estimating infectious disease risk in the absence of ... · the model is run worldwide, generally...

ESRI International Health GIS Conference Thomas et al Washington, DC October 17-20, 2004

Estimating Infectious Disease Risk in the Absence of Incidence Data D. Michael Thomas, DO, MPH&TM1,2, Arthur D. Desch1, Holly D. Gaff, PhD1,3, Suzanne K. Scheele, MS1, Richard K. Jordan, MS, PhD1, Jonathan R. Davis, PhD, MS1,4 1. Health & Environmental Security Branch, Dynamics Technology, Inc., 1555 Wilson Blvd Ste 703,

Arlington, Virginia 22209 2. Department of Health Ecology, University of Nevada, Reno, Nevada 3. Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine,

Baltimore, Maryland 4. Center for Vaccine Development, University of Maryland School of Medicine, Baltimore, Maryland Abstract Estimating disease risk in developing countries may rely heavily on subjective analysis of data that can be incomplete and based on anecdotal reporting. GIS and remotely sensed data provide a more efficient and objective tool needed to support this analytical effort. For each disease of interest, a metric is defined and a relative risk scale value is assigned to physical parameters for a number of factors known to be associated with the disease, their potential for significant influence, and availability of data. A composite score combines relative risk scaled values to give the site specific risk. Detailed monthly maps are produced for a specific country or region and show the relative suitability of small land areas to support disease transmission. A color-coded disease "risk category" is assigned to each pixel. This method provides a useful first-order assessment of infectious disease risk when incidence data is lacking or of poor quality.

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Introduction Use of earth-observing satellites and geographic information systems (GIS) are increasingly being used for ecological niche modeling for vector-borne diseases1-7. Mathematical models can be applied to this worldwide data and are particularly important where little or no historical epidemiological data is available, but a first order estimate of infectious disease risk is needed. Other efforts to estimate risk for various infectious diseases, especially vector-borne diseases, have focused on relating point-occurrence disease data to electronic maps of relevant ecological dimensions8,9. A set of rules is produced that defines the niche, and these rules are then applied to a larger geographical area to predict niches and risk of disease. We propose a multiplicative mathematical model that uses a priori biological factors rather than incidence data to estimate risk for selected infectious diseases. Such a model also identifies areas that are at risk for disease emergence, even if the disease is not currently present. Methods This infectious disease risk model is a GIS model that takes specific GIS data and remotely-sensed data as input layers. Each layer is reclassified, i.e., rescaled according to a prescribed function, to translate the raw data values into relative risk of disease. These reclassified layers are then combined to create a final map output layer. This output layer is classified into no risk, rare cases, low risk, medium risk and high risk categories for all locations where the input data exists. The model is run worldwide, generally on a monthly time step. The environmentally based modeling approach chosen for the study is best suited for modeling infectious diseases that require a vector or host such as an insect, tick, rodent, or snail for disease transmission to humans. The input factors used in each disease model are selected based upon potential for significant influence on the health and reproduction of key vectors and hosts as well as availability of worldwide data. Public health interventions, better living conditions, or current absence of an appropriate vector or host species can prevent disease transmission in an area that is otherwise at risk for transmission. This risk model does not adjust for these factors and is an attempt to map the underlying suitability for disease absent these mitigating factors. Weights, or scale values, are assigned to each measurement of the selected input factors in the following way: If the measurement reveals that the factor is in a range that precludes the possibility of disease activity in a region (for example, temperature below a known threshold value for disease transmission), then the factor is assigned a weight of zero. Otherwise factors are assigned an integer weight between one and nine, with nine indicating that the measured factor is an a range that is highly favorable for disease activity and one corresponding to a measured value that indicates only a small possibility

