Cory Morin Presentation Outline Outline of Objectives of Study

Investigation of Spatial Mosquito Population Trends Using EOF Analysis: Model Vs Count Data in Pasco County Florida

• Cory Morin

Presentation Outline

• Outline of Objectives of Study

• Background of Research – Why Study Mosquitoes?

• Introduction to DyMSiM

• Model Runs + Correlation and Regression Coefficients

• EOF Analysis

• Conclusions and Discussion

Objectives

• Validate Model (DyMSiM) with Mosquito Count Data

• Using 25 Locations within Pasco County Florida (1995-1997,2002-2004)
• Correlation Coefficients (Daily)
• Regression Coefficients (Daily, Weekly, and Monthly)
• EOF Analysis of Model and Trap Data
• Spring, Summer, and Fall (weekly)

Mosquitoes: Aedes Aegypti

• Characteristics

• Urban, Container Breeding Mosquito
• Tropical Habitat
• Dengue Fever Vector

• Dengue Fever

• 100 Million Cases a Year Worldwide
• 4 Serotypes without Cross Immunity
• Dengue Hemorrhagic Fever from Multiple Infections

Mosquitoes: Culex Quinquefasciatus

Modeling Mosquitoes

• Inputs

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

• Governing Rules

• Development Rates
• Death Rates
• Reproductive Rates
• Larval/Pupa Capacity
• Water Flux (sources and sinks)

Conceptual Model (DyMSiM) Dynamic Mosquito Simulation Model

Data

• Temperature Data was Obtained from the National Climate Data Center

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

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

Sample of Model Run

Regression + Correlation Coefficients

• Regression Coefficient

• Best fit line in the data that minimizes the sum of the square of the error
• Shows how the magnitude of one variable changes with another

• Correlation Coefficient

• Calculated from the square root of the variance explained
• Describes the relationship between two variables (Range from -1 to 1)

Correlation/Pearson Coefficients

EOF Analysis

• Used to Analyze Spatial Patterns in a Dataset

• The 1st EOF Shows the Largest Fraction of Variance Explained in a Dataset

• Found from Eigenvalues and Eigenvectors
• Only a limited number of EOFs are Significant (North Test)

Spring North Test

EOF 1 for Spring

EOF 2 for Spring

Summer North Test

EOF 1 for Summer

Fall North Test

EOF 1 for Fall

EOF 2 for Fall

Conclusions

• 1st EOF Dominates in Each Season for both Trap and Model Data

• One individual location sticks out in particular (Large Population)

• 2nd EOF: Model and Trap Data share some common characteristics but are not identical

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

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

Model Limitations

• “All Models are Wrong, Some are Useful” -George Box

• The model only accounts for climate and land use factors

• Predation, Pesticides, Food Availability, Human Behaviors, and Migration are not accounted for

• Trap Data is Not Truth

• Trapping mosquitoes may largely effect population dynamics
• Microenvironments are important for mosquitoes but are not caught with climate data

Thank You for Your Attention

• Thank You for Your Attention

• Any Questions?

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