a small population is expected to be influenced both by stochasticity and underlying heterogeneities in the transmission dynamics. In a recent analysis of prevalence data from Scottish cattle farms (12), it was shown that the observed distribution of prevalences across cattle groups could not arise through the inherent stochasticity in infection dynamics alone but that the highly skewed distribution is best explained when a small proportion of cattle is assumed to have much higher transmission rates than the others.

Accumulating evidence suggests that some cattle may harbor and shed E. coli O157 at higher concentrations than others. Several recent studies of slaughterhouse cattle have identified a proportion of animals as being high shedders of E. coli O157 (1619). A recent longitudinal study of naturally infected calves (20) found that although in the majority of calves the pathogen was isolated intermittently, a small number of individuals appeared to be persistent high shedders.

Although considerable variation in shedding concentrations is observed (1622), many of these studies do not reveal the relative extent of within- and between-host variability in carriage during the course of a natural infection with the organism. However, the success of previous modeling work (12) in describing the E. coli O157 prevalence data suggests that between-host variation in shedding concentrations is epidemiologically important.

In the present study, we consider a cross-sectional study of cattle groups from 474 cattle farms (see Fig. 1a) for which bacterial counts (see Fig. 2) were obtained for the majority of positive samples. These two data sets provide a unique opportunity to examine the role of heterogeneities in shedding concentrations on the transmission dynamics of E. coli O157 in the field.

An external file that holds a picture, illustration, etc.
Object name is zpq0020607940001.jpg

The distribution of prevalences of E. coli O157. (a) Gray bars represent observed prevalences in fecal pats sampled from cattle groups on 474 Scottish cattle farms. Pink bars show output from a stochastic simulation of the model with infection profiles for infected individuals such that 20% of the observed variance in counts arises from host-to-host variability in bacterial carriage. Best fit parameters for this model are R0 = 1.5, λ = 0.01, and α = 0.9. (b) As in a but with a restricted vertical axis to expose the tail of the distribution.

An external file that holds a picture, illustration, etc.
Object name is zpq0020607940002.jpg

The distribution of bacterial counts (cfu per gram of fecal matter) taken from 440 positive fecal pats. The limit of accurate enumeration is 100 cfu/g, and below this threshold, all counts have been set to 50 cfu·g.

As a consequence of the fact that most studies report farm level prevalences of E. coli O157 infection to be highly variable, typically comprising sporadic outbreaks, occasional high prevalences, and periods of apparent absence (2328), we view the prevalence data as a snapshot of a dynamic process. Additionally, we take a previously undescribed approach in which we underpin the transmission dynamics with a model incorporating within-host variability based on the bacterial count data, which allows infectiousness to be related to the level of pathogen excretion. Specifically, by fitting a stochastic susceptible-infected-susceptible model incorporating within-host variability in infectiousness to the prevalence data, we aim to (i) relate infectiousness to bacterial count under a range of assumptions about the relative extent of within- and between-host variability in bacterial carriage, (ii) determine how mean infectiousness varies between hosts, and (iii) evaluate the efficacy of potential control options.

Results are shown for four model scenarios in which host-to-host variability in bacterial carriage contributes 100%, 20%, 10%, or 2% of the observed variance in counts. The 95% confidence limits are indicated in parentheses.

Click here to view.

Acknowledgments

We thank Alistair Smith, Hazel Knight, Judith Evans, Geoff Foster, and David Fenlon for assistance and two anonymous reviewers for valuable discussions. This study is a part of the International Partnership Research Award in Veterinary Epidemiology, Epidemiology, and Evolution of Enterobacteriaceae Infections in Humans and Domestic Animals, funded by the Wellcome Trust. L.M. is grateful to the Wellcome Trust for a Mathematical Biology Research Training Fellowship. The Scottish Agricultural College receives financial support from the Scottish Executive Environment and Rural Affairs Department.

Acknowledgments

Notes

Conflict of interest statement: No conflicts declared.

This paper was submitted directly (Track II) to the PNAS office.

Abbreviation: cfu, colony-forming unit.

Notes
Conflict of interest statement: No conflicts declared.
This paper was submitted directly (Track II) to the PNAS office.
Abbreviation: cfu, colony-forming unit.

