title: Influenza pandemic preparedness in France: modelling the impact of interventions.
abstract: BACKGROUND
  Influenza pandemics result in excess mortality and social disruption. To assist health authorities update the French pandemic plan, the authors estimated the number of health events (cases, hospitalisations, and deaths) in a pandemic and compared interventions in terms of impact and efficiency.
  
  
  METHODS
  A Monte Carlo simulation model, incorporating probability distributions of key variables, provided estimates of health events (HE) by age and risk group. Input variables were set after literature and expert consultation. The impact of targeted influenza vaccination and antiviral prophylaxis/treatment (oseltamivir) in high risk groups (elderly, chronic diseases), priority (essential professionals), and total populations was compared. Outcome measures were HE avoided, number of doses needed, and direct cost per HE avoided.
  
  
  RESULTS
  Without intervention, an influenza pandemic could result in 14.9 million cases, 0.12 million deaths, and 0.6 million hospitalisations in France. Twenty four per cent of deaths and 40% of hospitalisations would be among high risk groups. With a 25% attack rate, 2000-86,000 deaths could be avoided, depending on population targeted and intervention. If available initially, vaccination of the total population is preferred. If not, for priority populations, seasonal prophylaxis seems the best strategy. For high risk groups, antiviral treatment, although less effective, seems more feasible and cost effective than prophylaxis (respectively 29% deaths avoided; 1800 doses/death avoided and 56% deaths avoided; 18,500 doses/death avoided) and should be chosen, especially if limited drug availability.
  
  
  CONCLUSIONS
  The results suggest a strong role for antivirals in an influenza pandemic. While this model can compare the impact of different intervention strategies, there remains uncertainty surrounding key variables.
authors.affiliation: EPIET and Institut de Veille Sanitaire, France. aoife.doyle@lshtm.ac.uk
authors.forename: Aoife
authors.initials: A
authors.lastname: Doyle
authors.forename: Isabelle
authors.initials: I
authors.lastname: Bonmarin
authors.forename: Daniel
authors.initials: D
authors.lastname: Lévy-Bruhl
authors.forename: Yann
authors.initials: Y
authors.lastname: Le Strat
authors.forename: Jean-Claude
authors.initials: JC
authors.lastname: Desenclos
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000998:Antiviral Agents / Q000627:therapeutic use*; D002648:Child; D002675:Child, Preschool; D002908:Chronic Disease; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000517:prevention & control*; D005260:Female; D005602:France / Q000453:epidemiology; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007252:Influenza Vaccines / Q000627:therapeutic use; D007251:Influenza, Human / Q000453:epidemiology / Q000401:mortality / Q000517:prevention & control*; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D053139:Oseltamivir / Q000627:therapeutic use*
chemical_list: D000998:Antiviral Agents; D007252:Influenza Vaccines; D053139:Oseltamivir