Download Kitron - Vector-Borne and Zoonotic Diseases

Vector-Borne and Zoonotic
Diseases: Climate, Landscape
and Transmission
IMED 2009
15 Feb. 2009, Vienna, Austria
Uriel Kitron
Emory University, Atlanta, GA
Vector borne zoonoses - arthropods,
humans and other animals
Many vectorborne diseases
are zoonoses
Different roles
and impacts
for wildlife,
domestic
animals and
humans
Complex
Impact of
climate
Non-zoonotic: Malaria, Dengue
WNV
Yellow fever
TBE
Lyme disease
Plague
Sleeping
sickness,
Leishmaniasis
Chagas
disease
CCHF
Prerequisites for an
active zoonotic VBD focus
Vector survival
Presence of reservoir hosts
Pathogen amplification
and transmission
Opportunities for
human exposure
(Some relevant) Components of
Climate (weather)
Temperature (average, min, max,
fluctuations)
Rainfall (amount, distribution, pattern,
fluctuations)
Humidity (relative, absolute, fluctuations)
Extreme events (occurrence, frequency,
level)
Differential impacts, separately and in
combination, on VBZD transmission dynamics
(vector, pathogen, reservoir, human exposure)
Climate variation and change:
Temporal and Spatial scales
Temporal resolution
Long term changes
warming
variability in weather
ocean depth
snow pack, melt dates
Medium term Cycles
ENSO ( El Niño)
NAO
Short term changes –
Weather
Storm severity
Extremes & Variability
Spatial resolution
Biosphere
Landscape
Ecosystem
Community
Species interactions
Species
populations
Individuals
Intra-host and vector
Climate Variability vs. Climate Change
Climate Variable
High
Increasing Rate of Increasing
Average, Greater Extremes
Average Trend
(solid line)
Actual Measure
(dashed line)
Low
Time
Climate, weather and VBZD
Temperature
Vector survival
Rainfall
Presence of
reservoir hosts
Humidity
Pathogen transmission
Variability
Extreme
events
Opportunities for
human exposure
Temperature
Human
Exposure
Rainfall
Extreme events
Human
exposure
Reservoir
Human
exposure
Rainfall
Extreme events
Reservoir
Temperature
Reservoir
Rainfall
Vector
Vector
Vector
Some associations between climate components and VBZD
Temperature
Extreme events
How will climate change impact
transmission – infection and disease?
Infection of reservoir hosts
Disease of human hosts
How can we relate global changes
to very local processes?
What happens when we oversimplify?
Warmer = buggier
Do we now enough about
climate per se and VBZD?
The usual suspects
Other Vectors
Mosquito borne
Malaria
Dengue
West Nile virus
Rift Valley fever
Chikungunya
Leishmaniases
Tick-borne
encephalitis
Lyme disease
Bluetongue
Chagas disease
An example when only temperature is considered
2050 projection for Malaria
(from Martens et al., 1999)
DJ Rogers & SE Randolph. 2000.
Science 289, 1763 -1766
The Global Spread of Malaria
in a Future, Warmer World
The frequent warnings that global climate change will allow malaria to spread are
based on biological transmission models driven by temperature. In an alternative
statistical approach using current multivariate climatic constraints, future distributions
showed remarkably few changes, even under the most extreme scenarios.
rios
Lyme disease in US
Projected distribution of climate-based
habitat suitability for I. scapularis
in US (Brownstein et al, 2005)
But he movement of lizards northward could result in the
disruption of the enzootic cycle of B. burgdorferi in the North
Present and predicted
future distribution of
tick-borne encephalitis
(TBE) virus in Europe
Probability
= 0.65 - 1.0
= 0.55 - 0.649
= 0.50 - 0.549
= 0.45 - 0.499
= 0.35 - 0.449
= 00 - 0.349
= Observed
Based on climate
surfaces derived from
medium-high
scenarios in 2020s.
(Redrawn from
Randolph & Rogers
2000)
Predicted (2020)
Network of independent but synergistic biological and non-biological factors
Examples of data from Estonia, Latvia, Lithuania, Slovenia and Czech Republic.
-2
4
2000
2000
8
1990
0
1980
12
1970
2
1970
2005
2000
1995
1990
1985
16
1990
Mean daily max temp
2000
1990
1980
1980
1970
More hosts
for adult ticks
CZ
20
15
15
10
10
5
5
0
2000
1990
1980
0
1970
40
LV
2000
Roe deer &
wild boar/1000 ha
60
20
1990
2000
1990
1970
1980
LV
80
20
0
2000
Increase in rodent
populations
(transmission hosts)
100
1990
Regeneration of
shrubs
4
21-30.Apr
Increased coco-feeding
transmission of TBEV
More infected ticks
1980
2000
1990
1980
1970
Greater human
exposure to ticks
in forests?
