Investigation of Spatial Investigation of Spatial Mosquito Population Trends Mosquito Population Trends

UsingUsingEOF Analysis: Model Vs EOF Analysis: Model Vs

Count Data in Pasco Count Data in Pasco County FloridaCounty Florida

Cory MorinCory Morin

Presentation OutlinePresentation Outline

Outline of Objectives of StudyOutline of Objectives of Study Background of Research – Why Study Background of Research – Why Study

Mosquitoes?Mosquitoes? Introduction to DyMSiMIntroduction to DyMSiM Model Runs + Correlation and Model Runs + Correlation and

Regression CoefficientsRegression Coefficients EOF AnalysisEOF Analysis Conclusions and DiscussionConclusions and Discussion

ObjectivesObjectives

Validate Model (DyMSiM) with Validate Model (DyMSiM) with Mosquito Count DataMosquito Count Data– Using 25 Locations within Pasco County Using 25 Locations within Pasco County

Florida (1995-1997,2002-2004)Florida (1995-1997,2002-2004)– Correlation Coefficients (Daily)Correlation Coefficients (Daily)– Regression Coefficients (Daily, Weekly, Regression Coefficients (Daily, Weekly,

and Monthly)and Monthly)– EOF Analysis of Model and Trap DataEOF Analysis of Model and Trap Data

Spring, Summer, and Fall (weekly)Spring, Summer, and Fall (weekly)

Mosquitoes: Mosquitoes: Aedes AegyptiAedes Aegypti

CharacteristicsCharacteristics– Urban, Container Urban, Container

Breeding MosquitoBreeding Mosquito– Tropical HabitatTropical Habitat– Dengue Fever VectorDengue Fever Vector

Dengue FeverDengue Fever– 100 Million Cases a 100 Million Cases a

Year WorldwideYear Worldwide– 4 Serotypes without 4 Serotypes without

Cross Immunity Cross Immunity – Dengue Hemorrhagic Dengue Hemorrhagic

Fever from Multiple Fever from Multiple InfectionsInfections

Picture taken from http://www.interet-general.info/IMG/Aedes-Aegypti-2.jpg

Picture from http://www.cdc.gov/ncidod/dvbid/

dengue/map-distribution-2005.htm

Mosquitoes: Mosquitoes: Culex QuinquefasciatusCulex Quinquefasciatus

Image taken from http://www.lahey.org/Medical/

InfectiousDiseases/WestNileVirus.asp

CharacteristicsCharacteristics– Urban MosquitoUrban Mosquito– Feeds on Humans Feeds on Humans

and Animalsand Animals– West Nile Virus West Nile Virus

VectorVector West Nile VirusWest Nile Virus

– Arrived in New York Arrived in New York 19991999

– Symptoms: Mild Symptoms: Mild Fever-EncephalitisFever-Encephalitis

West Nile Virus Cases and Deaths by Year

02000400060008000

1000012000

1999 2000 2001 2002 2003 2004 2005 2006 2007

Year

Cas

es-D

eath

s Cases

Deaths

Data from CDC.gov

Modeling MosquitoesModeling Mosquitoes

InputsInputs– Temperature, Precipitation, LatitudeTemperature, Precipitation, Latitude– Evaporation Derived (Hamon’s Equation) Evaporation Derived (Hamon’s Equation) – Irrigation/Land Cover Irrigation/Land Cover

Governing RulesGoverning Rules– Development Rates Development Rates – Death Rates Death Rates – Reproductive RatesReproductive Rates– Larval/Pupa CapacityLarval/Pupa Capacity– Water Flux (sources and sinks)Water Flux (sources and sinks)

Conceptual Model (DyMSiM) Dynamic Conceptual Model (DyMSiM) Dynamic Mosquito Simulation ModelMosquito Simulation Model

DataData Temperature Data was Temperature Data was

Obtained from the Obtained from the National Climate Data National Climate Data Center Center

Precipitation Data was Precipitation Data was Obtained from the Obtained from the National Climate Data National Climate Data Center and The Pasco Center and The Pasco County Vector and County Vector and Mosquito Control DistrictMosquito Control District

Mosquito Data was Mosquito Data was Obtained from the Pasco Obtained from the Pasco County Vector and County Vector and Mosquito Control DistrictMosquito Control District

Image from http://pix.epodunk.com/locatorMaps/fl/FL_8834.gif

Sample of Model RunSample of Model Run

Site N3: 1995-1997

0

20

40

60

80

100

120

Jan

-19

95

Ma

r-1

99

5

Ma

y-1

99

5

Jul-

19

95

Se

p-1

99

5

No

v-1

99

5

Jan

-19

96

Ma

r-1

99

6

Ma

y-1

99

6

Jul-

19

96

Se

p-1

99

6

No

v-1

99

6

Jan

-19

97

Ma

r-1

99

7

Ma

y-1

99

7

Jul-

19

97

Se

p-1

99

7

No

v-1

99

7

Date

Mo

sq

uit

o P

op

ula

tio

n

Model

QUI

Regression + Correlation Regression + Correlation CoefficientsCoefficients

Regression CoefficientRegression Coefficient– Best fit line in the data that minimizes Best fit line in the data that minimizes

the sum of the square of the errorthe sum of the square of the error– Shows how the magnitude of one Shows how the magnitude of one

variable changes with anothervariable changes with another Correlation CoefficientCorrelation Coefficient

