Type-specific human papillomavirus biological features: validated model-based estimates.

Iacopo Baussano,Joakim Dillner,Anna Gillio-Tos,Laura De Marco,Annarosa Del Mistro,Fulvio Lazzarato,K Miriam Elfström,Francesca Carozzi,Silvia Franceschi,Guglielmo Ronco,Gerardo Chowell

doi:10.1371/journal.pone.0081171

Iacopo Baussano, Joakim Dillner + Show 9 more

Open Access

https://doi.org/10.1371/journal.pone.0081171

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Journal: PloS one	Publication Date: Nov 29, 2013
Citations: 23	License type: CC BY 3.0

Affiliation: Karolinska Institutet, University of Turin

Abstract

Infection with high-risk (hr) human papillomavirus (HPV) is considered the necessary cause of cervical cancer. Vaccination against HPV16 and 18 types, which are responsible of about 75% of cervical cancer worldwide, is expected to have a major global impact on cervical cancer occurrence. Valid estimates of the parameters that regulate the natural history of hrHPV infections are crucial to draw reliable projections of the impact of vaccination. We devised a mathematical model to estimate the probability of infection transmission, the rate of clearance, and the patterns of immune response following the clearance of infection of 13 hrHPV types. To test the validity of our estimates, we fitted the same transmission model to two large independent datasets from Italy and Sweden and assessed finding consistency. The two populations, both unvaccinated, differed substantially by sexual behaviour, age distribution, and study setting (screening for cervical cancer or Chlamydia trachomatis infection). Estimated transmission probability of hrHPV types (80% for HPV16, 73%-82% for HPV18, and above 50% for most other types); clearance rates decreasing as a function of time since infection; and partial protection against re-infection with the same hrHPV type (approximately 20% for HPV16 and 50% for the other types) were similar in the two countries. The model could accurately predict the HPV16 prevalence observed in Italy among women who were not infected three years before. In conclusion, our models inform on biological parameters that cannot at the moment be measured directly from any empirical data but are essential to forecast the impact of HPV vaccination programmes.

Highlights

In year 2008, new cancer cases attributable to high-risk human papillomavirus (HPV) infection were estimated to be 610,000 [1]
Some key parameters that govern the natural history of hrHPV infection, including the probability of transmission per sexual partnership, the rate of clearance of incident infections and immune response following infection clearance, are, currently ill-defined because they cannot be inferred from empirical data
The model accounted for the effect of age and sexual behaviour, categorized in classes of sexual activity (CSA), time since infection, and different patterns of immune response to re-infection

Summary

Introduction

In year 2008, new cancer cases attributable to high-risk (hr) human papillomavirus (HPV) infection were estimated to be 610,000 [1]. Some key parameters that govern the natural history of hrHPV infection, including the probability of transmission per sexual partnership, the rate of clearance of incident infections and immune response following infection clearance, are, currently ill-defined because they cannot be inferred from empirical data. These parameters are needed, among other uses, for projecting the impact of cervical cancer control measures (vaccination and/or screening) by simulation with mathematical models, as done in some populations [8,9,10]. Transmission models have been parameterized either by imposing plausible sets of parameter values to the simulated population [11,12,13,14] or by calibration of model-based outputs against empirical sets of data [13,15,16,17,18]

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Conclusion