The Relationship Between Mosquito Abundance and Rice Field Density In

Richards et al. International Journal of Health Geographics 2010, 9:32 INTERNATIONAL JOURNAL http://www.ij-healthgeographics.com/content/9/1/32 OF HEALTH GEOGRAPHICS

RESEARCH Open Access TheResearch relationship between mosquito abundance and rice field density in the Republic of Korea

Erin E Richards*1, Penny Masuoka1, David Brett-Major1,2, Matthew Smith3, Terry A Klein4, Heung Chul Kim5, Assaf Anyamba3 and John Grieco1

Abstract Background: Japanese encephalitis virus (JEV), the causative agent of Japanese encephalitis (JE), is endemic to the Republic of Korea (ROK) where unvaccinated United States (U.S.) military Service members, civilians and family members are stationed. The primary vector of the JEV in the ROK is Culex tritaeniorhynchus. The ecological relationship between Culex spp. and rice fields has been studied extensively; rice fields have been shown to increase the prevalence of Cx. tritaeniorhynchus. This research was conducted to determine if the quantification of rice field land cover surrounding U.S. military installations in the ROK should be used as a parameter in a larger risk model that predicts the abundance of Cx. tritaeniorhynchus populations. Mosquito data from the U.S. Forces Korea (USFK) mosquito surveillance program were used in this project. The average number of female Cx. tritaeniorhynchus collected per trap night for the months of August and September, 2002-2008, was calculated. Rice fields were manually digitized inside 1.5 km buffer zones surrounding U.S. military installations on high-resolution satellite images, and the proportion of rice fields was calculated for each buffer zone. Results: Mosquito data collected from seventeen sample sites were analyzed for an association with the proportion of rice field land cover. Results demonstrated that the linear relationship between the proportion of rice fields and mosquito abundance was statistically significant (R2 = 0.62, r = .79, F = 22.72, p < 0.001). Conclusions: The analysis presented shows a statistically significant linear relationship between the two parameters, proportion of rice field land cover and log10 of the average number of Cx. tritaeniorhynchus collected per trap night. The findings confirm that agricultural land cover should be included in future studies to develop JE risk prediction models for non-indigenous personnel living at military installations in the ROK.

Background its primary vector Culex tritaeniorhynchus is plentiful in Japanese encephalitis virus has been identified by the U.S. many Asian countries. It has been shown that the number National Center for Medical Intelligence as operationally of JEV vector mosquitoes collected in an area is highly important to the U.S. military. Similarly, the pathogen is correlated with the number of JE patients in that area [3]. important to unvaccinated personnel from other militar- While a reliable JE vaccination exists, this disease still ies around the globe, expatriates, and travelers [1]. JE is a affects many Southeast Asian countries [4,5]. Recent out- potentially debilitating and deadly flavivirus that is breaks in the last five years (2005 through 2010) have endemic in rural areas in east, south, and southwest Asia. occurred in the indigenous population and unvaccinated Successful vaccination programs have diminished the expatriates and travellers in India, Nepal, Taiwan, Malay- number of illnesses in many countries, causing many to sia, China, and Vietnam [6-12]. Recent maps showing the consider the disease to be rare and exotic [2]. However, geographical distribution of JE have been published in the JEV is maintained in populations of wading birds and several different papers [2,12,13]. The JE distribution map in Figure 1 is based on the U.S. Centers for Disease Con- * Correspondence: [email protected] trol and Prevention (CDC) 2009 version of the map cur- 1 Department of Preventive Medicine and Biometrics, Uniformed Services rently distributed to international travellers [13]. University, Bethesda, Maryland, USA Full list of author information is available at the end of the article

© 2010 Richards et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Richards et al. International Journal of Health Geographics 2010, 9:32 Page 2 of 10 http://www.ij-healthgeographics.com/content/9/1/32

Figure 1 Geographical distribution of Japanese encephalitis. Geographical distribution of Japanese Encephalitis as published in CDC Health In- formation for International Travel 2010 [13]. Guam and Saipan (not shown on this map) have also had cases of Japanese Encephalitis.