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of disease activity. This choice of weights guarantees that the suitability for disease activity, which is defined as the product of the weights, is zero if the value of one of the measured factors is in a range where disease transmission cannot occur. As for the weights indicating that disease activity could occur, any set of increasing numbers could be chosen as long as the smallest is greater than equal to one. This restriction guarantees that when the weights are multiplied together, the result is a number that is greater than or equal to each of the individual weights (with equality if and only if all the weights are one). In addition, the product of all the weights will be at least as large as the product of any subset of the weights. In other words, the suitability for disease transmission when multiple factors are present is greater than the suitability associated with the individual factors of a subset of factors. In the event that data are not available for one or more of the selected factors in a region, the model will not run for that region. To derive the function used to translate each input layer into appropriate scale values, we researched the relevant scientific studies relating disease risk to that layer’s data. From these studies, we derived as many reliable points as possible, where the point would be an explicit layer data value or range and its corresponding scale value. For example, data points for layers such as temperature include minimum and maximum thresholds as well as ideal ranges. Then for layers in which there was no documented relationship between the data points, the default relationship of a simple linear function is used. For example, the function for the temperature layer used in the malaria risk model is shown below (Figure 1).

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0 5 10 15 20 25 30 35 400

1

2

3

4

5

6

7

8

9

Mean Monthly Temperature (C)

Sca

le V

alue

Piecewise-linear function from known data pointsMedain value for each bin

MostFavorable

LeastFavorable

NoRisk

Median Value for (20-24C)

Figure 1. Temperature scale values used for malaria risk model. More complex temperature models are used when justified by previous research, such as when modeling certain tick populations that display seasonal cohorting. Figure 2 shows a part of the temperature model for Central European Tick-borne Encephalitis that is used to determine when ticks become active in the spring. This factor is scaled using a simple growth function.

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Figure 2. Temperature scale values used for Spring Activation of Ixodes ricinus. Land cover is often useful as important vectors or hosts may inhabit specific biomes or land cover types. The target species frequently exhibit graduated land cover preferences as opposed to exclusionary land cover requirements. Therefore, land cover subtypes are defined using existing land cover maps and assigned scale values of relative importance based on previous research. For example, forest cover can be divided into subtypes and the preferred forest types for Ixodes ricinus given more favorable scale values as illustrated in the Central European Tick-borne Encephalitis model (Table 1). Table 1. Land cover scale values for the Central European Tick-borne Encephalitis risk model. Land Cover Scale Value Mixed forest dominated by deciduous broadleaf trees, open shrubland, wooded grassland, woodlands

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Closed Canopy deciduous broadleaf forest

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Open canopy deciduous broadleaf forest 5 Mixed forest dominated by coniferous trees

3

Coniferous forest, grasslands, croplands, urban, bare ground, closed scrublands

1

No Data Restricted Long term precipitation patterns are helpful in refining land cover classifications. Forests and grasslands require distinct yearly precipitation patterns to thrive. Where a land cover

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map may only indicate grasslands the precipitation maps can help distinguish between dry and wet grasslands which harbor different species. These precipitation patterns are also indicators of regions with established water regimes that are harmful or beneficial to the vector or host being modeled, such as seasonal pools for mosquito breeding. The climatology scale values used in the Rift Valley Fever risk model are shown in Figure 3.

Figure 3. Climatologic precipitation scale for Rift Valley Fever model. A close relationship exists between increased vegetation “greenness”, as measured by the monthly Normalized Difference Vegetation Index (NDVI), and population increases in both insect disease vectors and small vertebrate host species6. Part of this relationship is due to the influence of rainfall patterns on vegetation. NDVI exhibits a nearly linear positive correlation with rainfall, particularly in arid and semi-arid regions10,11. Prolonged greenness (positive NDVI), therefore, can indicate increased rainfall and moisture which promote mosquito population growth and increased food supplies for small vertebrates12,13. Disease outbreaks are frequently associated with unstable environmental conditions that can result in a dramatic increase in vector populations1,14,15. NDVI Greenness values higher than normal can indicate that habitat on the ground is beneficial to vector populations increasing beyond normal levels12,13. If this greater than normal greenness is maintained for a second and third month, conditions are likely to remain highly favorable to mosquito breeding1. These areas are also favorable for population peaks in small vertebrate hosts.

