Modelling HIV/AIDS in Southern Africa Centre for Actuarial Research (CARe) A Research Unit of the University of Cape Town

History of the ASSA AIDS and Demographic model  Doyle-Metropolitan model (c1990)  ASSA500 (c1995)  ASSA600 (c1998)  ASSA2000 suite (2001): lite, full, provincial (beta 2002)  ASSA2002 lite and full (2004)  ASSA2003 suite (2005): lite, full, provincial  Other models ( Orphans, select populations, other countries

Methodology: ASSA model Antenatal data (by age) Adult death data Adjust for bias (public anc vs all women) Demographic parameters (base population, fertility, non- AIDS mortality and migration) Cohort component projection model Calibration Epi and behavioural parameters (e.g. % in risk groups, amount of sex, probability of transmission, probability a condom used, etc) Epidemiological, behavioural, intervention model Interventions (IEC, VCT, STI, PMTCT, ART) Detailed output including:  No. infected  No. new infections  No. AIDS deaths, etc

Features of the ASSA lite model  Heterosexual behavioural cohort component projection model (individual ages/years)  Population divided by risk by: Age (young, adult, old) ‘Behaviour’ (PRO, STD, RSK, NOT) ‘Previous socio-economic disadvantage’ (racial groups) Geographic region (province)  Sex activity Risk group of partner, probability of transmission, number of new partners p.a., number of contacts per partner, condom usage, No sex between racial groups or provinces

Modelling prevention and treatment  Five interventions: Social marketing, information and education campaigns (IEC) Improved treatment for sexually transmitted diseases (STDs) Voluntary counselling and testing (VCT) Prevention of mother-to-child transmission (PMTCT) Antiretroviral treatment (ART)

The fitting process - calibration  Set as many of the parameters/assumptions from independent estimates (% STD, probability of transmission, condom usage, age of (male) partners, the median term to survival of adults and children, impact of HIV on fertility and bias in ANC data, all non-HIV demographic assumptions)  Set some other assumptions (which are not particularly important) by reasonable guesses (e.g. relative fertility, and risk groups of migrants)  The remaining assumptions are set in order to produce known data of the prevalence or impact of the epidemic such as the antenatal prevalence and the mortality figures - calibration (e.g. size of the RSK group, the mixing of risk groups, sex activity by age, no. of partners, number of contacts per partner)

Calibration targets  Prevalence levels Antenatal – overall prevalence Antenatal – prevalence by age over time Ratio of antenatal to national by age HSRC prevalence by sex and age  Deaths Population or vital registration – overall by sex, age and over time Cause of Death – proportion AIDS in adults by sex and age Cause of Death – proportion AIDS in children by age Cause of Death – ratio of male to female by age over time

Calibration targets (cont’d)  Census Numbers by sex and age nationally and provincially Mortality rates by age and sex  Orphanhood  CEB/CS  Deaths in household  Other Numbers on treatment (private and public)

Antenatal prevalence: South Africa Confidence intervals prior to 1998 were incorrectly calculated – should be wider

Number of deaths - men

Number of deaths - women

Uncertainty  Demography (Base population, Fertility, Mortality & Migration)  Epidemiological assumptions (% in risk groups, mixing of the risk groups, probabilities of transmission, infectivity and infectiousness by stage, etc)  Interventions (in particular treatment) Roll-out Effectiveness  Behaviour  Future developments (e.g. vaccine)

Selected results

Comparison with HSRC05: South Africa (Prevalence: males and females)

Prevalence: adults 20-64: South Africa

Numbers infected by province: South Africa

Numbers on HAART by province: South Africa

Prevalence by sub-district: Botswana

Numbers infected by stage by year: Botswana

Numbers of deaths by year: Botswana

Future developments  Circumcision  Vaccine  Age-specific interventions  Pregnancy and transmission?  Risk group migration?  Better demographic estimation  Uncertainty  Education?  Household impact?  Fitting to other (SADC) countries