Immuno-epidemiology of malaria

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Immuno-epidemiology of malaria

• Klaus Dietz
• Department of Medical Biometry
• University of Tübingen, Germany
• DIMACS Worksop
• 11-13 December 2006

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Outline

• What is immuno-epidemiology?
• What is malaria?
• Why model malaria immunity?
• Malaria immunity models a brief history
• A within-host malaria model
• Concluding remarks

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Serological surveys as immuno-epidemiological
tools
Desowitz RS, Saave JJ, Stein B. The application
of the indirect haemagglutination test in recent
studies on the immuno-epidemiology of human
malaria and the immune response in experimental
malaria. Mil Med.,1311157-1166 (1966).
Suzuki M. Malaria immuno-epidemiology a trial
to link field study with basic science. Gaoxiong
Yi Xue Ke Xue Za Zhi.7224-232 (1991). .....malar
ia serological assessment was carried out in
endemic areas in Haiti, Indonesia, Sudan and in
Brazil Amazon. The serological survey was useful
in finding latent foci in a controlled area, for
the assessment of past epidemics,....
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A theoretical framework for immuno-epidemiology
Woolhouse, M.E.J.et al. Acquired immunology and
epidemiology of Schistosoma haematobium., Nature
351, 757-759, 1991 (Acquisition of this immunity
seems to be related to the cumulative effects of
repeated infection and provides only partial
protection. These characteristics are consistent
with immuno-epidemiological data for both S.
mansoni and S. haematobium infections of humans.)
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Hellriegel, B. Immunoepidemiology-bridging the
gap between immunology and epidemiology. Trends
in Parasitology, 17, 102-106, 2001

• Immunoepidemiology combines individual- and
  population-oriented approaches to create new
  perspectives.
• It examines how inter-individual differences in
  immune responses affect the population dynamics
  of micro- and macro-parasites to produce the
  epidemiological patterns of infection observed in
  heterogeneous host populations.

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A cartoon of immuno-epidemiological models
Parasite density
Immunity level
Morbidity level
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STI project on the mathematical modeling of the
impact of malaria vaccines on the clinical
epidemiology and natural history of Plasmodium
falciparum malariaThomas A. Smith and team

• Many millions of simulation runs, each in the
  order of hours
• Need for supercomputing

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Network Computing
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Volunteer Computing Project
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Malaria cycle
SourceWellcome Trust
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Infected Red Blood Cells (IREs) by asexual (red)
and sexual (green) parasites in Patient G141
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n 54 mean 212 days, median 216 days
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Present geographical distribution of malaria
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The challenge of malaria
The President And Mrs. Bush Will Host The White
House Summit On Malaria On December 14, 2006, In
Washington, DC, To Discuss And Highlight Measures
For Combating This Preventable Disease
In June, President Bush announced a new
commitment to combat malaria. His proposal calls
for an additional 1.2 billion over the next five
years. The money will pay for insecticide-treated
nets, it will allow for indoor spraying against
mosquitoes, and it will provide effective new
combination drugs to treat malaria. Our goal with
this new funding is to reach more than 175
million people in 15 nations. (1.37 /Person/year)
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Growth of the yearly number of malaria
publications Doubling time 10 years and 7 months
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Why model malaria immunity?

• Not necessary, if one is aiming for eradication
• Macdonald, G. Theory of the eradication of
  malaria. Bull. WHO 15, 369-387, 1956.
• WHO global malaria eradication campaign 1955

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Original Eradication Plans

• Interruption of transmission of main species
  infecting humans by DDT spraying
• Malaria disappears spontaneously in under 3 years

Source Gabaldon
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Pampana Textbook on malaria eradication

• If malaria eradication will be achieved it will
  be first of all due to the computer
• Macdonald DDT spraying and mass drug
  administration every two months will interrupt
  malaria transmission in Africa