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The multiple environmental factors for which NDVI is a surrogate allow the index to be used in different ways depending on the vector or host being modeled. A two to three month window of recent anomalies is often used for modeling mosquitoes while previous years spring and fall anomalies are important for some rodents and ticks. For the Risk Valley Fever risk model, using the methodology described by Anyamba et al6, we identified areas with three consecutive months of positive NDVI anomalies within the current study year. First we calculate the monthly NDVI anomaly ( ) from the 20 year climate value (

∆NDVINDVI ) for each month:

∆NDVI = NDVINDVI − The average NDVI anomaly over a given three month window is then computed:

)3( monNDVI ∆ = 3

2

∑−

∆

t

ttNDVI

Only anomalies averaging at least 0.1 NDVI unit above normal over the 3 months are considered. Locations experiencing this anomaly are given a scale value equal to nine (Table 2. Scale values for Greenness Anomaly used in Rift Valley Fever risk model.). Regions that do not meet this requirement are given a scale value equal to one. Table 2. Scale values for Greenness Anomaly used in Rift Valley Fever risk model.

Greenness Anomaly Scale Value 3 month positive greenness anomaly ≥ .1 NDVI units 9 Long-term anomaly < requirements 1 No Data Restricted

Monthly precipitation is also a key factor for several disease cycles. Each risk map is calculated by a series of map algebra steps. The model begins with several data layers of the same land area, one for each of the selected key biological factors. All data is in raster or ‘pixel’ format. For example, a sample of the raster temperature map showing sixteen pixels is viewed as a matrix (data is shown for only four pixels):

............

...9.230.25...

...0.245.19...

............

Each value in this matrix is reclassified according to its assigned scale. This results in a matrix of values for the reclassified raster map:

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............

...88...

...84...

............

This process is repeated for each of the map layers that are included in the risk model. As a result, new data layers for our land area are created, each with scaled values for the biological factors. These new values from the map layers are multiplied pixel by pixel. The mathematical equation the Rift Valley Fever Risk Model is given by:

............

......

......

............

,,

,,

yxyx

yxyx

SSSS =

............

...********...

...********...

............

,,,,,,,,,,

,,,,,,,,,,

yxyxyxyxyxyxyxyxyxyx

yxyxyxyxyxyxyxyxyxyx

TPCGLTPCGLTPCGLTPCGL

where S x,y is the risk of Rift Valley Fever at location (x,y), L x,y is the Land Cover at (x,y), G x,y is the Greenness Anomaly at (x,y), Cx,y is the Climatologic Precipitation at (x,y), P x,y is the Precipitation Anomaly at (x,y), T x,y is the Temperature at (x,y). The resulting risk map given by the S-matrix from the equation will have a “Risk Index Score” values from zero to 59049 (9^5). These scores are converted into a color-coded map showing the appropriate risk category for each pixel (e.g., “High Risk for Rift Valley Fever” will be shown in red). This entire process is completed using map algebra calculations in the ESRI ArcGIS 9 software. A simplified diagram of the mathematical processes described above is shown in Figure 4 below.

Figure 4. Diagram of the mathematical process performed by the GIS software.

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These models are easily implemented in ESRI ModelBuilder (Figure 5).

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Figure 5. ESRI ModelBuilder diagram of the Rift Valley Fever risk model.


Each pixel (about 10 km) on the map is assigned its risk index score. For visual display, the score is assigned a color which is displayed on the map. Six categories are used for describing Rift Valley Fever Risk (Table 3). The ranges of risk index score for each risk category are derived from a visual comparison of the model outputs to existing maps of disease incidence and endemicity as well as from previous modeling experience. As can be seen in the table below, the range for no risk is 0-10, the range for rare cases is 10-32(25), the range for low risk is 32(25) – 525(3.55), the range for medium risk is 525(3.55) – 4000(5.255), and the range for high risk is above 4000(5.255). These values were determined by adjusting reclassification of the model output until the results showed the highest visual correlation to available published Rift Valley Fever data. Very little published data have the temporal and spatial specificity required to calculate the ranges based on exact correlations between model output and field data, but rather the ranges were adjusted to visually match with published guidelines and known risk areas. The values shown in parentheses is a first cut at a theory that the thresholds for each model will be determined by these values (2, 3.5 and 5.25) raised to the power of the number of key factors used within this model. The threshold for the Rare Cases category has been found to be disease dependent. This scaling routine holds true when more factors were added to this model and for other models with similar structure but more factors. It is unclear at this time whether this scaling routine will be valid for all disease models. Table 3. Interpretation of the Rift Valley Fever risk map. RVF Risk Risk Index Color on Map High 4000.01 or greater Red Intermediate 525.01 – 4000 Yellow Low 32.01 – 525 Light Blue Rare Cases 10.01 – 32 Green No Risk Restricted or “-1” Black Missing Data N/A Hatch Marks

