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Impact of Acquired Immunity and Dose‐Dependent Probability of Illness on Quantitative Microbial Risk Assessment

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  • A. H. Havelaar
  • A. N. Swart

Abstract

Dose‐response models in microbial risk assessment consider two steps in the process ultimately leading to illness: from exposure to (asymptomatic) infection, and from infection to (symptomatic) illness. Most data and theoretical approaches are available for the exposure‐infection step; the infection‐illness step has received less attention. Furthermore, current microbial risk assessment models do not account for acquired immunity. These limitations may lead to biased risk estimates. We consider effects of both dose dependency of the conditional probability of illness given infection, and acquired immunity to risk estimates, and demonstrate their effects in a case study on exposure to Campylobacter jejuni. To account for acquired immunity in risk estimates, an inflation factor is proposed. The inflation factor depends on the relative rates of loss of protection over exposure. The conditional probability of illness given infection is based on a previously published model, accounting for the within‐host dynamics of illness. We find that at low (average) doses, the infection‐illness model has the greatest impact on risk estimates, whereas at higher (average) doses and/or increased exposure frequencies, the acquired immunity model has the greatest impact. The proposed models are strongly nonlinear, and reducing exposure is not expected to lead to a proportional decrease in risk and, under certain conditions, may even lead to an increase in risk. The impact of different dose‐response models on risk estimates is particularly pronounced when introducing heterogeneity in the population exposure distribution.

Suggested Citation

  • A. H. Havelaar & A. N. Swart, 2014. "Impact of Acquired Immunity and Dose‐Dependent Probability of Illness on Quantitative Microbial Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 34(10), pages 1807-1819, October.
    • Handle: RePEc:wly:riskan:v:34:y:2014:i:10:p:1807-1819
    DOI: 10.1111/risa.12214
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    References listed on IDEAS

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    1. Maarten J. Nauta & Wilma F. Jacobs‐Reitsma & Arie H. Havelaar, 2007. "A Risk Assessment Model for Campylobacter in Broiler Meat," Risk Analysis, John Wiley & Sons, vol. 27(4), pages 845-861, August.
    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.
    3. Emilie Rieu & Koenraad Duhem & Elisabeth Vindel & Moez Sanaa, 2007. "Food Safety Objectives Should Integrate the Variability of the Concentration of Pathogen," Risk Analysis, John Wiley & Sons, vol. 27(2), pages 373-386, April.
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    1. Eric G. Evers & Martijn Bouwknegt, 2016. "Combining QMRA and Epidemiology to Estimate Campylobacteriosis Incidence," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1959-1968, October.

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