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J. Haffernan's presentation J. Haffernan's presentation Presentation Transcript

  • H1N1: media coverage and waves of infection Practicum Course Presentation
  • Infectious Disease Modelling http://en.wikipedia.org/wiki/Image:Tomandjerrywithmeasles.JPG
  • Mathematical Modelling of Infectious Diseases
    • Epidemiology
      • Spread of disease in a population
    • Why?
    • Immunology
      • Spread of an infection in a host
  • Introduction
    • H1N1 (S-OIV) Influenza pandemics is now one of the recent threats to the human populations. Vast amount of time and resources of nations are invested for planning the pandemic preparedness.
    • Public health officials are interested in determining whether a third wave of H1N1 will occur, whether H1N1 will reassort with seasonal influenza, and whether H1N1 will replace a seasonal influenza strain?
    • All of this depends on what we do during the previous waves of the pandemic and during regular flu season
  • Past Pandemics waves
    • 1918 pandemic - mild first wave in summer with substantial morbidity, low mortality, followed by two severe waves the following winter.
    • 1957 pandemic - three waves in US, with notable excess mortality in the nonsuccessive winter seasons between 1959 and 1962.
    • 1968 pandemic - a mild first flu season in Eurasia, with the full effects on morbidity and mortality in the second season of pandemic virus circulation.
    • Reasons for multiple waves of varying impact are not precisely understood, but they probably include adaptation of the virus to its new host, demographic or geographic variation, seasonality and the overall immunity of the population .
    NEJM 360; 25 (2009)
  • Surveillance –Canada (April 30-August 22) PHAC, FluWatch
  • What affects influenza?
    • We want to develop a model that will help us determine effective control strategies that will help our population reduce deaths and infection, and hopefully, reduce infection in the next wave of influenza (or season of influenza)
  • What affects influenza?
    • Antivirals
    • Vaccination
    • Evolution
    • Social behaviour
    • Travel
    • Media coverage
  • Antivirals
    • Early administration of antivirals increases the benefits of treatment
    • In fact it is assumed that the length of illness is reduced by 3.1 days (in average) if the drug is applied on Phase I [0-24 hrs], where as the length of illness is reduced by 1.5 days (in average) if the drug is applied on Phase II [24-48 hrs].
  • Antivirals
    • Pre-exposure prophylaxis
      • Given to high risk individuals before they are exposed
    • Post-exposure prophylaxis
      • Given to exposed individuals
    • But do you really know when an individuals has been exposed – contract tracing…
    • Stockpile – limited number of drugs
    • May not be effective
      • If you don’t give it early enough in infection
      • Flu strain may be resistant to antivirals
  • Antivirals
  • Vaccination
    • Vaccination induces immunity in individuals (if vaccine takes)
    • Does it induce only partial immunity?
    • You can still get infected
      • May not show symptoms
      • May transmit disease
    • Immunity wanes over time
  • Vaccination
    • What is the rate of vaccine uptake?
    • Can we induce herd immunity?
    • Are those who are getting the vaccine the people that need it most?
      • Some people have immunity from a previous infection.
    • What happens if the vaccine strain is not ‘close’ enough to the circulating strain?
  • Vaccination
  • Evolution
    • The flu is constantly mutating
    • So, immunity gained from a previous infection may not be fully effective when you next are exposed to the virus
    • The flu may also evolve so that antiviral are not effective
    • Evolution may also increase transmissibility (important for pandemic strains!)
  • Evolution
  • Social Behaviour
    • People may change their behaviour during an outbreak (or influenza season)
      • Wash hands more often, use hand sanitizer
      • Go to public places less
      • Stay home if you think that you are sick with flu
    • These will help reduce the probability of transmission, but how effective are these strategies if we need to go to school and work everyday?
    • Children are disease vectors – school, daycare?
  • Social Behaviour
  • Travel
    • People may travel less (social behaviour)
    • Travel also enables the transportation of the virus
      • i.e. H1N1 started in Mexico and came to Canada via infected people who were vacationing in infected areas
    • Even if travel is greatly reduced a country can still get cases
      • i.e. high infected areas, low vaccination coverage
  • Travel N Engl J Med. 2009 Jul 9;361(2):212-4. Epub 2009 Jun 29. Spread of a novel influenza A (H1N1) virus via global airline transportation. Khan K , Arino J , Hu W , Raposo P , Sears J , Calderon F , Heidebrecht C , Macdonald M , Liauw J , Chan A , Gardam M .
  • Media Coverage
    • Media coverage can drastically affect disease outcomes in a population
      • Affects social behaviours
    • At the beginning media coverage is high and then it decreases
      • May increase again when a new vaccine or drug therapy is introduced
    • Positive media coverage vs. negative coverage
    • But media is also infectious
    • Game theory
  • Media Coverage
    • H1N1 overplayed by media, public health: MDs
    • Canada has more H1N1 vaccine than most countries
    • US VP Joe Biden
    • The media and the flu (Nov 2009, report, The National)
    • Vaccine side effects
  • Media Coverage
  • Mathematical modelling
    • All of the above can be included in a mathematical model of influenza in a population
    • Which are more important?
      • You can study this by focusing on them individually and then in a combination
    • Media coverage will affect many of the above…
  • SEIR model
    • An SEIR model will be the basis of the study
    • S – Susceptible
    • E – Exposed
    • I – Infectious
      • Definitions may vary in the literature
        • Infected, infectious (able to transmit)
    • R – Recovered or Removed
  • Model assumptions
    • Should I include birth and death?
    • Should I include a disease induced death rate?
    • How do I model a limited stockpile of antivirals?
    • How do I model vaccine uptake
      • Continuous? Impulsive?
      • Game theory?
    • How does the rate of evolution change with the use of antivirals and vaccination?
    • Metapopulation? Network?
    • What variables should depend on media coverage?
  • Basic Reproductive Ratio
    • R0
      • The number of infected individuals produced by one infected individual introduced into a population of susceptibles
    • A fundamental concept in both within-host and epidemiological models of pathogen dynamics
    • Used to:
      • Gauge risk of a novel pathogen
      • Estimate degree of control necessary to contain and outbreak
  • Basic Reproductive Ratio
    • If R0<1 what happens?
      • Pathogen will die out
    • If R0>1 what happens?
      • Pathogen will grow
    • Herd Immunity
      • Critical vaccination threshold
        • The vaccination campaign goal is to reduce R0<1
        • How many people need to be vaccinated to reduce R0=1?
  • SIR model S (susceptible) I (infected) R (recovered)    d d d What do these mean in terms of the biology?
  • Calculation of R 0
    • Many different methods
    • Here are three that are most commonly used:
      • Jacobian and stability conditions
        • Eigenvalues
      • Next Generation Method
        • Spectral radius
      • Survivor Function Method
        • Probability and hazard rate
    • Be careful – these may not agree!!!
  • Goals of project
    • Next wave
    • What affects the size of the next wave?
      • Does it coincide with regular flu season?
    • You have to include the effects of media coverage.
  • Important references
    • Modelling and influenza literature
    • PHAC FluWatch
    • Media reports