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Useful Tools in Mosquito Surveillance Presentations/23... · in Amazonas State, Brazil, January 13...
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Transcript of Useful Tools in Mosquito Surveillance Presentations/23... · in Amazonas State, Brazil, January 13...
Useful Tools in
Mosquito Surveillance
DenominatorsInfection Rates
(MIR and MLE’s)
Data Smoothing
Vector Index
Denominator
The denominator is the lower portion of a fraction used to
calculate a rate or ratio.
1512/01# Sand Flies CollectedDate
Human Bait Collection of Lutzomyia Sand Flies
in Amazonas State, Brazil, January 13 to 17, 1975
1512/01# Sand Flies CollectedDate
Human Bait Collection of Lutzomyia Sand Flies
in Amazonas State, Brazil, January 13 to 17, 1975
3513/011512/01
# Sand Flies CollectedDate
3513/011512/01
# Sand Flies CollectedDate
Human Bait Collection of Lutzomyia Sand Flies
in Amazonas State, Brazil, January 13 to 17, 1975
4514/013513/011512/01
# Sand Flies CollectedDate
4514/013513/011512/01
# Sand Flies CollectedDate
Human Bait Collection of Lutzomyia Sand Flies
in Amazonas State, Brazil, January 13 to 17, 1975
5015/014514/013513/011512/01
# Sand Flies CollectedDate
5015/014514/013513/011512/01
# Sand Flies CollectedDate
Human Bait Collection of Lutzomyia Sand Flies
in Amazonas State, Brazil, January 13 to 17, 1975
6016/015015/014514/013513/011512/01
# Sand Flies CollectedDate
6016/015015/014514/013513/011512/01
# Sand Flies CollectedDate
Human Bait Collection of Lutzomyia Sand Flies
in Amazonas State, Brazil, January 13 to 17, 1975
10017/016016/015015/014514/013513/011512/01
# Sand Flies CollectedDate
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 13 to 17, 1975
0
20
40
60
80
100
120
12-Jan 13-Jan 14-Jan 15-Jan 16-Jan 17-JanDate
Sand
Flie
s
# flies collected
10017/01
6016/01
5015/01
4514/01
3513/01
1513/01
# collectedDate
Human Bait Collection of Lutzomyia Sand Flies
in Amazonas State, Brazil, January 13 to 17, 1975
310017/01
36016/01
25015/01
14514/01
13513/01
11513/01
Persons# collectedDate
Human Bait Collection of Lutzomyia Sand Flies
in Amazonas State, Brazil, January 13 to 17, 1975
2310017/01
136016/01
225015/01
314514/01
213513/01
111513/01
HoursPersons# collectedDate
Human Bait Collection of Lutzomyia Sand Flies
in Amazonas State, Brazil, January 13 to 17, 1975
2310017/01
136016/01
225015/01
314514/01
213513/01
111513/01
HoursPersons# collectedDate
Human Bait Collection of Lutzomyia Sand Flies
in Amazonas State, Brazil, January 13 to 17, 1975
XX
X
X
XX
==
=
=
==
62310017/01
3136016/01
4225015/01
3314514/01
2213513/01
1111513/01
Man hrs.HoursPersons# collectedDate
Human Bait Collection of Lutzomyia Sand Flies
in Amazonas State, Brazil, January 13 to 17, 1975
XX
X
X
XX
==
=
=
==
610017/01
36016/01
45015/01
34514/01
23513/01
11513/01
Man hrs.# collectedDate
Human Bait Collection of Lutzomyia Sand Flies
in Amazonas State, Brazil, January 13 to 17, 1975
==
=
=
==
16.762310017/01
20.03136016/01
12.54225015/01
15.03314514/01
17.52213513/01
15.01111513/01
Flies/Man HrMan HrsHoursPersons# collectedDate
Human Bait Collection of Lutzomyia Sand Flies
in Amazonas State, Brazil, January 13 to 17, 1975
==
=
=
==
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 13 to 17, 1975
0
20
40
60
80
100
120
13-Jan 14-Jan 15-Jan 16-Jan 17-Jan 18-JanDate
Sand
Flie
s
# flies collected / man hr.
# flies collected
5,2804694,995145,4022007
4,7161673,18793,2772006
