Transcript Anopheles gambiae

An Overview of
the Effects of
Climate on
Malaria
Transmission
Barbara Wendelberger
27 April 2010
Some Simplifications to MARA
Anopheles gambiae s.l.
 Plasmodium falciparum.
 Independent analyses of rainfall and
temperature

Why Climate Mappings Fail
Lack of data
 Use of crude geographic and climate isolines
 No clear, reproducible numerical
definitions

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Prevents ability to compare data
Improvements

Large global data sets
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Up to 1.6 billion observations daily
Climate data
Population data
Satellite imagery and topography
Geographical Information Systems (GIS)
 Advanced imaging software

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Overlaying of varying levels of understanding

Ex. Rainfall and temperature
Finding Stability Distributions

MARA

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
Finding the limits of the
distribution of stable
malaria areas
Based on temperature and
rainfall data
R0 (vectorial capacity)

Main component strongly
determined by climate

Reproduction rate of malaria
parasite and mosquito vector
Modeling Problems

Malaria is not definable:


in space because the edge of the distribution is
indistinct
in time because both intensity and distribution
wax and wane with natural periodicity of
events
Logic
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Boolean Logic
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Climate has only two states
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Suitable for transmission (1)
Unsuitable for transmission (0)
Fuzzy Logic

An extension of Boolean logic

Allows “fractions”
 Suitable (1)
 Semi-suitable (between 0 and 1)
 Unsuitable (0)
Transmission Areas
Perennial: always able to sustain
transmission
 Seasonal: suitable for a short season each
year
 Epidemic: long-term variation in climate
renders suitable conditions irregularly
 Malaria-free: always unsuitable

*Long term monthly means exclude rare
epidemic zones
A “fuzzy”
model that
demonstrates
the different
suitability
zones
Temperature Effects

Sporogonic duration (n)

n=
DD _
T – Tmin
DD=degree days for parasite development (111)
T=mean temperature
Tmin=temperature at which parasite development ceases
(16 C)

Mosquito survival (p)

p=e


(-1/(-4.4+1.31T-0.03T^2)
Defined by Martens
Assumes constant humidity
Temperature, p, and n
pn =
percentage of vector cohort that
survives the required temperature
time period
ld =
=
larval density
1
___
(0.00554T – 0.06737)
Temperature, p, and n
Rainfall
Best studied when temperature is not
limiting
 No direct, predictable relationship between
rainfall and Anopheles gambiae s.l.

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Anopheles gambiae s.l. breed more prolifically in
temporary, turbid water bodies, such as those
formed by rain
Impacts:

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
Humidity
Saturation deficit
Temporary and permanent bodies of water
Sustainability
Temperature cut-off point between
epidemic and no-malaria zone: 18ºC
22ºC allows stable transmission
The rainfall requirement for stable
transmission is ~80mm/month for at
least 5 months
Climate/Transmission Relevance
More limiting variable used.
Climate Change and Health Research
(NIH Portfolio Analysis-funded activities in 2008)
Number of studies in
some way related to
climate change
Number that directly
relate to climate change
Number that examine
how climate variables
affect health
Climate is likely an
important factor but is
not explicitly addressed
1,357
7
85
706
NIH Studies
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Health:

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Exposure pathways:

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Infectious diseases, respiratory diseases, asthma,
heat stress, exposure to environmental toxins,
trauma/injury, and cancers
Extreme weather, UV radiation, pollution, waterborne, vector-borne, and zoonotic diseases
Study Types

Laboratory experiments,
population studies, field
ecology, and mathematical
modeling
Deaths
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The WHO
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160,000 deaths due to climate change in 2000
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From malaria, malnutrition, diarrhea, flooding, and
heat waves
BUT:
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How does this compare to climate-related
deaths in other years?
What is the error? Could this number be within
the range of the normal number expected?
NIH Initiatives
The NIH is interested in studies that
directly examine climate impacts on
human health.
 Research needs to bridge the gap between
global scale and micro studies.

Could Global Warming Increase Malaria Prevalence?

Optimum constant temperatures for adults and
larvae:
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23ºC to 24ºC
Development rates
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Increased development for both parasite and vector with
increased temperature
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Could increase it to the point of weakening the progeny
Density
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At 30ºC, when density increases, survival increases
At 27ºC, when density increases, survival decreases
Current Predictions Based On
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Continuing change in global temperature
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The present distribution of malaria
parasites and their mosquito vectors
Warming Effects
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High Temperature
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Increase
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Development rate to adulthood
Frequency of blood-feeding
Rate at which parasites are required
Parasite incubation time
Decrease
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Adult mosquito survival
Thermodynamics
Negative Correlation Coefficients?
Data


Dar es Salaam (Tanzania)
Dodowa (Ghana)

-0.7 (mean max monthly temp/number of cases)
Could the Malaria Endemicity Center Move?

Multiple factors suggest yes
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Intrinsic optimum temperature model
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Exhibits the effects on enzyme inactivation in relation
to development
Co-evolution of vector and parasite (23ºC to 24ºC)
Temperature and the sexual events of the
malaria parasite in the mosquito gut
Relative transcription levels of rRNA involved
in sporogony
The success of mosquito development from
aquatic to adult stage
The Bottom Line

Climate is a complex variable

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Study individual components
Understand how they interact and affect each
other
If temperatures continue to increase, then
the center of malaria endemicity will likely
move to avoid temperatures that are too
hot to encourage stable development

Tropics are not equivalent to “hot
environments”
Research Sources
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Ahumada, J.A.,D. Lapointe, and M.D. Samuel. 2004. Modeling the Population
Dynamics of Culex quinquefasciatus (Diptera: Culicidae), along an Elevational
Gradient in Hawaii. J. Med. Entomol. 41 (6):1157-1170.
Armstrong J.A., and W.R. Bransby-Williams. 1961. The Maintenance of a Colony of
Anopheles gambiae With Observations on the Effects of Changes in Temperature.
Bull. WHO 24, 427-435.
Craig, M.H., R.W. Snow, and D. le Seuer. A Climate-Based Distribution Model of
Malaria Transmission in Sub-Saharan Africa. Parasitology Today, vol. 15, no. 3, 1999.
Hay, S.I., Snow, R.W. and Rogers, D.J. (1998) Prediction of malaria seasons in Kenya
using multi-temporal meteorological satellite sensor data. Trans. R. Soc. Trop. Med.
Hyg. 92, 12–20
Ikemoto, T. 2008. Tropical Malaria Does Not Mean Hot Environments. J. Med.
Entomol. 45(6): 963Ð969
Lindsay, S.W. and Martens, W.J.M. (1998) Malaria in the African highlands: past,
present and future. Bull. WHO 76, 33–45.
Lyimo, E.O., W. Takken, and J. C. Koella. 1992. Effect of rearing temperature and
larval density on larval survival, age at pupation and adult size of Anopheles
gambiae. Entomol. exp. appl. 63: 265-271.
Taylor, D. Trans-NIH group assesses response to climate change.
Special thanks to Derrick Parker for the variety of literature that he made available
for my research.