Presentation on theme: "Infectious Disease Epidemiology and Transmission Dynamics"— Presentation transcript:
1Infectious Disease Epidemiology and Transmission Dynamics Ann BurchellInvited lecture EPIB 695McGill UniversityApril 3, 2007
2ObjectivesTo understand the major differences between infectious and non- infectious disease epidemiologyTo learn about the nature of transmission dynamics and their relevance in infectious disease epidemiologyUsing sexually transmitted infections as an example,to learn about the key parameters in transmission dynamicsto appreciate the use of mathematical transmission models to assess the impact of prevention interventions (e.g., vaccines).
3Infectious disease epidemiology Definition of infectious disease (Last, 1995)“An illness due to a specific infectious agent or its toxic products that arises through transmission of that agent or its products from an infected person, animal, or reservoir to a suceptible host, either directly or indirectly through an intermediate plant or animal host, vector, or the inanimate environment”
4Prevalence affects incidence, a case can be a risk factor How is infectious disease (ID) epidemiology different from non-ID epidemiology?Prevalence affects incidence, a case can be a risk factorPrevalence not just a measure of burden of disease in a population, but also the probability of encountering an infected personMeans contact patterns between people are criticalPeople can be immune
5Some key terms to describe individuals Susceptible: uninfected, but able to become infected if exposedInfectious: infected and able to transmit the infection to other susceptible individualsImmune: possessing cell-mediated or humoral antibody protection against an infectionDiseased/clinical infection: implies the presence of clinical signs of pathology (not synonymous with infected)Latent infection / subclinical infection: implies presence of infectious agent but absence of clinical diseaseCarrier: implies a protracted infected state with shedding of the infectious agent. Carriers may be diseased, recovering, or healthy.
6Key time periods for an infectious disease Incubation period: extends from the moment a person is infected until they develop symptoms of diseaseSerial interval (or generation time): for diseases that are spread person-to-person, it is the time period between the appearance of symptoms in successive generationsInfectious period: time period during which a person can transmit the infectionLatent period: time period from infection until the infectious period startsGiesecke, J. Modern Infectious Disease Epidemiology
7Some key terms to describe the infectious disease at the population level Epidemic: The occurrence in a community or region of cases of an illness clearly in excess of normal expectancyOutbreak: An epidemic limited to localized increase in the incidence of a diseaseEndemic: The constant presence of a disease or infectious agent within a given geographic area or population groupPandemic: An epidemic occurring over a very wide area, crossing international boundaries and usually affecting a large number of peopleLast, JM. A Dictionary of Epidemiology
8Examples of transmission routes Direct transmissionIndirect transmissionMucous membrane to mucous membrane – sexually transmitted diseasesWater-borne – hepatitis AAcross placenta – toxoplasmosis“Proper” air-borne – chicken poxTransplants, including blood – hepatitis BFood-borne – salmonellaSkin to skin – herpes type IVectors – malariaSneezes, coughs - influenzaObjects/fomites – scarlet fever (e.g. toys in a day care centre)Giesecke J. Modern Infectious Disease Epidemiology p. 16
9Reproductive rate, R Also called “reproductive number” Average number of new infections caused by 1 infected individualIn an entirely susceptible populationBasic reproductive rate, R0In a population where <100% are susceptibleEffective reproductive rate, R = proportion susceptible x R0
11Determinants of R0For a pathogen with direct person-to-person transmissionR0 = βcDwhere β is the probability of transmission per contact between infected and susceptible personsc is the contact rateD is the duration of infectivityWhat examples of factors affecting these 3 components can you think of?Some examples…β: handwashing, condoms, face masks, sterilization of medical instruments, weakened immunity (e.g. due to age, other illness, immunosuppressive drugs)c: population density (urban/rural, schools, daycares, nursing homes), quarantineD: treatment