References

  • 1. Lipsitch, M., Cohen, T., Cooper, B., Robins, J. M., Ma, S., James, L., Gopalakrishna, G., Chew, S. K., Tan, C. C., Samore, M. H., et al. (2003) Science300, 1966–1970.
  • 2. Anderson, R. M. &amp; May, R. M. (1991) Infectious Diseases of Humans: Dynamics and Control (Oxford Univ. Press, Oxford).
  • 3. Woolhouse, M. E. J., Dye, C., Etard, J.-F., Smith, T., Charlwood, J. D., Garnett, G. P., Hagan, P., Hii, J. L. K., Ndhlovu, P. D., Quinnell, R. J., et al. (1997) Proc. Natl. Acad. Sci. USA94, 338–342.
  • 4. Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E. &amp; Getz, W. M. (2005) Nature438, 355–359. [[PubMed]
  • 5. Galvani, A. P. &amp; May, R. M. (2005) Nature438, 293–295. [[PubMed]
  • 6. Boelle, P. Y., Cesbron, J. Y. &amp; Valleron, A. J. (2004) BMC Infect. Dis.4, 26.
  • 7. Donaldson, A. I., Alexandersen, S., Sorensen, J. H. &amp; Mikkelsen, T. (2001) Vet. Rec.148, 602–604. [[PubMed]
  • 8. Hunter, N(1997) Trends Microbiol.5, 331–334. [[PubMed][Google Scholar]
  • 9. Woolhouse, M. E. J., Etard, J. F., Dietz, K., Ndhlovu, P. D. &amp; Chandiwana, S. K. (1998) Parasitology117, 475–482. [[PubMed]
  • 10. Yorke, J. A., Hethcote, H. W. &amp; Nold, A. (1978) Sex. Transm. Dis.5, 51–56. [[PubMed]
  • 11. Haydon, D. T., Chase-Topping, M., Shaw, D. J., Matthews, L., Friar, J. K., Wilesmith, J. &amp; Woolhouse, M. E. J. (2003) Proc. R. Soc. Lond. B270, 121–127.
  • 12. Matthews, L., Mckendrick, I. J., Ternent, H., Gunn, G. J., Synge, B. &amp; Woolhouse, M. E. J. (June 3, 2005) Epidemiol. Infect., 10.1017/S0950268805004590. ] [
  • 13. Borczyk, A. A., Karmali, M. A., Lior, H. &amp; Duncan, L. M. C. (1987) Lancet1, 98. [[PubMed]
  • 14. Gansheroff, L. J. &amp; O'Brien, A. D. (2000) Proc. Natl. Acad. Sci. USA97, 2959–2961.
  • 15. Synge, B. &amp; Paiba, C. (2000) Vet. Rec.147, 27. [[PubMed]
  • 16. Fegan, N., Vanderlinde, P., Higgs, G. &amp; Desmarchelier, P. (2004) J. Appl. Microbiol.97, 362–370. [[PubMed]
  • 17. Low, J. C., McKendrick, L. J., McKechnie, C., Fenlon, D., Naylor, S. W., Currie, C., Smith, D. G. E., Allison, L. &amp; Galy, D. L. (2005) Appl. Environ. Microbiol.71, 93–97.
  • 18. Ogden, I. D., MacRae, M. &amp; Strachan, N. J. C. (2004) FEMS Microbiol. Lett.233, 297–300. [[PubMed]
  • 19. Omisakin, F., MacRae, M., Ogden, I. D. &amp; Strachan, N. J. C. (2003) Appl. Environ. Microbiol.69, 2444–2447.
  • 20. Robinson, S. E., Wright, E. J., Hart, C. A., Bennett, M. &amp; French, N. P. (2004) J. Appl. Microbiol.97, 1045–1053. [[PubMed]
  • 21. Lahti, E., Ruoho, I., Rantala, L., Hanninen, M. L. &amp; Honkanen-Buzalski, T. (2003) Appl. Environ. Microbiol.69, 554–561.
  • 22. Widiasih, D. A., Ido, N., Omoe, K., Sugii, S. &amp; Shinagawa, K. (2004) Epidemiol. Infect.132, 67–75.
  • 23. Synge, B. A., Chase-Topping, M. E., Hopkins, G. F., McKendrick, I. J., Thomson-Carter, F., Gray, D., Rusbridge, S. M., Munro, F. I., Foster, G. &amp; Gunn, G. J. (2003) Epidemiol. Infect.130, 301–312.
  • 24. Zhao, T., Doyle, M. P., Shere, J. A. &amp; Garber, L. P. (1995) Appl. Environ. Microbiol.61, 1290–1293.
  • 25. Rahn, K., Renwick, S. A., Johnson, R. P., Wilson, J. B., Clarke, R. C., Alves, D., McEwen, S. A., Lior, H. &amp; Spika, J. (1997) Epidemiol. Infect.119, 251–259.
  • 26. Mechie, S. C., Chapman, P. A. &amp; Siddons, C. A. (1997) Epidemiol. Infect.118, 17–25.
  • 27. Besser, T. E., Hancock, D. D., Pritchett, L. C., McRae, E. M., Rice, D. H. &amp; Tarr, P. I. (1997) J. Infect. Dis.175, 726–729. [[PubMed]
  • 28. Shere, J. A., Bartlett, K. J. &amp; Kaspar, C. W. (1998) Appl. Environ. Microbiol.64, 1390–1399.
