Presentation on theme: "Department of Infectious Disease Epidemiology Imperial College London"— Presentation transcript:
1 Department of Infectious Disease Epidemiology Imperial College London The impact of vaccination on the epidemiology of infectious diseases (lecture 1)Roy AndersonDepartment of Infectious Disease EpidemiologyFaculty of Medicine,Imperial College LondonNew Delhi, India - September 19th 2011
2 Contents-Lecture 1 History. Basic epidemiological principles. Criteria for eradication.Heterogeneities.Impact of mass vaccination on epidemiological pattern.Conclusions.Epidemiccurves
3 Vaccines as a fraction of total pharmaceutical sales in total – worldwide in 2006Total sales - $ 380 billion
4 Geographical distribution of vaccine market share April 2011
5 Vaccine Trails - Stages Stage Number of people in trialPre-clinical yearsPhasePhasePhase ,000-70,000Phase ,000-1,000,000Time from proof of concept to market – of the order of 10 years or longer
13 Stochasticity & persistence Disease extinction likely by chance when number of infectives falls to very low numbers.- so extinction more frequent as population size decreases and cycle amplitudes increase.
14 Critical community size Minimum population size at which measles fadeouts(proportion of weeks with no cases) become rare.TRUE
15 Seasonality in transmission of measles – school holidays Confidencebar
16 HIV-1 prevalence in Nairobi, Kenya 1981-1999 – stratified by risk group % infected with HIV-1Sex workersMen with GUDPregnant womenSex workersMen with GUDPregnant womenYear
17 Daniel Bernoulli was one of a number of early mathematicians Daniel Bernoulli to 1782D. Bernoulli (1760)used a simple mathematical model to evaluate the effectiveness of variolation to protect against smallpox.Daniel Bernoulli was one of a number of early mathematicianswho turned their skills to probability problems raised by gamblers - perhaps at the card tables in Monte Carlo(e.g. C. Huyghens 1657).
18 Basic principles in Infectious Disease Epidemiology The key determinant of incidence and prevalence of infection is the basic reproductive number Ro.Ro measures the average number of secondary cases generated by one primary case in a susceptible populationMany factors determine its magnitude, including those that influence the typical course of infection in the patient and those that determine transmission between people.
19 Basic Reproductive number, Ro Chains of transmission between hostsGeneration12345678Number InfectedR1.51.331.16R0 Basic reproduction numberaverage no. of secondary casesgenerated by 1 primary case in asusceptible populationRt Effective reproduction numbernumber of infections caused by each new case occurring at time, tThe key determinant of incidence and prevalence of infection is the basic reproductive number Ro.Many factors determine its magnitude, including those that influence the typical course of infection in the patient and those that determine transmission between people.
20 exhaustion of susceptibles Equilibrium, or recurrent epidemics The epidemic curveRate of new infectionsestablishmentexponentialgrowthTimeexhaustion of susceptiblesendemicityEquilibrium, or recurrent epidemicsy e(R0-1)/TG tRandom (stochastic)effects
21 Typical course of infection within the host Estimation of average latent & infectious periods plus distributions
22 Viral load over course of infection as a function of patient age (influenza A - viral titre AUC versus age)900Line of regressionOsterhaus, Oxford, Jackson, & Ward (2001)95% confidence limits for mean predicted value800700600Viral titre AUC (log10 TCID50h/mL)5004003002001001020304050607080Age (years)90100
23 The typical course of infection SARS CoVInfluenza AViral loadSymptomscore* Hayden et al. (1998) J. Clin Invest 101 p643Experimental human influenza A/Texas/36/91 (H1N1)intranasal inoculation 105 dosePeiris et al (2003)
24 Basic principles in Infectious Disease Epidemiology The magnitude of Ro varies according to location and population - it is strongly influenced by birth rate, population density and behavioural factors.The magnitude of Ro can be ascertained by cross sectional serological surveys.
25 What is Herd Immunity?The impact of the fraction immune in the community on the per capita rate of transmission of an infectious agent.The level of herd immunity can be measured by reference to the magnitude of reduction in the value of Ro.
