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DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA) What can SACEMA do for Africa?

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Title: DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA) What can SACEMA do for Africa?


1
DST/NRF Centre of Excellence in Epidemiological
Modelling and Analysis(SACEMA)What can SACEMA
do for Africa?
  • John Hargrove

2
The South African Centre for Epidemiological
Modelling and Analysis
  • SACEMA is a new Centre of Excellence, with core
    funding from the South African Government.
    Housed at the University of Stellenbosch in the
    Western Cape, it is a national centre with a
    mandate to promote epidemiological modelling and
    analysis in South Africa.

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Philosophy (I)
  • Understanding many economic, health and
    environmental processes requires the development
    of dynamical mathematical models and thus a
    reasonably high level of mathematical expertise.
  • South Africa, like many other developing
    countries, is relatively weak in this area. 
  • Intention is to bring together people interested
    in addressing such problems while providing
    training opportunities for young mathematicians
    who are interested in applying their skills to
    address critical problems in South Africa.

7
Philosophy (II)
  • Although the Centre operates on a worldwide
    basis and contributes to the general advancement
    of epidemiology, it must also be considered in
    the context of South African epidemiology. Each
    programme will benefit the South African
    epidemiology community ..
  • If South Africa is strong in the field, South
    African scientists will play a major part in the
    programme if relatively weak, the programme
    should help to raise South African standards.
    Instructional courses, aimed at younger
    researchers and research students, will play a
    vital role.Adapted from the website of the
    Isaac Newton Institute

8
Five-year goals
  1. Complete work in the field of mathematical
    epidemiology that contributes substantially to
    the alleviation of the effects of major diseases,
    and other problems, currently affecting people in
    South Africa, and in Africa as a whole.
  2. Strengthen sustainable capacity in South Africa,
    the Region and Africa generally, to continue, and
    build on, this work.

9
Strategy (I)
  • Use international and local scientists, including
    graduate students, to achieve the above goals.
  • Ensure flow of high-quality workers through
    SACEMA develop strong international links
    Berkeley, Paris, Ghent, WHO, CDC, Stats Canada,
    Johns Hopkins, NIH, Rutgers....
  • Fund short/medium term fellowships for scientists
    to work at SACEMA and buy-out of time for
    scientists working elsewhere.
  • Use summer schools/meetings to collect small
    numbers of interested, talented individuals to
    focus intensively on a given modelling problem.
  • Visiting fellows to contribute to capacity
    building as well as research wherever appropriate.

10
Taking AIMS
  • SACEMA will encourage suitable students from
    AIMS, and from South African universities and
    institutes, to take on projects in epidemiology.
  • This can then be used as a selection process that
    can identify the most promising students for
    potential recruitment as junior fellows at
    SACEMA.
  • Through the SACEMA/StIAS/AIMS network, link good
    students with other institutions and people with
    good projects.

11
Strategy (II)
  • Focus initially on HIV-AIDS, TB and associated
    diseases.
  • Seek major international funding to further
    support these efforts NIH, PEPFAR, Global
    Fund.
  • Use this funding to facilitate expansion into
    other areas malaria/trypanosomiasis, bovine TB,
    avian flu, impact of climate changes, optimal
    fish harvesting,

12
Strategy (III)
  • Crucial to the achievement of Goal I are
  • Sound understanding of biology/epidemiology.
  • Innovative mathematical modelling.
  • Access to the best possible data.
  • Interdisciplinary collaboration.
  • Good communications with policy makers.

13
Develop TB/HIV database Seebregts
HIV/TB model Williams/Corbett/Lauer
HIV male circ. model Auvert/Williams/L-Smith
Host-viral dynamics project Witten
Vaccine modelling Welte
Modelling HIV/TB in Botswana Lungu
HIV prevalence/incidence Marinda/Hargrove
Superspreading Lloyd-Smith
Analysis of health etc data.
Malaria/tryps Torr/Hargrove/Vale
Malaria/warming/GIS Freeman/Marijani
HIV population models Matthews
HIV/TB in W. Cape Wood/Lawn
Microsimulation modelling Pretorius/Welte
Stanford-SA bio-informatics Seioghe
DE modelling CD4/mortality Ouifki
Bovine TB/Kruger Getz/Geoghan
14
Male circumcision MC
  • Auverts MC trial at Orange Farm Gauteng
    indicated that MC reduces sexual transmission of
    HIV from female to male by 60.
  • SACEMA associates Williams, Lloyd-Smith and
    others modelled this situation and estimated the
    effects on HIV infections, prevalence and
    deaths of promoting MC as a public health policy.

15
The simplest model for men and women
?im
? if sm
im
sm
if
sf
? im sf
?if
No MC
100 MC
16
If MC reduces transmission in one direction by a
factor of ? this is equivalent to a reduction in
both directions by a factor of 1-v(1- ?)
  • MC is equivalent to a vaccine which reduces
    transmission by 37

17
? 0.6
HIV P
MC c
?I(k) ?I()N
?I() ?(1-?)P/10
18
Elimination of HIV?
  • In South Africa R0 5.
  • Introducing MC could reduce R0 to 2.
  • A further reduction of 2 would then be sufficient
    to eliminate HIV as a public health problem

19
Superspreading and the effect of individual
variation on disease emergence
  • Quantitative study of epidemic dynamics centres
    on the basic reproductive number, R0
  • Yet real epidemics (e.g. SARS in 2003) feature
    superspreading individuals who infect far more
    people than the average case.
  • How to incorporate superspreading in outbreak
    models?
  • How prevalent is superspreading for different
    diseases?
  • How does individual variation affect outbreak
    dynamics?