3,778332,06947,8282005
9232332,15354,1142004
1,1801511,88521,4922003
# Trap Nights
# (+) Pools# Pools
# MosquitoesYear
Trapping Effort, Fairfax County, Trapping Effort, Fairfax County, 20032003--0707
Conclusions
• Numbers can be impressive
• Numbers can be misleading
• Denominators put numbers into perspective
MLEMLE
MLEML
E
MIRMIR
MIRMI
R
MLEMLE
MLEML
E
MIRMIR
MIRMI
R
MLEMLEMLE
MLE
MIRMIR
MIR
MIR
MIR or MLE?
That is the Question
Infection Rate
MLEMLE
MLEML
E
MIRMIR
MIRMI
R
MLEMLE
MLEML
E
MIRMIR
MIRMI
R
MLEMLEMLE
MLE
MIRMIR
MIR
MIR
Mosquito Infection Rate
MIR – Minimum Infection Rate
MLE – Maximum Likelihood Estimation
MLEMLE
MLEML
E
MIRMIR
MIRMI
R
MLEMLE
MLEML
E
MIRMIR
MIRMI
R
MLEMLEMLE
MLE
MIRMIR
MIR
MIR
MIR – Minimum Infection RateAssumes 1 infected mosquito per
pool (sample)
# positive samplestotal # of mosquitoes
tested
X 1,000MIR =
Mosquito Infection Rate
MLEMLE
MLEML
E
MIRMIR
MIRMI
R
MLEMLE
MLEML
E
MIRMIR
MIRMI
R
MLEMLEMLE
MLE
MIRMIR
MIR
MIR
Mosquito Infection Rate
MLE – Maximum Likelihood EstimationConsiders the possibility of more than 1
positive mosquito per pool and compensates for different sized samples
Calculated with an Excel Add-In
http://www.cdc.gov/ncidod/dvbid/westnile/software.htm
Brad Biggerstaff, CDC
Maximum Likelyhood Estimation of WNV in Culex Mosquitoes Collected in gravid Traps, by Week, Fairfax, Va, 2006
0
5
10
15
20
25
30
35
40
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
EPI Week
Infe
ctio
n ra
te p
er 1
,000
MLE Upper Limitr Lower Limit
WNV Infection Rates in Culex Mosquitoes Collected in Gravid Traps, per Week, Fairfax, Va, 2006
0
5
10
15
20
25
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41EPI Week
Infe
ctio
n ra
te p
er 1
,000
MLE MIR
Difference Between MIR and MLE
Difference Between MIR and MLEWNV Infection Rates in Culex Mosquitoes Collected in Gravid Traps, Fairfax, Va, 2003
0
10
20
30
40
50
60
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40EPI Week
Infe
ctio
n ra
te p
er 1
.000
MLE MIR
Conclusions
• MIR does not stand for Mosquito Infection Rate
• You can use either MIR or MLE when IR is < 20, but you should use the MLE when IR is > 20
• Use either MIR of MLE but not both
• MIR’s and MLE’s have to be calculated weekly
• Seasonal MIR’s and MLE’s don’t mean anything, these shouldn’t be used
25,34,
24,
44,36,
35, 34,42,
52,
34,
44,
22,35,
Data Smoothing
25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 25,
34,
24,
44,36,
35, 34,42,
52,
34,
44,
22,
25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 25,34,
24,
44,36,
35, 34,42,
52,
34,
44,
22,35,
Data Smoothing
Data Smoothing is a form of low pass filtering = It blocks out the high
frequency components in order to emphasize the low frequency ones (longer trends)
25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 25,34,
24,
44,36,
35, 34,42,
52,
34,
44,
22,35,
Data Smoothing
The running mean or moving average
The exponential weighted average
25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 25,34,
24,
44,36,
35, 34,42,
52,
34,
44,
22,35,
Data Smoothing
1yk = yk-1 + ---- (xk – xk-n)n
yk = new set of smoothed dataxk = original set of data
n = the size of the set of number
The running mean (1) or moving average
1yk = yk-1 + ---- (xk – xk-n)
nyk = new set of smoothed dataxk = original set of data
n = the size of the set of number
25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 25,34,
24,
44,36,
35, 34,42,
52,
34,
44,
22,35,
Data Smoothing
SUM(xk : xk-n)yk = --------------------n
