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Climate change
and vector-borne disease
How do we know what we know?
Richard S. Ostfeld
Cary Institute of Ecosystem Studies
Millbrook, New York
“As the atmosphere has
warmed over the past
century, droughts…and
precipitation bursts…have
promoted by various
means the emergence,
resurgence, and spread of
infectious disease.”
--P. Epstein, Sci. Am. 2000
“As the atmosphere has
warmed over the past
century, droughts…and
precipitation bursts…have
promoted by various
means the emergence,
resurgence, and spread of
infectious disease.”
--P. Epstein, Sci. Am. 2000
“Predictions that global
warming will spark
epidemics have little
basis… [because] public
health measures will
inevitably outweigh effects
of climate.”
--G. Taubes, Science, 1997
“Predictions that global
warming will spark
epidemics have little
basis… [because] public
health measures will
inevitably outweigh effects
of climate.”
--G. Taubes, Science, 1997
Hypothesis: Global climate change
will increase the global burden of
infectious disease
Hypothesis: Global climate change
will increase the global burden of
malaria
“The scientific method”
The way science really works
Experiments
Correlations
Models
Resolution: standard / high
Figure 2.
Larvae maturation. Rate of development of larvae as a fraction of
the complete development cycle as a function of the water
temperature in Celsius. Data based on that of Jepson et al. Line
best fit by least squares. X-axis average water temperature, Yaxis: rate of development (in 1/days) as the reciprocal of length
of cycle.
Hoshen and Morse Malaria Journal 2004
3:32 doi:10.1186/1475-2875-3-32
Download authors' original image
Anopheles gambiae larvae
Hoshen & Morse, Malar. J. 2004
Anopheles gambiae larvae – probability of surviving to
maturity
Daily survival
Hoshen & Morse, Malar. J. 2004
Anopheles
pseudopunctipennis
Time to
oviposition
Temperature
Lardeaux et al. 2008
Malar. J.
Relationship between temperature and
malaria parasite development time inside the mosquito
Patz J A, Olson S H PNAS 2006;103:5635-5636
©2006 by National Academy of Sciences
An. pseudopunctipennis
Temperature
Lardeaux et al. 2008
Malar. J.
Afrane et al.
EID, 2008
dwellings, Western Kenya
0.5
Children bitten per day
Mosquitoes per human
8
6
4
2
0
For
est
e
De
igh
De
ste
dH
igh
lan
d
lan
d
ted
cool
warm
0.2
0.1
Low
lan
For
est
ed
De
fore
s
Hig
d
De
fore
s
ted
H
hla
nd
14
12
0.3
0.0
fore
s
ted
igh
lan
d
Low
lan
d
8
hot
Vectorial capacity
Duration of sporogony (days)
dH
fore
0.4
10
8
6
4
6
4
2
2
0
For
est
ed
De
Hig
hla
n
fore
d
0
De
fore
s
ste
dH
igh
lan
d
For
est
ed
ted
L
ow
lan
d
De
Hig
fore
hla
nd
De
ste
fore
s
dH
ted
igh
lan
d
Low
lan
d
Afrane et al.
EID, 2008
dwellings, Western Kenya
0.5
Children bitten per day
Mosquitoes per human
8
6
4
2
0
For
est
e
De
igh
De
ste
dH
igh
lan
d
lan
d
14
12
cool
0.3
0.2
0.1
Correlation: Malaria
risk factors are
higher in warmer
hot areas.
0.0
fore
s
ted
Low
lan
For
est
ed
De
fore
s
Hig
De
fore
s
ted
H
hla
nd
d
ted
igh
lan
d
Low
lan
d
warm
8
10
Vectorial capacity
Duration of sporogony (days)
dH
fore
0.4
8
6
4
6
4
2
2
0
For
est
ed
De
Hig
hla
n
fore
d
0
De
fore
s
ste
dH
igh
lan
d
For
est
ed
ted
L
ow
lan
d
De
Hig
fore
hla
nd
De
ste
fore
s
dH
ted
igh
lan
d
Low
lan
d
General conceptual model for vector-borne diseases
Vector vital
rates and
behavior
Climate
Abiotic
conditions
Risk of human
exposure
Parasite
development
rates
Human
incidence
rates
Force of infection
30 C
Faster development of
vector & parasite, faster
ovipositing & biting rate,
lower larval mortality,
higher adult mortality
rate
Model: Risk of infection
increases with mean
temperature up to
about 30oC (86oF)
Yr 2100
Model: 450,000,000 more people
at risk of malaria in 100 years
Martens et al. 1999
The way science really works
Experiments
Correlations
Models
Challenging a model’s predictions
using correlations
1900
2007
“The quantification of a global recession in malaria…
over the twentieth century…suggests that the
success or failure of our efforts against the parasite
in the coming century are likely to be determined by
factors other than climate change.”
Questioning assumptions
Vector vital
rates and
behavior
Human immunity
Climate
Abiotic
conditions
Risk of human
exposure
Human
incidence
rates
Human behavior
Parasite
development
rates
Causes of the 20th century malaria
“recession”
21st Century Protection
in the developed world
Hypothesis: Increase in
standard of living will
prevent increased risk
from translating into
increased actual cases.
Modeled change in malaria between 2010 and 2050:
Climate and socioeconomic changes
Answer: 210 M more
people at risk even with
4-fold increase in per
capita gross domestic
product.
Béguin et al. 2011. Global Env Change
United States, 2010
Data from CDC
Cases of imported malaria
1000
Will
malaria
re-enter
the
US?
800
600
400
200
0
Af
ric
a
As
ia
Ce
nt
ra
l
Country of origin
Ot
h
Am
er
er
ic a
Anopheles quadrimaculatus
Relationship between temperature and
malaria parasite development time inside the
mosquito
Patz J A, Olson S H PNAS
2006;103:5635-5636
©2006 by National Academy of Sciences
Chikungunya virus “that which bends up”
Aedes aegypti
and Ae. albopictus
6,675 locallytransmitted cases as of
Feb. 21, 2014
Source: CDC
“The disease is not likely to spread
to the United States, because it is
carried by two species of mosquito
– the yellow fever mosquito, Aedes
aegypti, and the Asian tiger
mosquito, Aedes albopictus – that
prefer warm climates.”
Aedes albopictus
Aedes aegypti
Chikungunya virus: experiment
Log10 RNA copies/salivary glands
10
2-day incubation at 28o C!
8
6
o AAPT
Δ ALPROV
4
2
0
0
2
4
6
8
10
12
14
Day after infection
Dubrulle et al. PLoS ONE, 2009
Aedes albopictus: model
Rochlin et al. PLoS ONE 2013
Conclusion: Chikungunya
is highly likely to arrive in
the United States, and
climate change is likely to
help it spread.
Wrap-up
•Warming increases risk
•Infrastructure protects in some cases
•In others, warming will increase disease
Mean temp = 18oC
Square-root arcsine (prevalence)
1.2
“The increase in global
temperatures by 1oC was
accompanied by a two- to
three-fold increase in the
average prevalence of
malaria in birds.”
1.0
0.8
0.6
0.4
0.2
0.0
0
1
Temperature anomaly (°C)
2