Results Using different variations of the methodology described above, a number of diseases were modeled. Selected risk maps are shown below.

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Figure 6. East African Malaria Risk Map, June 1998. The risk of malaria transmission is June 1998 is shown in Figure 6. The hatch marks are solely to draw attention to the area of interest in east Africa. Earlier in the year part of the area was suffering from the effects of the 1997-1998 El Niño-associated heavy rainfall.

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Figure 7. Central European Tick-borne Encephalitis risk map, Europe, June 1998. Figure 7 shows a risk map for Central European Tick-borne Encephalitis. The tick vector is only found in Europe, where the High Risk and Medium Risk areas correlate well with known endemic areas. The vector has not found west of the Caspian Sea, although the risk map suggests that suitable habitat exists there (e.g., in Kazakhstan).

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Figure 8. Russian Spring-Summer Encephalitis, Europe, June 1998. The tick vector for Russian Spring-Summer Encephalitis (RSSE) is not found in Europe, except for in European Russian. The risk map above (Figure 8) shows large parts of Sweden and Finland as having ideal environmental conditions for disease emergence; however, the necessary tick vector is currently not found in these areas. While ticks may not expand into new areas as readily as certain mosquito species, the risk map suggests areas where this tick species is most likely to find a suitable habitat.

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Figure 9. Japanese Encephalitis risk map, Asia, July 1998. Japanese Encephalitis is endemic across most of Asia. Most humans are seropositive for exposure by the age of 15 years16. Environmental conditions are shown to be suitable for disease emergence in other regions besides Asia, also (Figure 9).

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Figure 10. Leishmaniasis Risk, Rainforest Dwelling Sand Flies, October 1998. Leishmaniasis is carried by sand flies, which either live in dry lands or rainforest. A different risk model is created for each habitat. Figure 10 shows leishmaniasis risk in Latin American rainforests.

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Figure 11. Schistosomiasis risk map, Africa (excluding east Africa), 1998. Figure 11 shows schistosomiasis risk in parts of Africa. A mask (the grayed-out regions) was placed over regions that were not the focus of attention in this map. Because this risk model was developed with travel medicine in mind, and preventive measures are similar for the various species of schistosomiasis (i.e., waterproof, liposomal DEET17), the risk map does not attempt to create different maps for each species of schistosomiasis that is found in Africa. Risk maps were produced for each year rather than for each month.

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Figure 12. Rift Valley Fever risk map, selected countries, May 1998. Rift Valley Fever is found in Africa, and in recent years an outbreak was confirmed in the southwestern Arabian Peninsula. A high risk area in Kenya and southern Somalia is shown in this map (Figure 12). This outbreak is related to the earlier 1997-1998 El Niño-associated heavy rainfall that is known to have engendered a Rift Valley Fever epidemic during this time period.

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Figure 13. Plague risk map, selected countries, July 1998. The relative suitability for enzootic plague is shown in Figure 13. Enzootic plague has been extensively studied in Kazakhstan, and the risk map identifies the known high risk areas in eastern and western Kazakhstan.

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Figure 14. Crimean-Congo Hemorrhagic Fever risk map, Africa (excluding eastern Africa), July 1998. Crimean-Congo Hemorrhagic Fever outbreaks have been better documented in Europe (or may occur more often in colder climates due to tick cohorting), but the disease has also been documented in Africa. The risk map is shown in Figure 14. While serological data from Africa is extremely limited, the High and Medium Risk areas shown in this map include countries where the virus has been documented by laboratory testing.