12Mathematical Model of Transmission Dynamics: Susceptible-Infectious-Recovered (SIR) model AssumptionsPopulation is fixed (no entries/births or departures/deaths)Latent period is zeroInfectious period = disease durationAfter recovery, individuals are immunePeople can be in one of three statesSusceptible to the infection (S)Infected and infectious (I)Recovered/immune (R*)* Not to be confused with R denoting reproductive number… unfortunate nomenclature!Giesecke J. Modern Infectious Disease Epidemiology pp
13Susceptible (S) Infected (I) Recovered (R) Rate of changeProportion in state at time tdS/dt = - βcSI1 OUTSusceptible(S)St = St-1 - βcSt-1It-1It = It-1 + βcSt-1It-1 – It-1/DRt = Rt-1 + It-1/D1dI/dt = + βcSI – I/D1 IN2 OUTInfected(I)2dR/dt = + I/D2 INRecovered(R)
14Example SIR Model Consider the following values N = 1000 peopleTransmission probability, β = 0.15Contact rate, c = 12 contacts per weekInfection duration, D = 1 weekBasic reproductive rate: R0 = 0.15 * 12 * 1 = 1.8Effective reproductive rate at time t: Rt = St * R0Go to Excel spreadsheet “SIR Model.xls”
15Mathematical Models of Infectious Disease Transmission Dynamics Frequently used in infectious disease epidemiologyMajor goal is to “further understanding of the interplay between the variables that determine the course of infection within an individual, and the variables that control the pattern of infection within communities of people”Anderson RM & May RM. Infectious Diseases of Humans. Dynamics and Control
16Why develop a model?To understand the system of transmission of infections in a populationTo help interpret observed epidemiological trendsTo identify key determinants of epidemicsTo guide the collection of dataTo forecast the future direction of an epidemicTo evaluate the potential impact of an intervention
17Types of transmission models Deterministic/compartmentalSIR model exampleCategorize individuals into broad subgroups or “compartments”Describe transitions between compartments by applying average transition ratesAim to describe what happens “on average” in a populationResults imply epidemic will always take same courseProbabilistic/stochastic (Monte Carlo, Markov Chain)Incorporates role of chance and variation in parametersProvides range of possible outcomesParticularly relevant for small populations and early in epidemicMain challenge for both types of models? Good data for transmission parameters!
18Sources of data for model parameters: The example of sexually transmitted infections (STI) Recall the three main parameters are:Transmissibility (β)Duration of infectivity (D)Contact rate (c)Where do estimates of these parameters come from?
19βAnderson RM. Transmission dynamics of sexually transmitted infections. In: Sexually Transmitted Diseases. Holmes KK et al., eds pp
20Transmissibility (β): Measurement Measured as the probability of transmission from an infected to a susceptible partner (attack rate)Sources of dataContact tracingDiscordant couplesStudies of sexually active individuals who report partners with known STI status, or if the prevalence of the STI in the pool of partners is well knownChallengesEnrollment of sexual partners may be difficultIdentification of contacts between infected and susceptibles, and direction of transmissionWhat is a “contact”?
21Duration of infectivity (D): Measurement Sources of dataDuration of clinical diseaseDuration of infectionChallenges in measurementDuration of disease = duration of infectivity?Asymptomatic versus symptomaticEthical obligation to treat identified infectionsMay need to rely on historical data of questionable quality
22Contact rate (c)Typically measured as the rate of new partner acquisition (e.g., per year)Model so far assumes homogeneity in contact rateData source is sexual behaviour surveysGeneral populationSelected populations (e.g., adolescents, adults aged 18-45, students, gay and bisexual men, drug users)
23Number of partners in past 5 years Number of partners in past 5 years. British National Survey of Sexual Attitudes and Lifestyles (NATSAL), 2000Johnson AM et al. Lancet 2001; 358:
24Contact rate (c) Clearly, the contact rate is heterogeneous One cannot assume that all individuals have the same contact rateFor sexual behaviour, an important concept is the “core group”A small group of individuals with a high contact rate that contribute disproportionately to the spread of STIs in the populationSTI becomes concentrated in this core group
25Random mixing and the contact rate (c) An assumption of the simple models seen so far is that mixing is randomEvery individual has an equal chance of forming a partnership with every other individualSurvey data show that mixing is not random for many characteristics (e.g., age, ethnicity, religion, education), but tends to be assortative“Like” mix with “like”But is mixing assortative with respect to past sexual history (and by extension, the likelihood of STI infection)?
