Useful Tools in Mosquito Surveillance
Denominators Infection 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.
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 Date 2/0 # Sand Flies Collected 5
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 Date 2/0 3/0 # Sand Flies Collected 5 35
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 Date 2/0 3/0 4/0 # Sand Flies Collected 5 35 45
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 Date 2/0 3/0 4/0 5/0 # Sand Flies Collected 5 35 45 50
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 Date 2/0 3/0 4/0 5/0 6/0 # Sand Flies Collected 5 35 45 50 60
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 Date 2/0 3/0 4/0 5/0 6/0 7/0 # Sand Flies Collected 5 35 45 50 60 00
20 Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 00 Sand Flies 80 60 40 # flies collected 20 0 2-Jan 3-Jan 4-Jan 5-Jan 6-Jan 7-Jan Date
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 Date 3/0 3/0 4/0 5/0 6/0 7/0 # collected 5 35 45 50 60 00
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 Date 3/0 3/0 4/0 5/0 6/0 7/0 # collected 5 35 45 50 60 00 Persons 2 3 3
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 Date # collected Persons Hours 3/0 5 3/0 35 2 4/0 45 3 5/0 50 2 2 6/0 60 3 7/0 00 3 2
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 Date # collected Persons Hours 3/0 5 X = 3/0 35 X 2 = 4/0 45 X 3 = 5/0 50 2 X 2 = 6/0 60 3 X = 7/0 00 3 X 2 =
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 Date # collected Persons Hours Man hrs. 3/0 5 X = 3/0 35 X 2 = 2 4/0 45 X 3 = 3 5/0 50 2 X 2 = 4 6/0 60 3 X = 3 7/0 00 3 X 2 = 6
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 Date # collected Man hrs. 3/0 5 = 3/0 35 2 = 4/0 45 3 = 5/0 50 4 = 6/0 60 3 = 7/0 00 6 =
Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 Date # collected Persons Hours Man Hrs Flies/Man Hr 3/0 5 = 5.0 3/0 35 2 2 = 7.5 4/0 45 3 3 = 5.0 5/0 50 2 2 4 = 2.5 6/0 60 3 3 = 20.0 7/0 00 3 2 6 = 6.7
20 Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 00 Sand Flies 80 60 40 20 0 # flies collected # flies collected / man hr. 3-Jan 4-Jan 5-Jan 6-Jan 7-Jan 8-Jan Date
Trapping Effort, Fairfax County, 2003-07 07 Year # Mosquitoes # Pools # (+) Pools # Trap Nights 2003 2,492,885 5,80 2004 54,4 2,53 233 923 2005 47,828 2,069 33 3,778 2006 93,277 3,87 67 4,76 2007 45,402 4,995 469 5,280
Conclusions Numbers can be impressive Numbers can be misleading Denominators put numbers into perspective
MIR MIR MIR MIR MLE MLE MIR MIR MLE MLE MLE MLE MLE MIR MIR MIR MLE MLE MIR Infection Rate MIR or MLE? That is the Question
Mosquito Infection Rate MLE MIR MIR MLE MLE MIR MIR MLE MLE MLE MIR MIR MIR MIR Minimum Infection Rate MLE MLE MLE MIR MLE Maximum Likelihood Estimation MIR MIR
MIR MLE Mosquito Infection Rate MIR MIR MLE MLE MIR MIR MIR Minimum Infection Rate Assumes infected mosquito per pool (sample) MLE MIR = # positive samples total # of mosquitoes tested MLE MLE MIR MIR MIR X,000 MLE MLE
MIR MLE Mosquito Infection Rate positive mosquito per MLE pool and MLE MIR MIR MLE Maximum Likelihood Estimation Considers the possibility of more than MIR MLE MLE MLE MIR MIR compensates for different sized samples Calculated with an Excel Add-In MIR MIR http://www.cdc.gov/ncidod/dvbid/westnile/software.htm MLE MLE Brad Biggerstaff, CDC
40 35 Maximum Likelyhood Estimation of WNV in Culex Mosquitoes Collected in gravid Traps, by Week, Fairfax, Va, 2006 MLE Upper Limitr Lower Limit In fectio n rate p er, 000 30 25 20 5 0 5 0 8 9 20 2 22 23 24 25 26 27 28 29 30 3 32 33 34 35 36 37 38 39 40 4 EPI Week
Difference Between MIR and MLE 25 WNV Infection Rates in Culex Mosquitoes Collected in Gravid Traps, per Week, Fairfax, Va, 2006 MLE MIR Infection rate per,000 20 5 0 5 0 8 9 20 2 22 23 24 25 26 27 28 29 30 3 32 33 34 35 36 37 38 39 40 4 EPI Week
