Title: DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA) What can SACEMA do for Africa?
1DST/NRF Centre of Excellence in Epidemiological
Modelling and Analysis(SACEMA)What can SACEMA
do for Africa?
2The 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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6Philosophy (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.
7Philosophy (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
8Five-year goals
- 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. - Strengthen sustainable capacity in South Africa,
the Region and Africa generally, to continue, and
build on, this work.
9Strategy (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.
10Taking 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.
11Strategy (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,
12Strategy (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.
13Develop 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
14Male 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.
15The simplest model for men and women
?im
? if sm
im
sm
if
sf
? im sf
?if
No MC
100 MC
16If 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
18Elimination 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
19Superspreading 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?
20Superspreading 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.
21Quantifying individual variation in transmission
- Collect detailed transmission data for many
diseases. - Apply maximum-likelihood estimation and Akaikes
Information Criterion to select best statistical
model for transmission data. - Compare diseases using model estimates.
22HIV infection modelling within host
- Host viral dynamics. Intracellular delays,
drug treatment and immune response.
Witten/Ouifki - HIV Strain Dynamics Welte/Pretorius/Mwanga
23New 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.
24In 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.
25Why were recent estimates so incorrect?
- Inadequate data.
- Poor/inappropriate modelling.
- Imperfect understanding of the biological and
mathematical problems. - Measures of incidence very rare.
26Modelling 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?
27HIV 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.
28Optical density vs time since last HIV negative
test
29HIV 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.
30Integrating 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?
31New 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.
32The 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 .
33In 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.
34What 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.