While JE clinical manifestations occur only in a small Culex tritaeniorhynchus is exophagic, prefers zoonotic number that become infected, the case fatality rate can be and domestic hosts, and tends to feed interspecifically as high as 40% for those demonstrating symptoms. Per- [18]. Primary vertebrate reservoirs for JEV are large water manent neurologic sequelae are observed in 40 to 50% of birds of the family Ardeidae, while wild and domestic pigs those who survive severe disease, resulting in minor to are amplifying hosts [17,19]. Cx. tritaeniorhynchus is severe complications and early death [14,15]. Further- highly susceptible to viral infection; studies suggest that more, JE is often misdiagnosed and lacks effective treat- over 30% become infected after feeding on a viremic pig ment beyond supportive care [16,17]. [20]. Mosquito populations are dependent on environ- U.S. military personnel have been stationed in the ROK mental and seasonal factors for their propagation. In the since World War II. Currently, programmed extended ROK, seasonal occurrence of Cx. tritaeniorhynchus are tour lengths have greatly increased the number of accom- reported between mid-June and early October, with adult panying family, placing family members and young peak populations from late July through early September unvaccinated children at risk of infection. While an estab- [21-26]. JEV infections within vector populations peak in lished Korean pediatric JEV vaccination program has late July and early August, with human disease rates greatly reduced the number of cases occurring in the peaking in late August and September [15,27]. Culex tri- Korean population, JEV continues to be of military public taeniorhynchus breed in open sunlit temporary and per- health concern in the ROK because U.S. personnel are manent water habitats with vegetation, and have an not vaccinated for JE. Because of this disparity, human average flight range of 1.5 km [4,28]. The relative abun- disease rates in the indigenous population are unreliable dance of Cx. tritaeniorhynchus is closely related to local for estimating transmission and disease risks to U.S. mili- rice agricultural practices, where wetland rice fields and tary, civilian, and family members stationed there. similar irrigation systems provide an optimal habitat for Richards et al. International Journal of Health Geographics 2010, 9:32 Page 3 of 10 http://www.ij-healthgeographics.com/content/9/1/32

