Nra nnh13zda001n equation Chapter 1 Section 1

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ROSES 2013


Equation Chapter 1 Section 1Contents

1Scientific/Technical/Management 2

1.1Objectives and Expected Significance 4

1.1.1Objectives 4

1.1.2Expected Significance 5

1.2Technical Approach and Methodology 6

1.3Perceived Impact to State of Knowledge 14

1.4Relevance to Element Programs and Objectives in the NRA 14

1.5Work Plan 15

1.5.1Key Milestones 16

1.5.2Management Structure 16

1.5.3Contributions of Principal Investigator 16

1.5.4Collaborators 16

2References and Citations 17

3Biographical Sketch 24

3.1Principal Investigator 24

4Current and Pending Support 26

4.1Current Awards 26

4.2Pending Awards 26

5Budget Justification: Narrative and Details 27

5.1Budget Narrative 27

5.1.1Personnel and Work Effort 27

5.1.2Facilities and Equipment 27

5.2Budget Details 27


Objective Summary:

We propose to employ NASA Earth Science products to develop a framework for global application of a recently-developed energy balance model that estimates water temperature and height in artificial containers that serve as habitat for the immature life stages of the dengue vector mosquito Aedes aegypti. The simulations will allow us to produce, with a physically-based approach, global suitability maps for the development of Ae. aegypti. The energy balance model will then be coupled with a dynamic life cycle model to best describe the seasonality and interannual variability of Ae. aegypti population dynamics for locations in North America and the Caribbean where we have field data at several locations for refining and validating our simulations. Finally, the sensitivity of results to climate change scenarios will be explored.

Motivation and Background:

Figure 1-1. National and subnational evidence consensus on complete absence (green) to complete presence (red) of dengue, following Bhatt et al. (2013).
Dengue is the most common and important vector-borne virus in the world (WHO 2009), with 3.5 billion people in over 100 countries living in regions of high risk (Guha-Sapir and Schimmer 2005; Kroeger and Nathan 2006; Beatty et al. 2009). Dengue risk areas (Fig. 1-1) extend across the tropical and sub-tropical Americas and Africa, southeast Asia, India, and Oceana (Guzman and Kouri 2002; Renganathan et al. 2003). A recent study indicates that dengue infections, once thought to number 50-200 million per year (Gubler 1998, 2004; Beatty et al. 2009; WHO 2009), actually total about 390 million per year (Bhatt et al. 2013), with about 1% of cases exhibiting the severe and often deadly dengue hemorrhagic fever (Gubler 1998). Both the geographic range and the magnitude of dengue infections have increased in the past 50 years (WHO 2009), due to population growth and urbanization in endemic areas, increases in global mobility and trade (Westaway and Blok 1997), and the discontinuation of insecticide spraying programs (particularly in the Americas) because of financial and environmental concerns (Gubler 1989). Despite the almost nonexistent risk for dengue in the United States in Fig. 1-1, dengue outbreaks have recently occurred in Key West FL (Graham et al. 2011; Radke et al. 2012), and dengue is present in the U.S./Mexico border region (e.g., Ramos et al. 2008; Hotez et al. 2012), with low income, absence of air conditioning, non-functioning window screens, and inadequate sanitation being strong risk factors (Reiter et al. 2003; Brunkard et al. 2007). An estimated 100,000-200,000 dengue cases occur annually among the Mexican-American population in the United States, presumably near the U.S./Mexico border (Hotez 2008). Aedes aegypti populations extend well into the United States, including southern Arizona (Engelthaler et al. 1997; Hoeck et al. 2003; Merrill et al. 2005; Hayden et al. 2010), and historically the mosquito’s range has extended up the U.S. eastern seaboard, being responsible for the 1793 Yellow Fever outbreak in Philadelphia (Foster et al. 1998). dengue_map.png

The primary dengue vector mosquito Aedes aegypti is closely associated with humans. It lives exclusively in urban and semi-urban areas, preferentially bites humans, and spends its developmental stages in artificial water containers (Focks and Alexander 2006; Halstead 2008; Scott et al. 1993). Ae. aegypti population dynamics thus depend on human behavior and socio-economic factors, including infrastructure, solid waste disposal, housing characteristics, and transportation (Chang et al. 1997; Gubler 1997; Nagao et al. 2003; Kay and Vu 2005; Morrison et al. 2008; Hayden et al. 2010). Another important factor to Ae. aegypti survival and development is climate variability. The primary limiting climatic factor for survival is cold temperature, with an approximate lower boundary of 10°C average winter temperature, below which Ae. aegypti are not observed (Christophers 1960; Focks et al. 1993a; Hopp and Foley 2001; Farnesi et al. 2009; Yang et al. 2009; Richardson et al. 2011). Water availability is a limiting factor in arid regions, and the seasonality of Ae. aegpyti depends on temperature in subtropical regions and rainy seasons (water availability) in tropical regions.