1970
field crops LV
0
20
SI
Adult ticks , May
10
24
20
0
20
ES
6
1980
30
1.Mar-20.Apr
1970
wooded LV
0
CZ
1975
2000
1990
0
1970
20
0
8
1980
1990
2000
LV
20
50
Sudden increase
in Spring temperature
40
CZ
5
More wealth
& leisure
40
TBE cases/100,000 population
10
Global brightening ??
Higher TBE incidence
60
2000
1990
20
1980
output LV
20
SI
1980
CZ
LV
40
15
1970
60
% unemployment
SI
80
1970
60
40
Environmental
awareness?
Reduced
industrial
pollution
Higher
unemployment
100
% of total
national land area
Nationl cattle herd,
% of mean for 1986-88
Decline of
agriculture
LT
80
1980
SocioSocio-economic
transition
employees
100
1970
Industrial employees,
Industrial output,
% of pre-1990 level
TBE
More ticks
Randolph SE, Microbes & Infection (2008)
(2008) 30, 209209-16
Human population growth is the
greatest single factor driving
changes at all scales
Climate change
Urbanization
Habitat fragmentation – extended ecotone
Loss of habitat complexity and species diversity
Pollution/eutrophication
Globalization of commerce
Some anthropogenic changes
Urbanization – demographics,
migration, biodiversity
Agricultural development – crops,
water management, migration
Forestation – Both deforestation
and reforestation
Global warming, other climate changes
Rate of
Change
is on the
increase
Fine-scale patterns of transmission,
amplification, and evolution of West Nile
virus in a “hot spot” in suburban Chicago,
USA
Uriel Kitron1, Tony Goldberg1, Marilyn Ruiz1, Ned Walker2,
Jeff Brawn1, Gabe Hamer2, Scott Loss1, and Luigi Bertolotti3
1
University of Illinois, USA
2 Michigan State University, USA
3 University of Illinois and U. of Torino, Italy
NSF/NIH Ecology of Infectious Disease
West Nile Virus in Illinois
WNV appeared in NYC during 1999 (arrived from the old world)
2001 - 123 positive bird specimens, 0 human cases
2002 - 884 human cases, 66 deaths, most in U.S. (4,156/284)
Over 680 cases occurred in Chicago and surroundings
2003 - 54 human cases, 1 death (U.S – 9,862/264)
2004 - 60 human cases, 4 deaths (U.S. – 2,539/100)
2005 - 252 human cases, 12 deaths (U.S. – 3000/119)
2006 - 215 human cases, 10 deaths (U.S. – 4269/177)
2007 - 57 human cases, 4 deaths (U.S. – 3,576/115)
2002
2003
WNV cases in Illinois 2002-2006
1000
900
800
700
600
500
400
300
2005
200
2006
100
2004
2003
Date
10/9
10/2
9/25
9/18
9/11
9/4
8/28
8/21
8/14
8/7
7/31
7/24
7/17
7/10
7/3
0
6/26
cumulative cases
2002
Outbreak years
were hot and dry
Distribution
of human
WNV cases
in the
greater
Chicago
area, 2002
Landscape
classification
Each different
color represents a
place with a
common set of
factors related to
housing,
vegetation, socioeconomics, and
land use.
Urban Type 5, dominated by 40s and 50s
housing. Mostly white, moderate vegetation
and moderate population density.
435 cases (64%) were in this group,
2.27 cases per 10,000 people (RR>3.5).
(All other types <0.65 cases per 10,000)
Area characterized
by many
undocumented
storm drains
In hot dry years
standing water
with organic
matter provide
habitat for Culex
mosquito larvae
High Spatial heterogeneity
among sites within the “hot spot” area
Mosquito
infection
July 30 –
Aug 19,
2006
Exceptional variation within hot spots
Landscape, mosquito vector, avian host , and
virus diversity and ecology
“Hot spots” within the greater Chicago area result
from:
Natural landscape (vegetation)
Built environment (housing type and density)
Climate and weather (rainfall, temperature)
Socio-demographics and mosquito control
These highly local processes may “coalesce” to
create broader, regional patterns of amplification.
