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Modelling as a tool for planning vaccination policies

Modelling as a tool for planning vaccination policies. Kari Auranen Department of Vaccines National Public Health Institute, Finland Department of Mathematics and Statistics University of Helsinki, Finland. Outline. Basic concepts and models dynamics of transmission

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Modelling as a tool for planning vaccination policies

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  1. Modelling as a tool for planning vaccination policies Kari Auranen Department of Vaccines National Public Health Institute, Finland Department of Mathematics and Statistics University of Helsinki, Finland

  2. Outline • Basic concepts and models • dynamics of transmission • herd immunity threshold • basic reproduction number • herd immunity and critical coverage of vaccination • mass action principle

  3. Outline continues • Heterogeneously mixing populations • more complex models and new survey data to learn about routes of transmission • Use of models in decision making • example: varicella vaccinations in Finland • Ude of models in planning contaiment strategies • example: a simulation tool for pandemic influenza

  4. Basic concepts and models

  5. A simple epidemic model (Hamer, 1906) • Consider an infection that • involves three “compartments” of infection: • proceeds in discrete generations (of infection) • is transmitted in a homogeneously mixing (“everyone meets everyone”) population of size N Susceptible Case Immune

  6. Dynamics of transmission • Numbers of cases and susceptibles at generation t+1 • C = R * C * S / N • S = S - C + B t + 1 0 t t t+1 t t+1 t S = number of susceptibles at time t (i.e. generation t) C = number of cases (infectious individuals) at time t B = number of new susceptibles (by birth) t t t

  7. Dynamics of transmission Epidemic threshold : S = N/R e 0

  8. Epidemic threshold S e • S - S = - C + B • the number of susceptibles increases when C < B • decreases when C > B • the number of susceptibles cycles around the epidemic threshold S = N / R • this pattern is sustained as long as transmission is possible t+1 t t+1 t t+1 t t t+1 e 0

  9. Epidemic threshold • C / C = R x S / N = S / S • the number of cases increases when S > S • decreases when S < S • the number of cases cycles around B (influx of new susceptibles) t+1 t 0 t t e e e t

  10. Herd immunity threshold • incidence of infection decreases as long as the proportion of immunes exceeds the herd immunity threshold • H = 1- S / N • a complementary concept to the epidemic threshold • implies a critical vaccination coverage e

  11. Basic reproduction number (R ) 0 • the average number of secondary cases that an infected individual produces in a totally susceptible population during his/her infectious period • in the Hamer model : R = R x 1 x N / N = R • herd immunity threshold H = 1 - 1 / R • in the endemic equilibrium: S = N / R , i.e., 0 0 0 0 e 0 R x S / N = 1 e e 0 0

  12. Basic reproduction number (2) R = 3 0

  13. Basic reproduction number (3) R = 3 endemic equilibrium 0 R x S / N = 1 0 e

  14. Herd immunity threshold and R 0 H = 1-1/R 0 (Assumes homogeneous mixing)

  15. Effect of vaccination Hamer model under vaccination S = S - C + B (1- VCxVE) Vaccine effectiveness (VE) x Vaccine coverage (VC) = 80% t t+1 t+1 Epidemic threshold sustained: S = N / R e 0

  16. Mass action principle • all epidemic/transmission models are variations of the use of the mass action principle which • captures the effect of contacts between individuals • uses an analogy to modelling the rate of chemical reactions • is responsible for indirect effects of vaccination • assumes homogenous mixing • in the whole population • in appropriate subpopulations (defined by usually by age categories)

  17. The SIR epidemic model • a continuous time model: overlapping generations • permanent immunity after infection • the system descibes the flow of individuals between the epidemiological compartments • uses a set of differential equations Susceptiple Infectious Removed

  18. The SIR model equations = birth rate = rate of clearing infection = rate of infectious contacts by one individual = force of infection

  19. Endemic equilibrium (SIR) N = 10,000 = 300/10000 (per time unit) = 10 (per time unit) = 1 (per time unit) 0

  20. The basic reproduction number • Under the SIR model, Ro given by the ratio of two rates: • R = = rate of infectious contacts x • mean duration of infection • R not directly observable • need to derive relations to observable quantities 0 0

  21. Force of infection • the number of infective contacts in the population per susceptible per time unit: • (t) = x I(t) / N • incidence rate of infection: (t) x S(t) • endemic force of infection = x (R - 1) 0

