predictive modeling of west nile virus outbreaks using remotely-sensed data
DESCRIPTION
Predictive Modeling of West Nile Virus Outbreaks Using Remotely-Sensed Data. Dr. Michael Ward Professor of Epidemiology College of Veterinary Medicine Texas A&M University. James Steele Conference on Diseases in Nature Transmissible to Man, Austin, 11 June 2007. James Schuermann - PowerPoint PPT PresentationTRANSCRIPT
Predictive Modeling of West Nile Virus
Outbreaks Using Remotely-Sensed
Data
Dr. Michael Ward
Professor of Epidemiology
College of Veterinary Medicine
Texas A&M University
James Steele Conference on Diseases in Nature Transmissible to Man, Austin, 11 June 2007
2
James SchuermannZoonosis Control GroupTexas Department of State Health Services,
Austin TX
Linda HighfieldDepartment of Veterinary Integrative
BiosciencesTexas A&M University, College Station TX
partial funding provided by theTexas Equine Research Advisory
Committee
3
Outline
1. Background2. Methods3. Results4. Discussion5. Conclusions
1. Background
6
West Nile Virusfamily Flaviviridae genus FlavivirusJapanese Encephalitis serocomplex, includes:
Japanese encephalitisMurray Valley encephalitisSt. Louis encephalitisKunjin
antigenically, all closely related
7
WNV Historyfirst occurrence in U.S.: 1999 ( Bronx Zoo, New York )by 2001: extension of range to include Florida2002: large equine epidemicby 2003: 46 states, 7 Canadian provinces, 5 Mexican statesonly states WNV not detected: Alaska, Hawaii
8
WNV Life Cycle
Vector
Mosquito
Reservoir
Wild birds
Dead end host
Horses and humans
9
WNV Mosquito Vectorsbiological and mechanical vectors
14 species identified
Culex spp. most likely in the U.S.breed in standing water
Cx. pipiens, quiquefasciatus, tarsalis
Aedes spp. may spread disease to horsesbreed in locations where water will be present
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WNV Avian Reservoirs
responsible for distribution
>110 species of birds
most susceptible species include American crows, fish jays, blue jays
game species (wild ducks, geese, pheasants, turkeys, pigeons, doves)
raptors (owls, hawks, eagles)
12
Indicator % countiesdead bird 62equine case 29human case 4infected mosquito pool 3sentinel bird seroconversion
0.8
seropositive wild-caught bird
0.2
First indicators of WNV activity
13
WNV Surveillance Programs
avian mortality surveillance tracking system
mosquito trapping and testing
testing wild birds, sentinel chickens,
horses and humans with neurologic disease
forecasting systems: environmental variables
temperature
precipitation
remotely-sensed data
2. Methods
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reported cases of equine WNV
encephalomyelitis: 2002, 2003 and 2004
time series of case reports, 2-week window
image data: 2-week 1km2 resolution rasters
of the Normalized Difference Vegetation
Index (NDVI)
mean NDVI for each 2-week period
periods with versus without reported cases
autoregressive model: NDVI as a predictor of
equine WNV cases (scaled, transform)
0
10
20
30
40
50
60
13-J
un
27-J
un
11-J
ul
25-J
ul
8-A
ug
22-A
ug
5-S
ep
19-S
ep
3-O
ct
17-O
ct
31-O
ct
14-N
ov
28-N
ov
12-D
ec
No.
cas
es
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What is the NDVI?
Advanced Very High Resolution
Radiometer (AVHRR) sensor,
NOAA polar-orbiting satellite
Normalized Difference Vegetation Index:
visible and near-infrared data
daily observations biweekly 1km2 resolution raster based
on daily maximum observed NDVI value
resulting 1x1 km pixel represents maximum scaled NDVI
value during each 2 weeks of the study period
17
tx02_011024m
Value
High : 0.830000
Low : -0.430000
3. Results
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0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
Jan-02 May-02 Oct-02 Mar-03 Jul-03 Dec-03 May-04 Sep-04
Rep
orte
d ca
ses
0.0
0.1
0.2
0.3
0.4
0.5
0.6
ND
VI
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2-week periods N mean (95% CI)
WNV cases reported 45 0.4390 (0.4219, 0.4561)
WNV cases not reported 33 0.3962 (0.3730, 0.4193)
(P<0.001)
correlation, number of cases reported versus NDVI: 45%
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cases = – 0.9102 + 8.5762 (casesweeks 1–2) – 5.6137 (casesweeks 3–4)
+ 0.9262 (NDVIweeks 1–2) – 0.2661 (NDVIweeks 3–4)
no. observed versus predicted cases highly correlated (rSP 83%,
P<0.001)
-2
-1
0
1
2
3
4
5
6
7
0.0 2.0 4.0 6.0 8.0
observed
predicted
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mean difference, observed versus predicted cases, P= 0.973
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0Ja
n-02
Mar
-02
May
-02
Jul-0
2
Sep
-02
Nov
-02
Jan-
03
Mar
-03
May
-03
Jul-0
3
Sep
-03
Nov
-03
Jan-
04
Mar
-04
May
-04
Jul-0
4
Sep
-04
Nov
-04
Observed
Predicted
4. Discussion
25
Prevention and Control
reduce exposure
indoor housing, repellants?
mosquito control
larvicides, adulticides, environment
vaccination
killed or recombinant canarypox-vectored
2 doses, 3-6 weeks apart; annual booster
26
Forecasting Systems
anticipate increases in risk
optimize control strategies
increased awareness
identify “hotspots”
sentinel warning for zoonotic disease
5. Conclusion
28
remotely-sensed data:availabilitylow-costcoverage
could be used to:enhanced WNV surveillanceprovide early warning of increased riskidentify hotspotswarn of potential zoonotic transmission of WNV