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Figure 15. Dengue Fever risk map, Asia, July 1998 No vaccine is available for Dengue Fever and the virus is spreading to geographic areas of the world. A risk map for Asia is shown in Figure 15. As with all diseases, the endemicity of disease cannot be determined from a single risk map due to seasonal or interannual variation. The mosquito vectors breed in natural and artificial water containers. The four dengue virus serotypes serve to induce cyclical epidemics of cocirculating strains18. This risk model might be able to be combined with other types of models to improve outbreak prediction. For example, in Thailand Cummings et al found a spatial-temporal traveling wave (traveling at a speed of 148 km per month) in Dengue Hemorrhagic Fever incidence emanating from Bangkok19.

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Figure 16. Yellow Fever risk map, Latin America, July 1998. A risk map for Yellow Fever is shown in Figure 16. Again, due to marked seasonal variation, a series of risk maps should be viewed before judging endemicity. Discussion Due to the lack of worldwide temporally and spatial matched incidence data, classification accuracy could not be statistically validated. However, the strength of this approach to modeling is that it can provide a first order assessment to disease risk when

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incidence data is lacking. It is obvious that some places marked as high risk are not currently at high risk. The output maps give insight into the suitability for different diseases, and do not account for public health interventions or socioeconomic conditions. The method used is based on the best science available using data readily available in GIS format. Many factors had to be neglected because of the lack of quality, worldwide data. The results of this method therefore do not always represent the true risk. In most cases, this will be shown as over-reporting the risk. This approach also provides information on which geographic locations are at risk for possible importation and emergence of disease for the first time, as well as locations that are at risk for disease re-emerge should public health interventions or socioeconomic conditions deteriorate. The Weighted Linear Combination Method is a popular technique for deriving composite maps20. If applied to disease risk modeling, this method can also start with a priori biological factors, and it can be adapted for nonlinear relationships. However, the additive assumption implies that inputs used in the model are mutually independent of each other. The multiplicative model betters reflects the synergistic effect that input factors can have on each other, and it also appears to better model large geographic areas that can have extremes in such attributes as temperature. It would be appealing for a risk model that uses very basic biological factors to show reasonable results across the globe without resorting to creating small ecological zones and using a different risk model for each zone. Disease risk models using methods such as isodata clustering have been applied to small geographical areas and may not be ideally suited for application on a worldwide scale. The a priori biological factors used to create a risk model do not change based upon location in the world, making this method appealing for modeling large geographical areas. This method might also be used as a first step in a risk model that uses this approach to identify high risk areas for further analysis with more detailed data and complex models. The approach would be useful in providing input values (e.g., estimated rodent populations) for agent-based SEIR (Susceptible-Exposed-Infected-Recovered) mathematical models of disease spread. Also, by replacing historical temperature and rainfall data with worldwide weather forecast data, disease risk can be predicted months in advance. Risk is a complex function of exposure and susceptibility. It depends upon the underlying ecological dynamics of an infectious organism in a region of interest. Risk therefore depends upon a spectrum of factors that, in general, are difficult or impossible to measure or infer without field data. The analysis described herein is a risk assessment based on measuring environmental, climatic, and social proxies for disease suitability. While this approach does not completely define “risk” in the largest sense, it is a necessary foundation upon which to build a more complete risk assessment.

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Acknowledgements We also thank Mark Bremer and Dr. Holly Butler of ESRI Professional Services Group, Vienna, Virginia, for their role in implementing this work in ESRI ArcGIS. We also thank Stephen Vanech and Dr. Joy Miller of the Department of Defense for their valuable input and encouragement. This study was supported and monitored by the Defense Intelligence Agency of the Department of Defense under Contract No. DAMD17-02-F-0653.

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References 1. Hay, S. I., Omumbo, J. A., Craig, M. H. & Snow, R. E. Earth observations,

geographic information systems and Plasmodium falciparum malaria in sub-Saharan Africa. Adv. Parasitol. 47, 173-215 (2000).