  22. Estimation of R 0 Relation between the average age at infection and R (SIR model) 0 = 1/75 (per year)

  23. A simple alternative formula • Assume everyone is infected at age A • everyone dies at age L (rectangular age distribution) Proportion 100 % Susceptibles Immunes Proportion of susceptibles: S / N = A / L R = N / S = L / A e A L e 0 Age (years)

  24. Estimation of and Ro from seroprevalence data 1) Assume equilibrium 2) Parameterise force of infection 3) Estimate 4) Calculate Ro Ex. constant Proportion not yet infected: 1 - exp(- a) , estimate = 0.1 per year gives reasonable fit to the data

  25. Estimates of R 0 * * * * Anderson and May: Infectious Diseases of Humans, 1991

  26. Critical vaccination coverage to obtain herd immunity • Immunise a proportion p of newborns with a vaccine that offers complete protection against infection • R = (1-p) x R • If the proportion of vaccinated exceeds the herd immunity threshold, i.e., if p > H = 1-1/R , infection cannot persist in the population (herd immunity) vacc 0 0

  27. Critical vaccination coverage as a function of R0 p = 1 – 1/R0

  28. Indirect effects of vaccination • If p < H = 1-1/R , in the new endemic equilibrium: • S = N/R , = (R -1) • proportion of susceptibles remains untouched • force of infection decreases 0 e 0 vacc vacc e

  29. Effect of vaccination on average age A’ at infection (SIR) • Life length L; proportion p vaccinated at birth, complete protection • every susceptible infected at age A Susceptibles S / N = (1-p) A’/L S / N = A/ L => A’ = A/(1-p) i.e., increase in the average age of infection Proportion e 1 e Immunes p A ’ L Age (years)

  30. Vaccination at age V > 0 (SIR) • Assume proportion p vaccinated at age V • Every susceptible infected at age A • How big should p be to obtain herd immunity threshold H Proportion H = 1 - 1/R = 1 - A/L H = p (L-V)/L => p = (L-A)/(L-V) 1 Susceptibles p i.e., p bigger than when vaccination at birth Immunes V A L Age (years)

  31. Modelling transmission in a heterogeneously mixing population

  32. More complex mixing patterns • So far we have assumes (so called) homogeneous mixing • “everyone meets everyone” • More realistic models incorporate some form of heterogeneity in mixing (“who meets whom”) • e.g. individuals of the same age meet more often each other than individual from other age classes (assortative mixing)

  33. Example: WAIFW matrix • structure of the Who Acquires Infection From Whom matrix for varicella , five age groups (0-4, 5-9, 10-14, 15-19, 20-75 years) table entry = rate of transmission between an infective and a susceptible of respective age groups e.g., force of infection in age group 0-4: a*I1 + a*I2 + c*I3 + d*I4 + e*I5 I1 = equilibrium number of infectives in age group 0-4, etc.

  34. POLYMOD contact survey • Records the number of daily conversations in study participants in 7 European countries • Use the number of contacts between individuals from different age categories as a proxy for chances of transmission • Is currently being used to aid in modelling the impact of varicella vaccination in Finland

  35. POLYMOD contact survey: the mean number of daily contacts

  36. POLYMOD contact survey: numbers of daily contacts

  37. POLYMOD contact survey:where and for how long

  38. Use of models in policy making • Large-scale vaccinations usually bring along indirect effects • the mean age at disease increases • population immunity changes • Population-level experiments are impossible • Need for mathematicl modelling • to predict indirect effects of vaccination • to summarise the epidemiology of the infection • to identify missing data or knowledge about the natural history of the infection

  39. References • Fine P.E.M, "Herd immunity: History, Theory, Practice", Epidemiologic Reviews, 15, 265-302,1993 • Fine P.E.M., "The contribution of modelling to vaccination policy, Vaccination and World Health, Eds. F.T. Cutts and P.G. Smith, Wiley and Sons, 1994. 3 Nokes D.J., Anderson R.M., "The use of mathematical models in the epidemiological study of infectious diseases and in the desing of mass immunization programmes", Epidemiology and Infection, 101, 1-20, 1988 • Anderson R.M. and May R.M., ”Infectious Diseases of Humans”; Oxford University Press, 1992. • Mossong J et al, Social contacts and mixing patterns relevant to the spread of infectious diseases: a multi-country population-based survey, Plos Medicine, in press • Duerr et al, Influenza pandemic intervention planning using InfluSim, BMC Infect Dis, 2007

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