– Calculated from the square root of the Calculated from the square root of the variance explainedvariance explained

– Describes the relationship between two Describes the relationship between two variables (Range from -1 to 1)variables (Range from -1 to 1)

Correlation/Pearson Correlation/Pearson CoefficientsCoefficients

Time Span D-ValueAverage Correlation

Significant (0.95)

1995-1997 0.4111 0.1343 Yes

2002-2004 0.3821 0.1190 Yes

Time SpanDaily

RegressionWeekly

RegressionMonthly Regression

1995-1997 0.0910 0.7337 0.9354

2002-2004 0.0695 0.7068 1.0270

EOF AnalysisEOF Analysis

Used to Analyze Spatial Patterns in a Used to Analyze Spatial Patterns in a DatasetDataset

The 1st EOF Shows the Largest The 1st EOF Shows the Largest Fraction of Variance Explained in a Fraction of Variance Explained in a DatasetDataset– Found from Eigenvalues and Found from Eigenvalues and

EigenvectorsEigenvectors– Only a limited number of EOFs are Only a limited number of EOFs are

Significant (North Test)Significant (North Test)

Spring North TestSpring North TestNorth Test: Spring Trap Data

0

0.1

0.20.3

0.4

0.5

0.6

0.70.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10

Eigenvalue

Var

iance

Exp

lain

ed

Variance Explained

upper confidence 0.95

lower confidence -0.95

North Test: Spring Model Data

0

0.1

0.20.3

0.4

0.5

0.6

0.70.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10

Eigenvalue

Variance E

xpla

ined

Variance Explained

upper confidence 0.95

lower confidence -0.95

- The first two EOFs in both Whisker Plots are Significant

EOF 1 for SpringEOF 1 for Spring

1st EOF for Trap Data

1st EOF for Model Data

EOF 2 for SpringEOF 2 for Spring2nd EOF Trap Data

2nd EOF Model Data

Summer North TestSummer North Test

North Test: Summer Model Data

0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4 5 6 7 8 9 10

Eigenvalue

Variance E

xpla

ined

Variance Explained

upper confidence 0.95

lower confidence -0.95

North Test: Summer Trap Data

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5 6 7 8 9 10

Eigenvalue

Variance E

xpla

ined

Variance Explained

upper confidence 0.95

lower confidence -0.95

Only EOF 1 is Significant for the Summer

EOF 1 for SummerEOF 1 for Summer

1st EOF for Trap Data

1st EOF for Model Data

Fall North TestFall North TestNorth Test: Fall Trap Data

0

0.1

0.20.3

0.4

0.5

0.6

0.70.8

0.9

1

0 1 2 3 4 5 6 7 8 9 10

Eigenvalue

Var

iance

Exp

lain

ed

Variance Explained

upper confidence 0.95

lower confidence -0.95

North Test: Fall Model Data

0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4 5 6 7 8 9 10

Eigenvalue

Variance E

xpla

ined

Variance Explained

upper confidence 0.95

lower confidence -0.95

The 1st and 2nd EOFs are Significant

EOF 1 for FallEOF 1 for Fall

1st EOF for Trap Data

1st EOF for Model Data

EOF 2 for FallEOF 2 for Fall

2nd EOF for Trap Data

2nd EOF Model Data

ConclusionsConclusions 11stst EOF Dominates in Each Season for both EOF Dominates in Each Season for both

Trap and Model DataTrap and Model Data– One individual location sticks out in particular One individual location sticks out in particular

(Large Population)(Large Population) 22ndnd EOF: Model and Trap Data share some EOF: Model and Trap Data share some

common characteristics but are not common characteristics but are not identicalidentical

Physical Mechanisms Behind the EOFs Physical Mechanisms Behind the EOFs Need to be Analyzed (Surface Cover / Need to be Analyzed (Surface Cover / Precipitation Patters) Precipitation Patters)

Overall, the EOF Analysis Supports the Overall, the EOF Analysis Supports the Utility and Accuracy of DyMSiMUtility and Accuracy of DyMSiM

Model LimitationsModel Limitations ““All Models are Wrong, Some are Useful” All Models are Wrong, Some are Useful”

-George Box -George Box

The model only accounts for climate and The model only accounts for climate and land use factorsland use factors– Predation, Pesticides, Food Availability, Human Predation, Pesticides, Food Availability, Human

Behaviors, and Migration are not accounted forBehaviors, and Migration are not accounted for

Trap Data is Trap Data is NotNot Truth Truth– Trapping mosquitoes may largely effect Trapping mosquitoes may largely effect

population dynamics population dynamics – Microenvironments are important for Microenvironments are important for

mosquitoes but are not caught with climate mosquitoes but are not caught with climate datadata

Thank You for Your Thank You for Your AttentionAttention

Any Questions?Any Questions?