26 How can the degree of herd immunity and the magnitude of Ro be assessed? Cross-sectional and longitudinal serological surveys.Serum and saliva (viral infections).Activated T cells (bacteria and protozoa)?Quantitative assays.
27 Scientific methods in the study of herd immunity Immunological and Disease Surveillance methods provide the empirical base for analysis and interpretation.Mathematical & statistical methods play an important role in the analysis of infectious disease transmission and control.They help to define both what needs to be measured, and how best to measure define epidemiological quantities.
28 Age-specific serology Age in YearsProportion Seropositive2.557.51012.51517.52010.90.80.70.18.104.22.168.20.1Maternal AntibodiesAntibodies from InfectionAge-specific serologyUsed to calculate the average ageat infection, A, the averageduration of maternal antibodyprotection, M, and the degreeof herd immunity
30 Force or rate of infection λ and the average age of infection A – estimation from serology (1) If the proportion seropositive at age a is p(a), then the average force or rate of infection λ over the interval 0 to a years of age is given by: λ(a)=p(a)/[L(1-e(-a/L))]; eq(1) where L is life expectancy. Other relationships of importance are: A≈1/λ; eq(2) R0≈L/A; eq(3) and pc=(1-1/R0)=(1-A/L). eq(4) Here R0 is the basic reproductive number (average number of secondary cases generated by one primary case, and pc is the critical fraction to be vaccinated to block transmission.
31 Force or rate of infection λ and the average age of infection A – estimation from serology (2) A simple worked example is as follows. Consider measles where 70% of children are seropositive at age 3 years (p(3)=0.7), and life expectancy is 60 years. Using the equations in the previous slide:from eqn(1) λ = yr-1;the average age at infection, A = 4.18 years;R0 = 14.35; andpc = 0.93, or 93% coverage.In reality many complications slightly alter these values. One important example is maternal antibodies which have a half life of 6 months in well nourished mothers who breast feed. This value (0.5 yrs) should be subtracted from a in the calculation of λ – which gives a required coverage of 94.2% (see Anderson & May (1991) for fuller discussion)
32 The calculation of the magnitude of Ro A series of simple relationships exist between keyepidemiological, demographic and vaccinationprogramme related parameters.The magnitude of Ro and the average at infectionprior to mass vaccination, A, plus life expectancyin the population are related as followsHere, M is the average duration of maternal antibodyprotection (6 months) and L is life expectancy
33 Force or rate of infection λ and the average age of infection A – estimation from serology (3) Some examples of studies from which the data employed in the previous calculations can be derived. Serology for Rubella virus in the UK (see Anderson & May (1991))Decay in maternal antibodiesLongitudinal and cross-sectional serological surveys
34 Mumps Virus - Age-specific change in the per capita rate of infection (Anderson & May 1983)2.521.5Force of infection/annum10.50.5-23-56-89-1415+Age in years
35 Human Parvovirus B19 serology – force of infection (FOI) by age Mossong et al (2008) Parvovirus B19 infection in five European countries: seroepidemiology, force of infection and maternal risk of infection. Epidemiol. Infect. (2008), 136, 1059–1068BelgiumEnglandFinlandPoland% SeropositiveForce of Infection (FOI)ItalyAge (years)
36 Parameters – Who mixes with Whom Wallinga J, Teunis P, Kretzschmar M. (2006) Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents. American Journal of Epidemiology 164, 936–944.Beutels P, et al. (2006) Social mixing patterns for transmission models of close contact infections : exploring self-evaluation and diary-based data collection through a web-based interface. Epidemiology and Infection. 134, 1158–1166.Edmunds WJ, O’Callaghan CJ, Nokes DJ. (1997) Who mixes with whom? A method to determine the contact patterns of adults that may lead to the spread of airborne infections. Proceedings of the Royal Society of London, Series B: Biological Sciences 264, 949–957.Farrington CP. (1990) Modelling forces of infection for measles, mumps and rubella. Statistics in Medicine 9, 953–967.
