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PLoS One. 2013 Nov 29;8(11):e81171. doi: 10.1371/journal.pone.0081171. eCollection 2013.

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

Author information

  • 1International Agency for Research on Cancer, Lyon, France.
  • 2Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • 3Unit of Cancer Epidemiology, Department of Medical Sciences, University of Turin, Turin, Italy.
  • 4Institute for Cancer Study and Prevention (ISPO), Florence, Italy.
  • 5Molecular Oncological and Diagnostic Immunology, Venetian Oncology Institute (IOV), Padova, Italy.
  • 6Unit of Cancer Epidemiology, Centre for Cancer Prevention, Turin, Italy.


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.

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