Modeled Water Table Depth

Arboviral Transmission Risk Model

Jonathan Day

Jeffery Shaman

Roxanne Connelly

Gregory Ross

 

         

2014 Updates

(Week 18) Arboviral Epidemic Risk for Florida (PDF) 05/01/2014

2013 Updates

(Week 14) Arboviral Epidemic Risk for Florida (PDF) 04/03/2013

2012 Updates

(Week 33) Arboviral Epidemic Risk for Florida (PDF) 08/14/2012

(Week 28) Arboviral Epidemic Risk for Florida (PDF) 07/09/2012

(Week 23) Arboviral Epidemic Risk for Florida (PDF) 06/12/2012

(Week 18) Arboviral Epidemic Risk for Florida (PDF) 04/27/2012

2011 Updates

(Week 31) Arboviral Epidemic Risk for Florida (PDF) 08/01/2011

(Week 27) Arboviral Epidemic Risk for Florida (PDF) 07/03/2011

(Week 24) Arboviral Epidemic Risk for Florida (PDF) 06/11/2011

(Week 20) Arboviral Epidemic Risk for Florida (PDF) 05/20/2011

2010 Updates

(Week 42) Arboviral Epidemic Risk for Florida (PDF) 10/18/2010

(Week 36) Arboviral Epidemic Risk for Florida (PDF) 09/09/2010

(Week 30) Arboviral Epidemic Risk for Florida (PDF) 07/24/2010

(Week 25) Arboviral Epidemic Risk for Florida (PDF) 06/22/2010

(Week 22) Arboviral Epidemic Risk for Florida (PDF) 05/31/2010

(Week 16) Arboviral Epidemic Risk for Florida (PDF) 04/20/2010

(Week 13) Arboviral Epidemic Risk for Florida (PDF) 03/28/2010

(Week 07) Arboviral Epidemic Risk for Florida (PDF) 02/18/2010

2009 Updates

(Week 32) MWTD Arboviral Epidemic Risk for Peninsular Florida (PDF) 08/12/2009

Mid-season EEE Transmission Analysis for Florida (PDF) 06/30/2009

(Week 18) MWTD Arboviral Epidemic Risk for Peninsular Florida (PDF) 05/06/2009

2008 Updates

Florida Arboviral Epidemic Risk for 2008 - Final (PDF)

07/21/2008 (Week 29) MWTD Arboviral Epidemic Risk for Peninsular Florida (PDF)

06/08/2008 (Week 23) MWTD Arboviral Epidemic Risk for Peninsular Florida (PDF)

04/25/2008 (Week 17) MWTD Arboviral Epidemic Risk for Peninsular Florida (PDF)

2007 Updates

09/09/2007 (Week 36) MWTD Arboviral Epidemic Risk for Peninsular Florida (PDF)

07/22/2007 (Week 29) MWTD Arboviral Epidemic Risk for Peninsular Florida (PDF)

Modeled Water Table Depth and Arbovirus Transmission

The efficient and effective surveillance for mosquito-borne viruses including St. Louis encephalitis virus (SLEV), eastern equine encephalitis virus (EEEV), and West Nile virus (WNV) include the monitoring of a combination of biotic and abiotic factors (Day and Lewis 1992). Biotic factors include vector and avian amplification host population dynamics, the number of susceptible secondary hosts, and the abundance and distribution of virus. These biotic cycles (vector and amplification host population dynamics and the spatio-temporal distribution of virus) are driven by abiotic factors, especially rainfall, drought, and temperature. Because abiotic factors are measurable, there exist pre-epidemic abiotic signatures that are predictive of arboviral epidemic transmission. These epidemic signatures can be tracked, measured, quantified, and presented visually to help forecast human arboviral epidemics. In Peninsular Florida (south of 29o 30� N Latitude), one of the most reliable epidemic signatures is modeled water table depth (WTD) that can be tracked throughout the year and used to predict arboviral transmission (Shaman et al. 2004a, Shaman et al. 2005).

The variation of WTD in space and time is a principal determinant of where and when pools of water form at the land surface, thus creating temporary mosquito breeding habitats that are especially prized by arboviral vectors in the genus Culex. To model WTD, a suite of meteorological and physical variables that include precipitation, temperature, area soil and vegetation type, and antecedent meteorological conditions must be accounted for so that evapotranspiration, water movement within the soil column, and river, stream, and canal runoff can be quantified. In addition, topography must be measured and quantified for the flow of water across the land surface, runoff rates, and the local convergence of water in lowlands (surface pooling) to be modeled accurately.

Water table depth has been modeled in Peninsular Florida and is currently used to track and predict arboviral transmission (Shaman and Day 2005, Shaman et al. 2004a). To track WTD and meteorologically-driven Culex mosquito reproduction, population age structure, and arboviral epidemic risk in Florida, we combined two mathematical models. The first was a soil column model that simulates the vertical movement of water and heat in the soil column and the exchange of water and heat between the soil, vegetation, and atmosphere. This model was combined with a second, the TOPMODEL (TOPography-based hydrology MODEL), which incorporates the statistics of topography to track the horizontal movement of shallow groundwater from the uplands to the lowlands. The hydrologic modeling technique we use is based on a blend of these two models to form a dynamic hydrology model referred to as the Topographically Based Hydrology (TBH) model.

