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Dose‐Response Model of Coxiella burnetii (Q Fever)

Author

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  • Sushil B. Tamrakar
  • Anne Haluska
  • Charles N. Haas
  • Timothy A. Bartrand

Abstract

Q fever is a zoonotic disease caused by the intracellular gram‐negative bacterium Coxiella burnetii (C. burnetii), which only multiplies within the phagolysosomal vacuoles. Q fever may manifest as acute or chronic disease. The acute form is generally not fatal and manifestes as self‐controlled febrile illness. Chronic Q fever is usually characterized by endocarditis. Many animal models, including humans, have been studied for Q fever infection through various exposure routes. The studies considered different endpoints including death for animal models and clinical signs for human infection. In this article, animal experimental data available in the open literature were fit to suitable dose‐response models using maximum likelihood estimation. Research results for tests of severe combined immunodeficient mice inoculated intraperitoneally (i.p.) with C. burnetii were best estimated with the Beta‐Poisson dose‐response model. Similar inoculation (i.p.) trial outcomes conducted on C57BL/6J mice were best fit by an exponential model, whereas those tests run on C57BL/10ScN mice were optimally represented by a Beta‐Poisson dose‐response model.

Suggested Citation

  • Sushil B. Tamrakar & Anne Haluska & Charles N. Haas & Timothy A. Bartrand, 2011. "Dose‐Response Model of Coxiella burnetii (Q Fever)," Risk Analysis, John Wiley & Sons, vol. 31(1), pages 120-128, January.
    • Handle: RePEc:wly:riskan:v:31:y:2011:i:1:p:120-128
    DOI: 10.1111/j.1539-6924.2010.01466.x
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    1. Christel Faes & Marc Aerts & Helena Geys & Geert Molenberghs, 2007. "Model Averaging Using Fractional Polynomials to Estimate a Safe Level of Exposure," Risk Analysis, John Wiley & Sons, vol. 27(1), pages 111-123, February.
    2. Peter F. M. Teunis & Nico J. D. Nagelkerke & Charles N. Haas, 1999. "Dose Response Models For Infectious Gastroenteritis," Risk Analysis, John Wiley & Sons, vol. 19(6), pages 1251-1260, December.
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