27Contact rate (c): measurement challenges Surveys of individuals obtain data on their sexual behaviour, but will be incomplete for their partnersSexual network studies get detailed partner data, but are usually localized and may not be generalizableGeneral population surveys are more representative of majority, but may insufficiently capture members of the core groupValidity of self-reported sexual behaviour and social desirability bias
28β, c, and D estimates: Bottom line Uncertainty and limitations in parameter estimatesWell-written papers willIdentify the source or reasoning behind parameter estimatesConduct sensitivity analysis to determine how much the model results depend on parameter valuesSometimes the transmission model will identify a lack of knowledge in these parameters, and can direct empirical research to obtain more data
29Example of a mathematical transmission model to assess the impact of a prevention intervention Hughes JP, Garnett GP, Koutsky L. The theoretical population-level impact of a prophylactic human papillomavirus vaccine. Epidemiology 2002; 13:Refer to handout.
30Human papillomavirus (HPV) Over 40 types of HPV infect the epithelial lining of the anogenital tractSome can lead to cancer of the cervix, and may also cause cancers of the vagina, penis, or anus (high-risk oncogenic types)Some produce genital warts (low-risk types)There are over 40 sexually-transmitted HPV types.Those that can lead to cancer are called “high-risk oncogenic types”. They can cause cervical cancer, but also vaginal, penile, and anal cancers.Those HPV types that don’t lead to cancer are called “low-risk types”. These may produce genital warts.
31Epidemiology of HPVHPV present in 5%-40% of asymptomatic women of reproductive ageAs many as 75% of adults are thought to be infected with at least one HPV type in their lifetimeFor the vast majority, the infection causes no ill health effects and is cleared within 1-2 yearsAmong women in whom HPV infection persists, time from initial infection to cervical cancer thought to be yearsHPV is the most common STI, with prevalence estimated between 5-40% depending on the study.In fact, as many as 3 in 4 adults are thought to have an HPV infection at least once in their lifetime.For most women, HPV infections are of little consequence. They are asymptomatic and clear within about one year.However, among some women these infections persist, and result in an increased risk for cervical cancer.
32Worldwide Distribution of Cervical Cancer, 2002 Canada '05Morbidity7.6 per 100,000Mortality2.0 per 100,000Worldwide, cervical cancer is the 2nd leading cancer site among women.This figure gives you a sense of the geographical distribution of the rates of new diagnoses of cervical cancer.Countries with the highest incidence of cervical cancer, shown in red, are in sub-Saharan Africa, South America, and some parts of Asia.Countries with the lowest incidence are shown in dark green, and Canada is among them. In 2005, the rate of new cervical cancer diagnoses was just under 8 per 100,000 women, with low mortality, and cervical cancer was the 12th most common cancer. These low rates are attributed to Pap test screening programs in Canada, and as well to low fertility rates. Nevertheless, about 400 Canadian women die of cervical cancer per year.Rate per 100,000 women
33Vaccine to prevent cancer! Gardasil™ by MerckProtects against infection with HPV-16 and HPV-18, as well as HPV-6 and HPV-11, the types that cause most genital wartsVaccine efficacy 89%+ (Villa et al., 2005)Approved for use in girls and women aged 9-26 in CanadaCervarix™ by GlaxoSmithKlineProtects against infection with HPV-16 and HPV-18, the types that cause most cervical cancersDivision of Cancer Epidemiology, McGill University involved in design & data analysis of trialVaccine efficacy 83%+ (Harper, Franco et al., 2004)Cervical cancer research is quite exciting these days, because we now have a vaccine to prevent cancer!Two vaccines have been developed. Clinical data indicate that they offer excellent protection against HPV infection. One vaccine, Gardasil, has been approved in Canada, and the second, Cervarix, is expected to be approved soon.But there is much to work out regarding the most appropriate and cost-effective vaccine strategy after licensure. Mathematical models can help us to anticipate the impact of a particular strategy.