2. Beck, L. R., Lobitz, B. M. & Wood, B. L. Remote sensing and human health: new sensors and new opportunities. Emerg. Infect. Dis. 6, 217-227 (2000).

3. Washino, R. K. & Wood, B. Application of remote sensing to arthropod vector surveillance and control. Am. J. Trop. Med. Hyg. 50, 134-144 (1994).

4. Brooker, S. et al. Predicting the distribution of urinary schistosomiasis in Tanzania using satellite sensor data. Trop. Med. Int. Health 6, 998-1007 (2001).

5. Kitron, U. & Mannelli, A. Ecological Dymamics of Tick-borne Zoonoses. Sonenshine, D. E. & Mather, T. N. (eds.), pp. 198-239 (Oxford University Press, New York,1994).

6. Anyamba, A., Linthicum, K. J., Mahoney, R., Tucker, C. J. & Kelley, P. W. Mapping potential risk of Rift Valley Fever outbreaks in Africa savannas using vegetation index time series data. Photogrammetric Engineering and Remote Sensing 68, 137-145 (2002).

7. Daniel, M., Kolar, J., Zeman, P., Pavelka, K. & Sadlo, J. Predictive map of Ixodes ricinus high-incidence habitats and a tick-borne encephalitis risk assessment using satellite data. Experimental and Applied Acarology 22, 417-433 (1998).

8. Levine, R. S., Peterson, A. T. & Benedict, M. Q. Geographic and ecologic distributions of the Anopheles gambiae Complex predicted using a genetic algorithm. Am. J. Trop. Med. Hyg. 70, 105-109 (2004).

9. Robinson, T. P. Statial Statistics and Geographic Information Systems in Epidemiology and Public Health. Adv. Parasitol. 47, 81-128 (2000).

10. Nicholson, S. E., Davenport, M. L. & Malo, A. R. A comparison of vegetation response to rainfall in the Sahel and East Africa using Normalized Difference Vegetation Index from NOAA-AVHHR. Climate Change 17, 209-241 (1990).

11. Tucker, C. J., Dregne, H. E. & Newcomb, W. W. Expansion and contraction of the Sahara Desert from 1980-1990. Science 253, 299-301 (1991).

12. Linthicum, K. J., Davies, F. G., Bailey, C. L. & Kairo, A. A mosquito species succession dambo in an East African forest. Mosquito News 43, 464-470 (1983).

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13. Linthicum, K. J., Davies, F. G., Bailey, C. L. & Kairo, A. Mosquito species encountered in a flooded grassland damno in Kenya. Mosquito News 44, 228-232 (1984).

14. Hielkema, J. U., Roffey, J. & Tucker, C. J. Assesement of ecological conditions associated with the 1980/1981-desert locust plague upsurge in West Africa using environmental satellite data. International Journal of Remote Sensing 7, 1609-1622 (2002).

15. Linthicum, K. J. et al. Application of polar-orbiting, meteorological satellite data to detect flooding of Rift Valley Fever virus vector mosquito habitats in Kenya. Med. Vet. Entomol. 4, 433-438 (1990).

16. World Health Organization Japanese Encephalitis: WHO position paper. http://www. who. int/vaccines-diseases/diseases/je. shtml (2003).

17. Salafsky, B. Liposomal DEET. University of Illinois School of Medicine at Rockford, 1601 Parkview Avenue, Rockford, IL 61107 (2004).

18. Ferguson, N., Anderson, R. & Gupta, S. The effect of antibody-dependent enhancement on the transmission dynamics and persistence of multiple-strain pathogens. Poc. Natl. Acad. Sci. USA 96, 794 (1999).

19. Cummings, D. A. T. et al. Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand. Nature 427, 344-347 (2004).

20. Malczewski, J. On the Use of Weighted Linear Combination Method in GIS: Common and Best Practice Approaches. Transactions in GIS 4, 5-22 (2000).

Corresponding Author: Dr. Mike Thomas Dynamics Technology, Inc. 1555 Wilson Blvd Ste 703 Arlington VA 22209 USA Phone: (703) 516-3213 [email protected]

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