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(graph by Martin Eichner)
hypo-
meso-
hyper-
holo-endemic
Sukuta(Gambia)
Cerebral malaria incidence (per 1000 per
year)(ages 0-9 years)
Kilifi N(Kenya)
Kilifi S(Kenya)
Siaya(Kenya)
Bakau(Gambia)
Force of infection (per year)
Snow et al. (1997) The Lancet
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Points of attack of potential malaria vaccines
Smith et al (2006)
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Malaria immunity models

• Dietz, K Mathematical models for transmission
  and control of malaria. 1091-1133 (1988)
• Molineaux, L and Dietz, K Review of intra-host
  models of malaria. Parassitologia 41221-231
  (1999)
• McKenzie, FE and Bossert, WH An integrated model
  of Plasmodium falciparum dynamic. J Theor.
  Biology 232, 411-426 (2005)

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Ronald Ross (1857-1932)
Second Nobel price in Medicine in 1902
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Infection without (Ross) and with (Macdonald)
superinfections
Ross (premunition)
0
Macdonald
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Age-specific prevalence
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Prevalence by age
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Implications of unrealistic assumptions about the
strength of immunity

• Underestimating the strength of immunity leads to
  an
• underestimate of the basic reproduction number
  and consequently an
• underestimate of the necessary eradication
  efforts and an
• overestimate of the expected control effects

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Garki Model (Dietz, Molineaux, Thomas,
1974)Garki Book (Molineaux L and Gramiccia G,
1980http//whqlibdoc.who.int/publications)Garki
Data http//www.sti.ch/de/forschung/biostatistics
/downloads.html
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Garki model fit
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Fit to Garki data of the STI project
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Prevalence by vectorial capacity
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Gupta, S. and Day, K.P. A theoretical framework
for the immunoepidemiology of Plasmodium
falciparum malaria. Parasite Immunology, 16,
361-370 (1994)
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Might malaria yield to mathematics (Economist,
March 12th 1994)
Recent epidemiological work suggests that there
may be a stronger chance of controlinling malaria
than was once thought likely Comparing the
distribution of the strains among people by age
with the spread produced by their model, they
found a very close match if the average number of
secondary cases caused by a single case was
between six and seven.
Gupta, Anderson, Day and Trenholme
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Innate and adaptive immunityStevenson and Riley
(2004)

• Research on the immunology of malaria has tended
  to focus on adaptive immunity
• Accumulating evidenceindicates a crucial role
  for innate immune responses in protective
  immunity to malaria
• Innate responses are essential to limit the
  initial phase of parasite replication,
  controlling the first wave of parasitemia and
  allowing the host time to develop specific
  adaptive responses that will enable the infection
  to be cleared

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Fig 1A
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Fig 1B
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Fig 1C
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Correlations between innate immunity threshold,
parasite growth rate and peak density
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Correlation between innate and adaptive immunity
thresholds and between two parameters for
adaptive immunity
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Feverthresholds
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Vaccine effect on initial multiplication factor
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Fig 3
B
A
4
3
2
1
0
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Vaccine efficacy
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Conclusions from the within-host model for the
first wave

• The 800 data points for 100 patients could well
  be described by a simple model with four
  interpretable parameters per patient.
• All parameters show large variation.
• The maximum parasitaemia is mainly controlled by
  innate immunity.
• The benefit of a vaccine targetting the asexual
  blood stages is expected to be strongly host
  dependent.
• At low immunogenicity the expected vaccine
  efficacy against severe malaria is much higher
  than against fever.

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Concluding remarks

• Historic data (neurosyphilis patients, Garki
  project) are still relevant today and in the
  future because new data about the natural course
  of malaria can for ethical reasons no longer be
  collected.
• Estimates for basic reproduction numbers are
  model dependent.
• In spite of the fact that malaria is probably the
  disease with the largest number of models there
  is still no generally acceptable model.

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Acknowledgements

• Louis Molineaux, Geneva
• Tom Smith and his team at the STI, Basle
• Martin Eichner, Günter Raddatz, Tübingen