yk = new set of smoothed dataxk = original set of data
n = the size of the set of number
The running mean (2) or moving average
SUM(xk : xk-n)yk = --------------------
nyk = new set of smoothed dataxk = original set of datan = the size of the set of number
25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 25,34,
24,
44,36,
35, 34,42,
52,
34,
44,
22,35,
Data Smoothing
yk = (1-b)xk + byk-1
The exponential weighted average
yk = new set of smoothed dataxk = original set of data
b = the fraction of the number that is used
yk = (1-b)xk + byk-1yk = new set of smoothed dataxk = original set of datab = the fraction of the number that is used
25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 25,34,
24,
44,36,
35, 34,42,
52,
34,
44,
22,35,
Data SmoothingFairfax Temperature, 2007
0102030405060708090
100
Jan
Feb Mar Apr May Jun Ju
lAug Sep Oct Nov Dec
Month
O F
Block out high frequency components in order to emphasize longer trends
25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 25,34,
24,
44,36,
35, 34,42,
52,
34,
44,
22,35,
Fairfax Tempreature, 2007
0102030405060708090
100
Jan Feb MarAprMay Jun JulAug Sep O ct NovDec
Month
o F
25,34,
24,
44,36,
35, 34,42,
52,
34,
44,
22,35,
25, 35, 44, 24, 36, 34, 42, 52, 44, 34,
Fairfax Temperature, 2007
0
20
40
60
80
100
Jan Feb Mar Apr May Jun Jul
Aug Sep Oct Nov DecMonth
O F
It blocks out the high frequency componentsemphasize low frequency components (TRENDS)
25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 25,34,
24,
44,36,
35, 34,42,
52,
34,
44,
22,35,
Data SmoothingDifferent Data Smoothing Strategies
0
10
20
30
40
50
60
70
80
90
100
Jan
Feb Mar Apr May Jun Ju
lAug Sep Oct Nov DecMonth
o F
Ex. Weight Run. mean (1) Run. mean (2)
25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 25,34,
24,
44,36,
35, 34,42,
52,
34,
44,
22,35,
Data Smoothing
Lutzomyia shannoni collected in light traps, Fairfax, VA, 2005
0
2
4
6
8
10
1 7 13 19 23 24 25 25 30 31 32 33 34 35 36 37 39 43 49
EPI Week
Flie
s pe
r tra
pLutzomyia shannoni collected in light traps,
Fairfax, VA, 2005
0
1
2
3
4
51 7 13 19 23 24 25 25 30 31 32 33 34 35 36 37 39 43 49
EPI Week
Flie
s pe
r tra
p
SUM(xk : xk-n)yk = --------------------n
The running mean (2) or moving average
n = 4
Conclusions
• It blocks out the high frequency components
• It emphasizes the low frequency components emphasizing longer trends
• Shows trends more clearly
• Use carefully
Vector Index (VI)
speciesi
ii P̂N Vectorial Indice Vector Index
Roger Nasci, CDC
Vector Index (VI)
• Estimate of the number of infected mosquitoes collected per trap night
• In a meaningful spatial and temporal sampling unit
• Summed for key mosquito species
• Quantitatively related to human risk (cases)
speciesi
ii P̂N Index Vector
VI uses data from existing mosquito-based surveillance
Proportion infectedIncidence of the disease agent in the vector population
Infection Rate
Key vector or indicator species
Relative abundance of species in terms of trapping effort
Information provided
Speciesi
Number collected per trap nightPopulation Density
UnitsParameter
iN
P̂
2141SD3974Average per trap night233442Total57316124253112044913936342221681
Cx. pipiensAe. albopictusTrap Site1. Calculate mosquito density
VECTOR INDEX (VI)
iN
VECTOR INDEX (VI)
0.950.01690.00020.0033ConfidenceUpper LimitLower LimitInfection Rate
Proportion Infected050Ae. albopictus6050Ae. albopictus5050Ae. albopictus4150Ae. albopictus3050Ae. albopictus2050Ae. albopictus1