  • 29. Hethcote, H. W. &amp; Yorke, J. A. (1984) Gonorrhea Transmission Dynamics and Control (Springer, Berlin).
  • 30. Sanderson, M. W., Besser, T. E., Gay, J. M., Gay, C. C. &amp; Hancock, D. D. (1999) Vet. Microbiol.69, 199–205. [[PubMed]
  • 31. Wray, C., McLaren, I. M., Randall, L. P. &amp; Pearson, G. R. (2000) Vet. Rec.147, 65–68. [[PubMed]
  • 32. Cray, W. C. &amp; Moon, H. W. (1995) Appl. Environ. Microbiol.61, 1586–1590.
  • 33. Johnson, R. P., Cray, W. C. &amp; Johnson, S. T. (1996) Infect. Immun.64, 1879–1883.
  • 34. Renshaw, E(1991) Modelling Biological Populations in Space and Time (Cambridge Univ. Press, Cambridge, U.K.).[Google Scholar]
  • 35. Barndorff-Nielson, O. E. &amp; Cox D. R. (1989) Asymptotic Techniques for Use in Statistics (Chapman and Hall, London).
  • 36. Woolhouse, M. E. J., Shaw, D. J., Matthews, L., Liu W.-C., Mellor D. J. &amp; Thomas M. R. (2005) Biol. Lett.1, 350–352.
  • 37. Naylor, S. W., Low, J. C., Besser, T. E., Mahajan, A., Gunn, G. J., Pearce, M. C., McKendrick, I. J., Smith, D. G. E. &amp; Gally, D. L. (2003) Infect. Immun.71, 1505–1512.
  • 38. Rice, D. H., Sheng, H. Q. Q., Wynia, S. A. &amp; Hovde, C. J. (2003) J. Clin. Microbiol.41, 4924–4929.
  • 39. Sheng, H. Q., Davis, M. A., Knecht, H. J. &amp; Hovde, C. J. (2004) Appl. Environ. Microbiol.70, 4588–4595.
  • 40. Hancock, D., Besser, T., Lejeune, J., Davis, M. &amp; Rice, D. (2001) Int. J. Food Microbiol.66, 71–78. [[PubMed]
  • 41. Stevens, M. P., van Diemen, P. M., Dziva, F., Jones, P. W. &amp; Wallis, T. S. (2002) Microbiology148, 3767–3778. [[PubMed]
  • 42. Dean-Nystrom, E. A., Gansheroff, L. J., Mills, M., Moon, H. W. &amp; O'Brien, A. D. (2002) Infect. Immun.70, 2414–2418.
  • 43. Potter, A. A., Klashinsky, S., Li, Y. L., Frey, E., Townsend, H., Rogan, D., Erickson, G., Hinkley, S., Klopfenstein, T., Moxley, R. A., et al. (2004) Vaccine22, 362–369. [[PubMed]
  • 44. Zhao, T., Doyle, M. P., Harmon, B. G., Brown, C. A., Mueller, P. O. E. &amp; Parks, A. H. (1998) J. Clin. Microbiol.36, 641–647.
  • 45. Zhao, T., Tkalcic, S., Doyle, M. P., Harmon, B. G., Brown, C. A. &amp; Zhao, P. (2003) J. Food Prot.66, 924–930. [[PubMed]
  • 46. Kudva, I. T., Jelacic, S., Tarr, P. I., Youderian, P. &amp; Hovde, C. J. (1999) Appl. Environ. Microbiol.65, 3767–3773.
  • 47. O'Flynn, G., Ross, R. P., Fitzgerald, G. F. &amp; Coffey, A. (2004) Appl. Environ. Microbiol.70, 3417–3424.
  • 48. Besser, T. E., Richards, B. L., Rice, D. H. &amp; Hancock, D. D. (2001) Epidemiol. Infect.127, 555–560.
  • 49. Brown, C. A., Harmon, B. G., Zhao, T. &amp; Doyle, M. P. (1997) Appl. Environ. Microbiol.63, 27–32.
  • 50. Sargeant, J. M., Gillespie, J. R., Oberst, R. D., Phebus, R. K., Hyatt, D. R., Bohra, L. K. &amp; Galland, J. C. (2000) Am. J. Vet. Res.61, 1375–1379. [[PubMed]
  • 51. Sargeant, J. M., Sanderson, M. W., Smith, R. A. &amp; Griffin, D. D. (2003) Prev. Vet. Med.61, 127–135. [[PubMed]
  • 52. Foster, G., Hopkins, G. F., Gunn, G. J., Ternent, H. E., Thomson-Carter, F., Knight, H. I., Graham, D. J. L., Edge, V. &amp; Synge, B. A. (2003) J. Appl. Microbiol.95, 155–159. [[PubMed]
  • 53. Chapman, P. A., Wright, D. J. &amp; Siddons, C. A. (1994) J. Med. Microbiol.40, 424–427. [[PubMed]
  • 54. Pearce, M. C., Fenlon, D., Low, J. C., Smith, A. W., Knight, H. I., Evans, J., Foster, G., Synge, B. A. &amp; Gunn, G. J. (2004) Appl. Environ. Microbiol.70, 5737–5743.
Collaboration tool especially designed for Life Science professionals.Drag-and-drop any entity to your messages.