37 Average age, A, at infection prior to immunisation Average age at infection, ALocation/time periodMeasles virus5-6 years2-3 yearsUSABangkok, Thailand 1967Rubella virus9-10 yearsSweden 1965Varicella virus6-8 yearsUSAPolio virus12-17 yearsUSAMumps virus7-8 yearsEngland & Wales 1975Smallpox virus10-15yearsBangladesh 1940
38 Estimates of the basic reproductive number, Ro InfectionLocationTime periodRoMeaslesEnglandCanada13-1511-13VaricellaUSA19437-8MumpsNetherlands11-14RubellaWest Germany6-7Polio19555-6HIV-1Nairobi, Kenya(sex workers)11-12SmallpoxBangladesh19404-6Influenza A (H1N1)20101-1.5
39 Estimation of R0 from past influenza A epidemics
40 SARS - distribution of R Rapid SARS Virus Spreadin Flats 7 and 8, Block EAmoy Garden, Hong Kong.1st March 2003 –292 people infected by oneindex caseSuper spreadingevents (person,place & setting)
41 The generation of secondary cases with vaccination 12345678Number InfectedR1.51.331.250.2Vaccinatedindividuals
42 Basic principles in Infectious Disease Epidemiology The magnitude of pc , the fraction of each birth cohortthat must be immunised to block transmission is given bythe following simple expression:,The parameter b is the average age at first vaccination,L is life expectancy (related to the net birth rate), and A is the average age at infection prior to mass immunisation. Vaccine efficacy = ε, ranging from 0-1.
44 Mass vaccination Anderson, et al (1998) Lancet 350:1466-1470 0.950.960.970.980.991Fraction that must be vaccinated toblock transmission (A=5 years)Q=0.97Q=0.98Fraction f vaccinatedMaximumvaccineefficacy QQ=1.00.511.522.533.544.55Average age at Vaccination V (years)Vaccine efficacy changes with age due topresence of maternal antibodies
45 Age at vaccination - good and bad patterns Poor pattern - spreadto the right side ofthe optimal ageGood pattern - clusteredaround the optimal age
46 Predictions based on mathematical models of transmission and the impact of mass vaccination1) Increase in average age at infection2) Increase in the inter-epidemic period3) Toughs in susceptibility in the herdimmunity profile4) Changes in the age distribution ofinfection and serious disease5) Non-linear relationship between theincidence of infection and disease andvaccine uptake
47 Average age of infection - the impact of vaccination prior to vaccinationset at 5 years.Average age ofvaccination setat 2 years.
48 Vaccination - and the age distribution of infection
50 Herd immunity profile - across age classes and through time (Anderson and May, 1982; Science 215: ).Measles - serology post the introduction of a cohortbased vaccination programme
51 Age stratified & longitudinal serology for Rubella antibodies Age stratified & longitudinal serology for Rubella antibodies. Finland<1m1-m6-m1-2-4-6-8-10-12-14-16-20-25-30-40->600.20.40.60.811979198119831985198719891991(Ukkonen et al, 1995)10.8Fraction seropositive0.6Fraction seropositive0.41991FemaleMale19890.219871985198319811979<1m6-m2-6-10-14-20-30->601-m1-4-8-12-16-25-40-AgeAge
52 Mass vaccination can increase the incidence of serious disease if the likelihood riseswith age per case of infection(Anderson & May, 1983; J. Hygiene 90: )The case of rubella and congenitalrubella syndrome
53 Percentage gain from the indirect effects of herd immunity Criticalvaccinationcoverageto blocktransmission,pc
54 Ferguson et al, 2006 – Nature - on line April 27th 2006 200 days compressedinto a few secondsFerguson et al, 2006 – Nature - on line April 27th 2006Individual based stochasticsimulation model with threescales of mixing – extensivesensitivity analysis and analysisof past influenza epidemics
55 ConclusionsEradication difficult when Ro large and population density plus net birth rate high.Heterogeneity in population density and vaccine coverage important.Carrier state important as are reservoir hosts (if involved).Mathematical & computational methods permit analytical & simulation studies of potential impact of different strategies.Cost benefit studies need to take account of indirect effects of mass vaccination on transmission.Vaccine coverage must be maintained at high levels to avoid the immigration of infectives stimulating epidemics in susceptible pockets.Imperfect vaccines – phase IV monitoring of vital importance.Multi- strain systems – more research required – will new strains replace those targeted by a multi-valent vaccine?