The TBH model has been used to simulate variations in WTD at 589 reporting stations located throughout Peninsular Florida (Figure 1). Mean area WTD provides an integrated measure of near surface soil wetness conditions. It is the rise and fall of the modeled WTD that determines where and when pools of water form at the land surface, thereby creating potential mosquito breeding habitats, especially for the Culex mosquitoes that are so important in the amplification and transmission of arboviruses. The TBH model permits, 1) calculation of the saturated fraction within the watershed (partial contributing area) and the groundwater flow that supports the watershed, 2) the mean WTD, and 3) a probability density function for soil moisture deficit derived from topographic statistics. Using the TBH model, we can produce a three-dimensional picture of soil moisture distribution within catchments. This approach to modeling the land surface has been validated at several catchments, ranging in scale from the Red Arkansas Basis (570,000 km2) to the Black Rock Forest catchment (1.34 km2).

The TBH model was used by Shaman et al. (2002) to measure surface wetness and identify potential fresh and swamp water mosquito breeding habitats in two northern New Jersey watersheds. Surface wetness was positively associated with adult abundance of the dominant floodwater mosquito species Aedes vexans and with the swamp water species Anopheles walkeri. The abundance of Culex pipiens, an important WNV vector and a non-floodwater species that breeds in polluted, eutrophic waters, was negatively correlated with WTD. Knowledge of floodwater and permanent water mosquito oviposition behavior can be coupled with modeled WTD permitting real-time monitoring and forecasting of mosquito population emergences and age structures at high spatial and temporal resolution (Shaman and Day 2005). These predictions enable public health agencies to institute mosquito control measures prior to arboviral amplification and the subsequent epidemic transmission of mosquito-borne viruses.

We recently used the TBH model to evaluate historical and real-time arboviral transmission data for SLEV (Shaman et al. 2004a) and WNV (Shaman et al. 2005) in Peninsular Florida. Two major SLEV epidemics were reported in the Florida peninsular during the summers of 1977 and 1990. These two epidemics were remarkably similar in the temporal and spatial distributions of human cases (Day 2001). Additionally, the TBH models for WTD during these two epidemic years were nearly identical (Figure 2). These two epidemic years provide a WTD signature for SLE epidemics in Peninsular Florida. From Figure 2, it is evident that epidemics require a deep spring drought, where the top of the water column is located between 1.0 and 1.5 meters below the land surface, to set the stage for widespread SLE transmission later in the year. We have proposed (Day and Shaman 2007, Shaman et al. 2004b) that deep drought during the winter and spring forces wild avian amplification hosts and vector mosquitoes into contact in focal freshwater habitats. A mid-year wetting event allows infected mosquitoes and wild birds to disperse from these initial amplification sites. A secondary dry-down (circled area on Figure 2) allows the establishment of secondary amplification foci where additional mosquitoes and birds become infected. Finally, a secondary wetting event allows large numbers of infected mosquitoes to again disperse, this time into developed habitats where humans and, in the case of WNV, horses are encountered and infected.

It is our contention that the TBH model can be used to track WTD in real-time, searching for the 1977/1990 SLE epidemic signature in Peninsular Florida. Furthermore, because of the similarities in the natural history, ecology, and epidemiology of SLE and WN viruses, the 1977/1990 SLE signature will also predict WN epidemics.

References

Day, J.F. and A.L. Lewis. 1992. An integrated approach to St. Louis encephalitis surveillance in Indian River County, Florida. Florida Journal of Public Health 4:12-16.

Day, J.F. 2001. Predicting St. Louis encephalitis virus epidemics: Lessons from recent, and not so recent, outbreaks. Annual Review of Entomology 46:111-38.

Shaman, J., J.F. Day and M. Stieglitz. 2002. Drought-induced amplification of St. Louis encephalitis virus in Florida. Emerging Infectious Diseases 8:575-580.

Shaman, J., J.F. Day and M. Stieglitz. 2003. St. Louis encephalitis virus in wild birds during the 1990 south Florida epidemic: The importance of drought, wetting conditions, and the emergence of Culex nigripalpus (Diptera: Culicidae) to arboviral amplification and transmission. Journal of Medical Entomology 40:547-554.

Shaman, J., J.F. Day, M. Stieglitz, S. Zebiak and M. Cane. 2004. Seasonal forecast of St. Louis encephalitis virus transmission, Florida. Emerging Infectious Diseases 10:802-809.

Shaman, J., J.F. Day and M. Stieglitz. 2004. The spatial-temporal distribution of drought, wetting, and human cases of St. Louis encephalitis in south-central Florida. American Journal of Tropical Medicine and Hygiene 71:251-261.

Shaman, J., M. Stieglitz, C. Stark, S. Le Blancq and M. Cane. 2002. Predicting flood and swampwater mosquito abundances using a dynamic hydrology model. Emerging Infectious Diseases 8:6-13.

Shaman, J., J.F. Day and M. Stieglitz. 2005. Drought-Induced amplification and epidemic transmission of West Nile Virus in south Florida. Journal of Medical Entomology 42:134-141.

Shaman, J. and J.F. Day. 2005. Achieving real-time operational hydrologic monitoring and forecasting of mosquito-borne disease transmission. Emerging Infectious Diseases 11:1343-1350.

Day, J.F. and J. Shaman. 2007. Using hydrologic conditions to track the risk of focal and epidemic arboviral transmission in Peninsular Florida. J. Med. Entomol. (In Press).