Positives# in poolSpeciesPool NumberPools tested for virus
2. Calculate infection rate as proportion (Ae. albopictus)
iP̂
VECTOR INDEX (VI)
0.950.02060.00020.0040ConfidenceUpper LimitLower LimitInfection Rate
Proportion Infected050Cx pipiens5050Cx pipiens4050Cx pipiens3050Cx pipiens2150Cx pipiens1
PositivesNumber in poolSpeciesPool Number
Pools tested for virus
2. Calculate infection rate as proportion (Cx pipiens)
iP̂
VECTOR INDEX (VI)
speciesi
ii P̂N
0.40VI=∑ (Ae. albopictus & Cx. pipiens)
0.160.24VI (individual species)
0.0040.0033Proportion Infected
3974Avg/trap night
Cx. pipiensAe. albopictusVector Index Calculation
3. Calculate individual and combined VI
iN
VECTOR INDEX (VI)
speciesi
ii P̂N
0.40VI=∑ (Ae. albopictus & Cx. pipiens)
0.160.24VI (individual species)
0.0040.0033Proportion Infected
3974Avg/trap night
Cx. pipiensAe. albopictusVector Index Calculation
3. Calculate individual and combined VI
iP̂
VECTOR INDEX (VI)
speciesi
ii P̂N
0.40VI=∑ (Ae. albopictus & Cx. pipiens)
0.160.24VI (individual species)
0.0040.0033Proportion Infected
3974Avg/trap night
Cx. pipiensAe. albopictusVector Index Calculation
3. Calculate individual and combined VI
ii P̂N
VECTOR INDEX (VI)
speciesi
ii P̂N
0.40VI=∑ (Ae. albopictus & Cx. pipiens)
0.160.24VI (individual species)
0.0040.0033Proportion Infected
3974Avg/trap night
Cx. pipiensAe. albopictusVector Index Calculation
3. Calculate individual and combined VI
speciesi
ii P̂N
Vector Density
0
50
100
150
200
250
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
EPI Week
No.
/ Tra
p N
ight
0
10
20
30
40
50
60
Cases
Cases Cx. pipiens+sp Ae. albopictus
.00
.01
.02
.03
.04
.05
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
EPI Week
Prop
ortio
n In
fect
ed
0
10
20
30
40
50
60
Cases
Cases Cx. pipiens+sp Ae. albopictus
Infection Rate (Proportion)
0.00.51.01.52.02.53.03.54.04.55.0
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
EPI Week
Vect
or In
dex
0
10
20
30
40
50
60
Cases
Cases Cx. pipiens+sp Ae. albopictus Combined
Vector Index
VECTOR INDEX (VI)
• Combine data from epidemic and non-epidemic years, look for significant correlation.
• Determine if it can predict cases 2, 3, and 4 weeks later.
Conclusions
•Quantifiable association between VI and cases with onset two weeks later
•VI can be used as a threshold for launching epidemic response (adulticide applications) to stem epidemic transmission.
•VI can be used as a method to determine maximum tolerable adult densities, as a guide for larval management programs
•Has to be calculated weekly, seasonal VI is worthless
Acknowledgements
Dr. Roger Nasci & Dr. Brad Biggerstaff CDC for sharing slides on Vector Index and for the MLE Add-In.
Lutzomyia shannoni collected in light traps, Fairfax, VA, 2005
0
1
2
3
4
5
1 7 13 19 23 24 25 25 30 31 32 33 34 35 36 37 39 43 49
EPI Week
Flie
s pe
r tra
p
WNV Infection Rates in Culex Mosquitoes Collected in Gravid Traps, per Week, Fairfax, Va, 2006
0
5
10
15
20
25
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41EPI Week
Infe
ctio
n ra
te p
er 1
.000
MLE MIR
Thank You
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 13 to 17, 1975
0
20
40
60
80
100
120
13-Jan 14-Jan 15-Jan 16-Jan 17-Jan 18-JanDate
Sand
Flie
s
0.00.51.01.52.02.53.03.54.04.55.0
26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
EPI Week
Vect
or In
dex
0
10
20
30
40
50
60
Cases
Cases Cx. pipiens+sp Ae. albopictus Combined
Vector Index
MIR - MLE Data Smoothing
Denominator