57 rubella prior to immunisation. Mass vaccination programmes for infections where the incidence of serious disease rises with age at infection (e.g. Rubella)Rubella. The equilibrium ration of rubellacases in pregnant women in the age range16-40 years after vaccination of a proportion p)the horizontal axis). At age V years divided bycases before vaccination. The fertility functionused to derive this graph is displayed below.The vaccination policy adopted is for vaccinationof boys and girls around one year of age (V=1).A is the average age ofinfection in years forrubella prior to immunisation.
58 Rubella vaccination programmes in the UK – change in policy (Anderson & May, 1983) The ratio of cases of infection in16-40 year old women after vaccinationdivided by before for tow different policies – vaccinated boys and girls at 2 years of age, and vaccinating girls only at12 years of age. The dark shaded regions denote where a policy could make matters worse. In the UK with coverage above 80% no problem arises
59 Spatial de-correlation induced by vaccination - measles Viewing de-correlationDensity of casesSpatial locationEpidemics initially highly synchronised before vaccination, then increasingly de-correlated post-vaccination.
60 Model design for individual based stochastic simulations High performance, object-oriented code. Intended to be scaleable to allow 000s of model simulations of 60 million population to be performed.Computationally intensive (>5GB memory use for 60 million).Three levels of population structure:Flexible modelling of disease biology (arbitrary distributions, number of disease stages), and interventions (ring vaccination, mass vaccination, quarantine, anti-viral treatment, movement constraints).ProbabilityDistanceHouseholdNetwork(disease specific)Spatial
61 Spatial kernels – mobile phone data – frequency versus – distanced moved per defined time unit Number of peopleDistance metres
62 Data needs: detailed population data: Landscan (Oakridge Natl. Lab.) Need both population data (density, age, household)and mixing/travel data.Population density – based on satellite imagery
63 HIV-1 vaccinesAt present, there are many ongoing HIV-1 vaccine trials and several pending for 2006, involving different vaccines (of which some are DNA-based, some use recombinant viral vectors, 4 are protein subunits and 3 are lipopeptide-based).Most are in Phase I, with VaxGen’s two trials (in North America and Thailand) the only Phase III studies completed. Both Phase III trials were completed in 2003, with efficacy data revealing no difference in infection in treated and untreated arms.The majority use clade B strains, but a few candidates (especially among the newer ones in the pipeline) are based on clades A, C, D and E (
64 Eradication criterion – protective vaccines pc is the critical fraction of each cohort immunised, R0 is the basic reproductivenumber, L is life expectancy, V is the duration of vaccine protection and ε isvaccine efficacy
65 Imperfect vaccinesVaccinated individuals acquire infection but show slower progression to AIDS.Slower progression is linked to lower viral loads – especially in the primary HIV-1 infection phase.Lower viral load is linked to lowered infectiousness to susceptible sexual partners.Vaccination may be linked to increased risk behaviours.
66 Eradication criterion – imperfect vaccines pc is the critical fraction of each cohort immunised, R0 is the basic reproductivenumber for unvaccinateds, R0v is the reproductive numbers for vaccinateds,L is life expectancy, V is the duration of vaccine protection and ε isvaccine efficacy
67 Illustrative schematic diagram, in two dimensions, of a multidimensional parameter space denoting the six possible equilibrium states that can exist for different epidemiological and vaccine property related parameter combinations (Anderson & Hanson, 2005, JID).Vaccination & good outcomeof increased HIV-1 infectionbut increased population size(yv*>y*, Nv*>N*)Vaccination & perverse outcomeof increased HIV-1 infection& decreased population size(yv*>y*, Nv*<N*)Vaccination & good outcomeof decreased HIV-1 infection& increased population size(yv*<y*, Nv*>N*)No persistenceof HIV-1 infection(Ro<1)Vaccination & eradicationof HIV-1 infection(yv*=0)No vaccination & endemic persistenceof HIV-1 infection(Ro>1, p=0)