20
Superspreading and the effect of individual
variation on disease emergence
  • Normal SARS cases infected 0 to 3 others, but
    superspreaders infected 10, 20 or more.
  • Is superspreading an exceptional property of
    SARS, or common to all infectious diseases?
  • How can this individual-level variation be
    modelled, and how does it affect outbreak
    dynamics?
  • Lloyd-Smith, Schreiber, Kopp Getz Nature 438
    355-359.

21
Quantifying individual variation in transmission
  1. Collect detailed transmission data for many
    diseases.
  2. Apply maximum-likelihood estimation and Akaikes
    Information Criterion to select best statistical
    model for transmission data.
  3. Compare diseases using model estimates.

22
HIV infection modelling within host
  • Host viral dynamics. Intracellular delays,
    drug treatment and immune response.
    Witten/Ouifki
  • HIV Strain Dynamics Welte/Pretorius/Mwanga

23
New campaigns (I)
  • Modelling changes in HIV prevalence and
    incidence.
  • There are fundamental problems in the modelling
    of HIV epidemics that on occasion lead to major
    agencies, responsible for advising African
    governments, arriving at inappropriate
    conclusions about trends in the epidemic.
  • Well illustrated by the situation in Zimbabwe.

24
In 2004 MoH working with UNAIDS, WHO and CDC
suggested no change in HIV prevalence after about
1994/1996.Now clear prevalence has been
declining since at least 1998 and incidence
perhaps as early as 1994.
25
Why were recent estimates so incorrect?
  • Inadequate data.
  • Poor/inappropriate modelling.
  • Imperfect understanding of the biological and
    mathematical problems.
  • Measures of incidence very rare.

26
Modelling the HIV epidemic tactics
  • Data available on the behaviours of individuals
    and on the consequent changes in HIV prevalence
    and incidence at the population level.
  • Mathematical challenge is to produce models that
    marry the two.
  • At the individual level use branching processes.
  • At population level use compartmental dynamical
    simulations as currently for male circumcision.
  • But how to combine the two? New mathematical and
    modelling approaches are needed. Network theory?
    Micro simulation?

27
HIV incidence via cross-sectional surveys
  • The rate of acquisition of new infections defines
    the development of the HIV epidemic.
  • Previously measured via longitudinal studies.
  • CDC have used ZVITAMBO samples to validate a
    detuned ELISA technique that allows estimation
    of HIV incidence via cross-sectional surveys.

28
Optical density vs time since last HIV negative
test
29
HIV incidence via cross-sectional surveys
  • Unfortunately the method over-estimates incidence
    by a factor of 2-3 or even more.
  • Work at SACEMA has suggested ways of adjusting
    the estimates such that the BED could be used to
    estimate HIV incidence from cross-sectional
    survey data.

30
Integrating incidence measures into ANC
sentinel-site monitoring
  • Once technique is perfected, prevalence and
    incidence can be estimated from the same
    cross-sectional survey.
  • Greatly enhances ability to model the epidemic.
  • Presents opportunities to identify problem
    situations and estimate the early effect of the
    roll-out of ARV therapy.
  • Indications, from ZVITAMBO and elsewhere, that
    the prospect of treatment has greatly increased
    levels of VCT and reduced stigma.
  • Will it also lead to a decrease in incidence?

31
New campaigns (II)
  • Modelling the HIV-TB epidemics.
  • Pretorius/Ouifki working with Wood team,
    modelling HIV/TB situation at Masiphumelele
  • May well be necessary to involve
    micro-simulation.
  • And the problem may be so large that may also be
    necessary to use distributed computing
    techniques.

32
The importance of good data
  • Modelling the HIV-TB epidemics.
  • Ensuring access to the best TB and HIV data
    available in South Africa will be crucial to this
    project.
  • The sine qua non of success of all SACEMA
    projects will be the requirement of access to
    good data.
  • Reference has been made above to three projects
    that have utilised excellent data from the
    ZVITAMBO project.
  • There is much more available .

33
In ZVITAMBO 14,110 post partum women and their
babies followed for two years, three monthly
intervals providing blood and breast milk
samples at each follow-up visit. Analyses of the
data already produced important insights into HIV
but still 600,000 samples not yet analysed.
34
What can SACEMA do for Africa?
  • Attract a strong team of mathematicians and
    modellers to improve understanding of diseases
    affecting Africa.
  • Work with AIMS, and other institutes, to
    encourage young African mathematicians in the
    pursuit of careers in mathematical epidemiology.
  • Work with African scientists in accessing, and
    making available for analysis, the best data
    available - in South Africa and elsewhere in
    Africa.
  • Funding promising African students to attend
    appropriate workshops.
  • Source donor funds to facilitate the above.
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