WNV in Georgia, USA
Human cases in Georgia ~10 times lower than in Illinois
WNV cases in Georgia 2001-2007
WNV cases in Illinois 2002-2006
1000
100
900
90
2002
800
2002
2003
2004
2005
2006
2007
80
Cumulative cases
700
600
500
400
300
2005
200
2006
70
60
50
40
30
20
2003
2004
100
10
Date
10/9
10/2
9/25
9/18
9/11
9/4
8/28
8/21
8/14
8/7
7/31
7/24
7/17
7/10
7/3
0
6/26
cumulative cases
2001
0
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Month
Why is WNV transmission in Georgia that low ?
Two other examples –
Eco-epidemiological studies supported by
NIH/NSF EID program
Chagas disease – vector distribution, habitat
modification and role of various zoonotic hosts
T. Cruzi in blood
Schistosomiasis in coastal Kenya – patterns of
infection and snail dispersal
during and after a drought
Eco-Epidemiology of Chagas
Disease in the Gran Chaco of
Argentina
R Gurtler, C Cecere, G Vazquez-Prokopec, P Marcet, L Ceballos,
M Cardinal, J Guarevitz - Univ. of Buenos Aires, Argentina
J Cohen - Rockefeller University, NY
S Blanco, D Canale, C Spillmann – National Vector
Control Program, Argentina
M Lauricella -, Inst. Fatala Chabén, Argentina
E Dotson – CDC
JP Dujardin - IRD-CNRS, France
U Kitron - Univ. of Illinois
Supported by NIH/NSF Ecology of Infectious Disease Program (NIH –
Fogarty)
The Gran Chaco of Argentina, Bolivia,
Paraguay and Brazil
1) High levels of poverty
2) Low population density
3) Mostly rural population
4) Subsistence economy
5) Limited health services,
political support
6) High disease burden
7) High T. infestans infestation
prevalence and transmission
of T. cruzi.
soybean boom
Changes in land use and ownership, deforestation, and accelerated
environmental degradation irreversible loss of biodiversity
Active dispersal of natural populations of T.
(A) Amamá
infestans
Transects
Light trap
Vazquez Prokopec et al. 2004, 2006
Flight from peridomestic sources determines the
reinfestation of houses after insecticide applications
Peak of dispersal in late summer (March) Association with climatic variables
1.4
1.2
Square root
(no. adults/no. light traps)
Square root
(n o . a d u lts/n o . lig h t tra p s)
1.6
1
0.8
0.6
0.4
0.2
0
0
2
4
6
8
Wind speed (km/h)
Negative association
with wind speed <
5km/h
10
12
Positive association with
temperature > 23 ºC
and relative humidity
Changes in climate/weather and landscape over three
decades since 1980:
Extensive deforestation resulted in reduced
number of refuges for opossums and bugs
Cycles of drought and wet years impact bug survival
Complex impact on
very narrow window
of bug dispersal
Other examples - concerns
Malaria – highlands
– return to temperate zones
Dengue – spread north and south
(esp. in Americas)
Other arboviruses:
> Rift Valley fever – introduction to US, Europe?
> Bluetongue and Chikungunya fever –
already in Europe, risk to US
Other viruses, bacteria, protozoa –
known and unknown
Effect of Location – Latitude, Altitude, Distance
from coast
To understand the impact of climate
change on VBZD dynamics, we need:
Better tools for vector and reservoir population
assessments
Long term studies – baseline data and surveillance
Study pathogen transmission dynamics
Study disease specific and multi-parasite systems
Consider Spatial/temporal scale and heterogeneity
Improved Global communication - ProMed
Improved understanding of associations between
(unchanging) climate and the complex
components of VBZD
Some needed data - Accurate
measures of the components of:
C
Vectorial Capacity
– Population size
– Blood feeding habit
– Survivorship
– Vector competence
= ma2 pn / (–lnp)
Pathogen persistence
- Survival
- Amplification
Human exposure
- Behavior
- SES
Reservoir Host
– Distribution in time
and space
– Behavior
– Susceptibility
– infectiousness
Pathogen transmission
- Vector-reservoir
- Vector-human
- Other routes
Conclusions
Climate change may enhance (or disrupt)
transmission of vector borne zoonoses,
But
other (mostly anthropogenic) factors can have
even more impact on transmission and disease
(in synergy or antagonistically), rendering the
"warmer=buggier" approach highly simplistic
Observational and modeling studies must be
interpreted cautiously before they are used to
forecast changes in vector and host populations
and the risk of outbreaks or spread of disease?