Difference Between MIR and MLE 60 50 WNV Infection Rates in Culex Mosquitoes Collected in Gravid Traps, Fairfax, Va, 2003 MLE MIR Infection rate per.000 40 30 20 0 0 8 9 20 2 22 23 24 25 26 27 28 29 30 3 32 33 34 35 36 37 38 39 40 EPI Week
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
Data Smoothing 25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 34, 25, 44, 36, 24, 35, 52, 42, 34, 44, 34, 22, 34, 25, 44, 36, 24, 35, 52, 42, 34, 44, 34, 22, 35,
Data Smoothing Data Smoothing is a form of low pass filtering = 25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 34, 25, 44, 36, 24, 35, 52, 42, 34, 44, 34, 22, 35, It blocks out the high frequency components in order to emphasize the low frequency ones (longer trends)
Data Smoothing 25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 34, 25, 52, 44, 42, 44, 36, 24, 34, The running mean or 35, moving average 34, 22, 35, The exponential weighted average
Data Smoothing The running mean () or moving average y k = y k- + ---- (x k x k-n ) n 25, 35, 44, 24, 36, 34, 42, 52, 44, 34, y k = new set of smoothed data x k = original set of data 34, 25, 52, 44, 42, 44, 36, 24, 34, 35, 34, n = the size of the set of number 22, 35,
y k = y k- + ---- (x k x k-n ) n y k = new set of smoothed data x k = original set of data n = the size of the set of number
Data Smoothing The running mean (2) or moving average SUM(x k : x k-n ) y k = -------------------- n 25, 35, 44, 24, 36, 34, 42, 52, 44, 34, y k = new set of smoothed data x k = original set of data 34, 25, 52, 44, 42, 44, 36, 24, 34, 35, 34, n = the size of the set of number 22, 35,
SUM(x k : x k-n ) y k = -------------------- n y k = new set of smoothed data x k = original set of data n = the size of the set of number
Data Smoothing The exponential weighted average y k = (-b)x k + by k- 25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 34, 25, 52, 44, 42, 44, 36, 24, 34, 34, 22, 35, y k = new set of smoothed data x k = original set of data 35, b = the fraction of the number that is used
y k = (-b)x k + by k- y k = new set of smoothed data x k = original set of data b = the fraction of the number that is used
25, 35, 44, 24, 36, 34, 42, 52, 44, 34, O F 00 90 80 70 60 50 40 30 20 0 0 Jan Feb Mar Fairfax Temperature, 2007 Data Smoothing Apr 34, 25, May Jun 44, 36, 24, 35, Jul 52, 42, 34, 44, Block out high frequency components in order to emphasize longer trends Month 34, Aug Sep Oct Nov Dec 22, 35,
25, 35, 44, 24, 36, 34, 42, 52, 44, 34, o F 00 90 80 70 60 50 40 30 20 0 0 Fairfax Tempreature, 2007 34, 25, 44, 36, 24, 35, Jan F eb Mar Apr May J unjul Month 52, 42, 34, 44, 34, 22, 35, Aug SepO ct Nov D ec
00 Fairfax Temperature, 2007 25, 35, 44, 24, 36, 34, 42, 52, 44, 34, O F 80 60 34, 25, 44, 36, 24, 35, 52, 42, 34, 44, 34, 22, 35, 40 20 0 Jan Feb Mar Apr It blocks out the high frequency components emphasize low frequency components (TRENDS) May Jun Jul Month Aug Sep Oct Nov Dec
00 90 Different Data Smoothing Strategies Data Smoothing Ex. Weight Run. mean () Run. mean (2) o F 80 70 60 25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 50 34, 25, 44, 36, 24, 35, 52, 42, 34, 44, 34, 22, 35, 40 30 20 0 0 Jan Feb Mar Apr May Jun Jul Month Aug Sep Oct Nov Dec
0 Lutzomyia shannoni collected in light traps, Fairfax, VA, 2005 Data 8 Smoothing Flies per trap 25, 35, 44, 24, 36, 34, 42, 52, 44, 34, 6 4 2 0 7 34, 25, 44, 36, 24, 35, 3 9 23 24 52, 42, 34, 25 25 30 3 44, 32 EPI Week 34, 33 22, 35, 34 35 36 37 39 43 49 The running mean (2) or moving average 5 Lutzomyia shannoni collected in light traps, Fairfax, VA, 2005 SUM(x k : x k-n ) y k = -------------------- n n = 4 4 3 2 0 7 3 9 23 24 25 25 30 3 32 33 34 35 36 37 39 43 49 Flies per trap EPI Week
