DST/NRF Centre of Excellence in Epidemiological Modelling and

Analysis

(SACEMA)

What can SACEMA do for Africa?

John Hargrove

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.

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”.

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]

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.

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.

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.

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, …

Strategy (III)

Crucial to the achievement of Goal I are:

1. Sound understanding of biology/epidemiology.

2. Innovative mathematical modelling.

3. Access to the best possible data.

4. Interdisciplinary collaboration.

5. Good communications with policy makers.

HIV/TB model

Williams/Corbett/Lauer

HIV prevalence/incidence Marinda/Hargrove

Malaria/warming/GIS Freeman/Marijani

Develop TB/HIV database Seebregts

HIV population models Matthews

Malaria/tryps Torr/Hargrove/Vale

Vaccine modelling Welte

Analysis of health etc data.

Bovine TB/Kruger Getz/Geoghan

HIV male circ. model Auvert/Williams/L-Smith

Stanford-SA bio-informatics Seioghe

HIV/TB in W. Cape Wood/Lawn

Host-viral dynamics project

Witten Modelling HIV/TB

in Botswana [Lungu]

Superspreading Lloyd-Smith

Microsimulation modelling Pretorius/Welte

DE modellingCD4/mortality [Ouifki]

Male circumcision [MC]

• Auvert’s 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.

sm

sf

im

if im sf

if sm

im

if

2 5 No MC 100% MC

The simplest model for men and women

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-√(1- )

MC is equivalent to a vaccine which reduces transmission by 37%

MC

HIVP

I(%)(1-)P/10

I(k)I(%)N

= 0.6

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

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?

• “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?

Superspreading and the effect of individual variation on disease emergence

Lloyd-Smith, Schreiber, Kopp & Getz Nature 438: 355-359.

1. Collect detailed transmission data for many diseases.

2. Apply maximum-likelihood estimation and Akaike’s Information Criterion to select best statistical model for transmission data.

3. Compare diseases using model estimates.

Quantifying individual variation in transmission

HIV infection modelling – within host

• Host viral dynamics. Intracellular delays, drug treatment and immune response. [Witten/Ouifki]

• HIV Strain Dynamics [Welte/Pretorius/Mwanga]

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.

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.

ANC prevalenceGreater Harare

Year1984 1988 1992 1996 2000 2004 2008

Pre

vale

nce

(%)

4

8

12

16

20

24

28

32

36

Double logistic fit ZVITAMBO estimatesMoH estimates 2000-2004 Mahomed et al. (1991)Mbizvo et al. (1996)MoH projection (2004)

Why were recent estimates so incorrect?

• Inadequate data.

• Poor/inappropriate modelling.

• Imperfect understanding of the biological and mathematical problems.

• Measures of incidence very rare.

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?

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.

Optical density vs time since last HIV negative test

Theoretical graph of sqrt(OD-n) vs ln(ti, j)

Log time (ti, j days) since last negative0 1 2 3 4 5 6 7

Sq

ua

re r

oo

t o

f O

D-n

-1.6

-1.2

-0.8

-0.4

0.0

0.4

0.8

1.2

1.6

Selected OD cut-off (B)

Negative baseline (A)

Slope = b1,i

Intercept = b0,i

..

..

.

Window (Wi )

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.

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?

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.

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 ….

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.

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.