Climate effects extend to development of Aedes aegypti immature mosquitoes in artificial containers. Potential containers for Ae. aegypti immature development include, but are not limited to, small sundry items (e.g., bottles, cans, plastic containers), buckets, tires, barrels, tanks, and cisterns (Morrison et al. 2004; Tun-Lin et al. 2009; Bartlett-Healy et al. 2012). Successful development of immature mosquitoes from eggs to larvae, pupae, and eventually adults is largely dependent on the availability of water and the thermal properties of the water in the containers. The optimal temperature for Ae. aegypti larval and pupal development, with short development times and high survival rates, is in the range of 24°-34°C (Bar-Zeev 1958; Rueda et al. 1990; Tun-Lin et al. 2000; Kamimura et al. 2002; Mohammed and Chadee 2011; Padmanabha et al. 2011a, 2012; Richardson et al. 2011; Farjana et al. 2012). Larval development can be impeded by water temperatures that are too low (8°-12°C) or high enough to cause physical harm, through heat stress (36°-44°C) (Bar-Zeev 1958, Smith et al. 1988, Tun-Lin et al. 2000, Kamimura et al. 2002, Chang et al. 2007, Richardson et al. 2011, Muturi et al. 2012). There also is a growing recognition that the magnitude of the daily temperature range (i.e., fluctuations over the course of a 24-hour period) impact life history traits of Ae. aegypti, including larval development time (Lambrechts et al. 2011, Mohammed and Chadee 2011, Carrington et al. 2013). Other factors that can have negative effects on larval development time or survival include poor nutrient content of the water and resource competition (Braks et al. 2004, Juliano et al. 2004, Padmanabha et al. 2011b, Walsh et al. 2011). For rain-filled containers, there are also risks of a container drying out or of the container over-flowing and the immatures being flushed out (Koenraadt and Harrington 2008, Bartlett-Healy et al. 2011).

Mathematical modeling of mosquito populations is done through statistical methods (i.e., regression or generalized models) or through dynamic life cycle models that simulate the life cycles of cohorts of mosquitoes using a mechanistic approach. Dynamic life cycle models for Aedes aegypti, including CIMSiM (Container Inhabiting Mosquito Simulation Model) and Skeeter Buster, are strongly influenced by water temperature, which impacts the development times and survival rates of eggs, larvae and pupae (Focks et al. 1993a,b; Cheng et al. 1998; Magori et al. 2009; Ellis et al. 2011). Perhaps the greatest limitation of these complex simulation models is the continued use of simplistic empirical relationships to predict water temperature in and water loss from containers based on several meteorological variables. Recent work has shown that physics-based approaches toward modeling container water properties are promising for resolving the complexities of container water dynamics (Tarakidzwa 1997, Kearney et al. 2009). Such models solve for the energy balance of the water inside of the container, taking into account shortwave (solar) and longwave (terrestrial) radiation and heat fluxes (sensible, latent, and ground). The methodology is similar to what is done in land surface models to simulate ground surface and soil temperature (e.g., Chen and Dudhia 2001), except modified for container dimensions. An energy balance container model developed by the proposer, termed the Water Height And Temperature in Container Habitats Energy Model (WHATCH’EM; Steinhoff and Monaghan 2013), solves for water temperature and height for user-specified containers with readily available weather data. Realistic estimation of water temperature and height from WHATCH’EM has potential to improve output from mosquito population models.

While sufficient climate data is available for many tropical and sub-tropical urban areas endemic to dengue, this is not necessarily the case in regions of Africa and southeast Asia. Additionally, rapid urbanization, inadequate infrastructure, and poor quality housing in these regions, along with favorable climate, result in high dengue risk. African dengue risk in particular is often underestimated, due to symptomatically similar illnesses and underreporting (Bhatt et al. 2013). Even in regions with high-quality climate data, variables like cloud cover and ground temperature, important for the surface energy balance, are localized and difficult to directly observe and apply spatially. Satellite remote sensing products and gridded numerical weather prediction products (including atmospheric reanalyses), which utilize data from a variety of sources, offer high quality estimate of climate data in regions where in situ climate data is insufficient. Similarly, gridded global climate model (GCM) output provides physically-based estimates of future climate states, and relatively simple methods exist to downscale coarse resolution GCM output for regional applications.

Given the global importance of dengue, and the strong influence of climate and water temperature to the dengue vector mosquito Aedes aegypti, we propose to model habitat suitability globally by creating a framework that uses high quality gridded NASA Earth Science products (Table 1-1), based on container water temperature and availability estimates from WHATCH’EM. The number of mosquitoes produced by containers (e.g., productivity) of different types and shading scenarios can be determined geographically. We will then couple the NASA/WHATCH’EM framework with the mosquito population model Skeeter Buster to characterize the seasonality and interannual variability of mosquito population dynamics for several locations across North America and the Caribbean where we have detailed data on container distributions and pupal counts. Current climate conditions will then be perturbed, using established methodologies, based on future climate scenarios derived from a NASA GCM (Table 1-1) to determine the sensitivity and response of habitat suitability and mosquito population dynamics to climate change.

NASA Earth Science Product

Variables Used

Grid Spacing

Time Periods Used


2 m air temperature, 2 m specific humidity, 2 m wind speed

0.5° lat, 0.67° lon



Ground surface temperature, Soil temperature

0.25° lat, 0.25° lon



Ground surface temperature, Soil temperature

0.125° lat, 0.125° lon




0.25° lat, 0.25° lon


NASA Langley Cloud Cover

Cloud fraction

0.25° lat, 0.3125° lon



2 m air temperature, 2 m specific humidity, Precipitation

2.0° lat, 2.5° lon

1979-present, 2020-2035, 2060-2075

Table 1-1. NASA Earth Science products used in this study.

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