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) Indice Vector Vectorial Index i species NiPˆ i 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)
VI uses data from existing mosquito-based surveillance Vector Index i species Ni Pˆ i Parameter Information provided Units Population Density N i Infection Rate Pˆ Species i Relative abundance of species in terms of trapping effort Incidence of the disease agent in the vector population Key vector or indicator species Number collected per trap night Proportion infected
VECTOR INDEX (VI). Calculate mosquito density Trap Site 2 3 4 5 6 Total Average per trap night SD Ae. albopictus Ni 68 42 39 20 42 3 442 74 4 Cx. pipiens 2 63 49 3 2 57 233 39 2
VECTOR INDEX (VI) 2. Calculate infection rate as proportion (Ae. albopictus) Pools tested for virus Pool Number Species # in pool Positives Ae. albopictus 50 0 2 Ae. albopictus 50 0 3 Ae. albopictus 50 4 Ae. albopictus 50 0 5 Ae. albopictus 50 0 6 Ae. albopictus 50 0 Proportion Infected Infection Rate 0.0033 Pˆ i Lower Limit Upper Limit Confidence 0.0002 0.069 0.95
VECTOR INDEX (VI) 2. Calculate infection rate as proportion (Cx pipiens) Pools tested for virus Pool Number 2 3 4 5 Proportion Infected Infection Rate 0.0040 Pˆ i Species Cx pipiens Cx pipiens Cx pipiens Cx pipiens Cx pipiens Lower Limit 0.0002 Number in pool 50 50 50 50 50 Upper Limit 0.0206 Positives 0 0 0 0 Confidence 0.95
VECTOR INDEX (VI) 3. Calculate individual and combined VI Vector Index Calculation Ae. albopictus Cx. pipiens Avg/trap night i species N ipˆ i Proportion Infected Ni 74 0.0033 39 0.004 VI (individual species) 0.24 0.6 VI= (Ae. albopictus & Cx. pipiens) 0.40
VECTOR INDEX (VI) 3. Calculate individual and combined VI Vector Index Calculation Ae. albopictus Cx. pipiens Avg/trap night i species N ipˆ i Proportion Infected Pˆ i 74 0.0033 39 0.004 VI (individual species) 0.24 0.6 VI= (Ae. albopictus & Cx. pipiens) 0.40
VECTOR INDEX (VI) 3. Calculate individual and combined VI Vector Index Calculation Ae. albopictus Cx. pipiens Avg/trap night i species N ipˆ i Proportion Infected 74 0.0033 39 0.004 VI (individual species) N ipˆ i 0.24 0.6 VI= (Ae. albopictus & Cx. pipiens) 0.40
VECTOR INDEX (VI) 3. Calculate individual and combined VI Vector Index Calculation Ae. albopictus Cx. pipiens Avg/trap night i species N ipˆ i Proportion Infected 74 0.0033 39 0.004 VI (individual species) 0.24 0.6 VI= (Ae. albopictus & Cx. pipiens) i species N ipˆ i 0.40
Vector Density Cases Cx. pipiens+sp Ae. albopictus No./ Trap Night 250 200 50 00 50 60 50 40 30 20 0 Cases 0 26 27 28 29 30 3 32 33 34 35 36 37 38 39 40 EPI Week 0
Infection Rate (Proportion) Cases Cx. pipiens+sp Ae. albopictus Proportion Infected.05.04.03.02.0 60 50 40 30 20 0 Cases.00 26 27 28 29 30 3 32 33 34 35 36 37 38 39 40 EPI Week 0
Vector Index Cases Cx. pipiens+sp Ae. albopictus Combined Vector Index 5.0 4.5 4.0 3.5 3.0 2.5 2.0.5.0 0.5 0.0 26 27 28 29 30 3 32 33 34 35 36 37 38 39 40 EPI Week 60 50 40 30 20 0 0 Cases
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.
25 WNV Infection Rates in Culex Mosquitoes Collected in Gravid Traps, per Week, Fairfax, Va, 2006 Thank You Infection rate per.000 Vector Index 20 5 0 5 0 5.0 4.5 4.0 3.5 3.0 2.5 2.0.5.0 0.5 0.0 MLE MIR MIR - MLE 8 9 20 2 22 23 24 25 26 27 28 29 30 3 32 33 34 35 36 37 38 39 40 4 EPI Week Cases Cx. pipiens+sp Ae. albopictus Combined Vector Index 26 27 28 29 30 3 32 33 34 35 36 37 38 39 40 EPI Week 60 50 40 30 20 0 0 Cases Flies per trap 5 4 3 2 0 Lutzomyia shannoni collected in light traps, Fairfax, VA, 2005 7 3 9 23 24 25 25 30 3 32 33 34 35 36 37 39 43 49 Sand Flies 20 00 Data Smoothing 80 60 40 20 0 EPI Week Human Bait Collection of Lutzomyia Sand Flies in Amazonas State, Brazil, January 3 to 7, 975 Denominator 3-Jan 4-Jan 5-Jan 6-Jan 7-Jan 8-Jan Date