34Model 1 is a compartmental model of HPV transmission dynamics Hughes JP et a. The theoretical population-level impact of a prophylactic human papilloma virus vaccine. Epidemiology 2002; 13:631-9.Model 1 is a compartmental model of HPV transmission dynamicsSexually active population, which authors implicitly defined as having contact rate c > 0 (i.e., acquiring new partners over time)Vaccine benefits: ↓ susceptibility, ↓ transmissibility, ↓ duration of infectiousnessVaccine failure: take, degree, durationCompartmental model (as opposed to stochastic) dealing with population averages.The model concerns the sexually active population, which the authors do not explicitly define, but imply that this consists only of individuals who are acquiring new partners.In their modeling, they explore 3 potential benefits of vaccination:Decreased susceptibilityDecreased transmissibility in breakthrough infectionsDecreased duration of infectiousness in breakthrough infectionsThe model allows for 3 types of vaccine failures:Take (when the vaccine has no effect in some people)Degree (when the vaccine reduces but does not eliminate susceptibility)Duration (loss of protective immunity over time)
35Sexually active population (η) μSexually active population (η)Φ1 - ΦμμσVaccinated (v)Susceptible (x)φλλμμInfected (w)Infected (y)People enter and exit the sexually active population at a constant rate μ (MU), where 1/μ is the mean duration in the sexually active population in years.A proportion Φ (upper-case PHI) of the sexually active population is vaccinated and successfully immunized. This incorporates vaccine “take”.A proportion σ (SIGMA) lose their protective immunity over time and enter the Susceptible compartment, where 1/σ is the duration of vaccine protection.Susceptibles are infected with HPV-16 at a constant rate λ (LAMBDA), otherwise known as the “force of infection”.Immunized individuals are also infected with HPV-16 at rate φλ, where φ (lower-case PHI) is the susceptibility of immunized individuals relative to unimmunized individuals (i.e. φ is a relative risk). If the vaccine is 100% effective, then φ is 0 and no immunized individuals become infected. If the vaccine efficacy is less than 100%, then some immunized individuals will have “breakthrough” infections.Infected individuals recover and become immune at rate γ (GAMMA), where 1/γ is the mean duration of infectiousness.Immunized individuals with breakthrough infections recover at rate αγ, where α (ALPHA) is the relative rate of recovery from infection in immunized versus unimmunized invididuals.αγγRecovered, immune (z)μHughes JP et al. Epidemiology 2002; 13:631-9.
36β, D, and c parameter estimates Transmissibility (β)Female-to-male = 0.7Male-to-female = 0.8Duration of infectiousness (D)1.5 yearsContact rate (c)High activity class: 3% of population, 9.0 new partners per yearMedium activity class: 15% of pop, 3.0 new partners per yearLow activity class: 82% of pop, 1.4 new partners per yearMixing parameter, ε = 0.7, where ε = 1 is fully random, and ε = 0 is fully assortativeAlthough it is not explicitly stated, one assumption throughout this paper is that all sexual activity is heterosexual.Hughes JP et al. Epidemiology 2002; 13:631-9.
37% reduction---44%30%19%12%68%Assumptions:90% vaccine coverageVaccine reduces infection by 75%Vaccine confers 10 year protectionBreakthrough infections have similar natural history to infections in unvaccinated individualsIn “targetted” approach, coverage is 90% in two highest risk groups, 10% in lowest risk groupAuthors conclude that vaccinating women only would be a reasonable strategy, since it would achieve 68% of the reduction in HPV-16 prevalence in women.Conversely, the targetted approach would be less effective.* 90% vaccine coverage, 75% vaccine efficacy, 10-year protection, similar natural history† 90% vaccine coverage in high and medium sexual activity class, 10% coverage in low sexual activity classHughes JP et al. Epidemiology 2002; 13:631-9.
38Hughes JP et al. Epidemiology 2002; 13:631-9. Main result in this table is the relative reduction in female HPV-16 prevalence, comparing female only to male&female vaccine strategies.1/μ = mean duration in the “sexually active populationε = mixing parameter, where 0 is fully assortative and 1 is randomc = contact rate greatest variability in vaccine impact, as it varies from to with contact rate, such that lower heterogeneity in contact rates result in poorer impact of female only strategy compared to higher heterogeneityr = relative risk of transmission in breakthrough vs unvaccinated infectionsφ =relative susceptibility of vaccined to unvaccinated individuals 1/σ = mean duration of vaccine protectionα = relative rate of recovery of breakthrough vs unvaccinated infectionsΦ = proportion who are effectively vaccinated (includes “take”)TAKE HOME MESSAGE: over broad range of assumptions, female only vaccination strategy is 60%-75% as good as vaccinated both females and malesHughes JP et al. Epidemiology 2002; 13:631-9.
39Hughes et al - Conclusions Given assumptions, an HPV vaccine for a given type would reduce prevalence of that type by44% if females and males vaccinated30% if only females vaccinatedOver a broad range of assumptions, female-only vaccination would be 60%-75% as effective as a strategy which vaccinated both females and malesVaccination targetted to high-risk individuals only would reduce prevalence by no more than 19%, probably less given difficulty in reaching these individualsHughes JP et al. Epidemiology 2002; 13:631-9.