larval development [18]. The ecological relationship rate risk model based on remotely sensed land and cli- between mosquitoes and rice fields has been studied mate data will allow for more efficient detection of extensively [29-31]; results show that these man-made increased JE risk to U.S. military populations in the ROK. breeding sites result in a greater prevalence of Cx. tritae- Additionally, the risk model would diminish reliance on niorhynchus than natural breeding sites [32,33]. man-power intensive surveillance systems, and may pro- Remote sensing and Geographical Information Systems vide rationale for the implementation of mandatory JEV (GIS) are increasingly used in disease epidemiology to vaccination to USFK military, civilian, and family mem- map vector and disease distributions so that vector con- bers assigned to the ROK. trol efforts and disease intervention strategies are admin- istered efficiently and effectively [29]. Past research has Methods demonstrated that remote sensing and GIS capabilities Study Area can be used successfully to identify breeding habitats of The study area comprised seventeen U.S. military instal- Cx. tritaeniorhynchus in Japan, the ROK, India, and Aus- lations in the ROK and their immediate surrounding tralia [18]. areas. Military installations included in this study are a Past studies on relative seasonal abundance and geo- convenience sample based on availability of high resolu- graphical distribution of Cx. tritaeniorhynchus in the tion images and mosquito data provided by the USFK ROK have contributed to the success and development of mosquito surveillance program. Installations included in a JE disease surveillance systems for local populations this study are Camp Humphreys, Camp Carroll, Gwangju [34]. These surveillance systems have been based on Air Base, Warrior Base, Camp Red Cloud, Camp Stanley, monitoring vector populations and seroprevalence in Camp Jackson, Camp Essoyons, Camp Long, Camp swine from selected slaughterhouses; while such surveil- Eagle, Camp Casey, Camp Castle, Camp Nimble, Camp lance systems have been effective, they have a negative Hovey, Osan Air Base, and Gunsan Air Base (Figure 2). impact on the limited human resources and man-power Reorganization of the U.S. military in the ROK has led available to public health. For this reason, the Uniformed to the realignment of existing military installations and Services University, the Armed Forces Health Surveil- closure of others. Camp Humphreys, which has been lance Center, Global Emerging Infections Surveillance under construction since late 2007, is one of the largest and Response System (AFHSC-GEIS), the National Aero- installations and has been augmented in order to accom- nautics and Space Administration (NASA) Goddard modate an additional 20,000 relocated military, civilian, Space Flight Center (GSFC), and the 65th Medical Bri- and family members. Wetland rice fields adjacent to gade-Korea, began working together to develop a JEV risk Camp Humphreys were purchased for installation expan- model in the ROK that incorporates remote sensing, land sion, with fill dirt used to elevate the low-lying rice pad- cover, elevation, and historical climate data. dies for development, thus changing the local landscape. The goal of this project was to determine if quantifica- A portion of the secured rice paddies are programmed to tion of wetland rice field land cover via remote sensing lay fallow, thereby contributing to a possible increase in and GIS techniques could feasibly be used as a parameter Cx. tritaeniorhynchus populations during the wet Mon- in a larger risk model to predict the abundance of Cx. tri- soons (June-September). Two images of Camp Hum- taeniorhynchus at U.S. military installations in the ROK. phreys were used in this project; one image is a 2006 While past studies have shown a correlation between Cx. QuickBird image and the other is a 2008 IKONOS image tritaeniorhynchus collected at cow and pig sheds and sur- of the same location. The rice field density statistics used rounding rice field acreage [35,36], this study examines in analysis for Camp Humphreys is based on the 2006 the number of Cx. tritaeniorhynchus collected on pri- image only. The 2008 image shows the amount of rice marily urban military bases and compares the number of fields lost as a result of installation construction. The two mosquitoes collected to the amount of rice field sur- images were used in an exploratory fashion to help rounding those bases. Quantifying the relationship describe how changes in local land cover effect the abun- between the amount of rice fields surrounding U.S. mili- dance of Cx. tritaeniorhynchus. tary installations in the ROK and the abundance of Cx. tritaeniorhynchus captured at these sites will enhance the Mosquito Data U.S. military's capability to predict the risk of JEV trans- Retrospective mosquito data were provided by the USFK, mission to military, civilians, and family members which conducts routine mosquito surveillance on U.S. assigned to the ROK. This proposed risk model would be military installations to monitor all mosquito species. used in conjunction with a predictive model for seasonal Mosquitoes were collected weekly using un-baited New JEV transmission using real-time data and would serve as Jersey (NJ) light traps in accordance with USFK preven- a disease risk monitoring system for U.S. military and tive medicine protocol from May through October, 2002 civilians living in the ROK. The development of an accu- to 2008. One to eight traps were established at each mili- Richards et al. International Journal of Health Geographics 2010, 9:32 Page 4 of 10 http://www.ij-healthgeographics.com/content/9/1/32

Figure 2 Study area. The following military installations within South Korea are shown: Camp Humphreys, Camp Carroll, Gwangju Air Base (Kwangju), Warrior Base, Camp Red Cloud, Camp Stanley, Camp Jackson, Camp Essoyons, Camp Long, Camp Eagle, Camp Casey, Camp Castle, Camp Nimble, Camp Hovey, Osan Air Base, and Gunsan Air Base (Kunsan). Land cover obtained from MODIS Land Cover Group http://www-modis.bu.edu/landcover. Richards et al. International Journal of Health Geographics 2010, 9:32 Page 5 of 10 http://www.ij-healthgeographics.com/content/9/1/32

tary installation. Collections were sent to the Entomology comparison studies have demonstrated that land classifi- Section, 5th Medical Detachment, for identification where cation outcomes are analogous for both image types [37]. data on adult female mosquitoes (i.e. species, trap night, Within each of the ten QuickBird images and one IKO- month, and year) were prepared. A summary of the total NOS image, military installations were located using lati- number of mosquitoes (males plus females) collected at tude and longitude coordinates. Processing of image data each location from 2002 to 2008 is listed in Table 1. involved two steps: digitizing and area computation. Although NJ light traps are relatively inefficient for col- Rice fields were identified using photointerpretation lecting mosquitoes when compared to newer technolo- and manual digitization via ArcGIS® 9.3 software (Envi- gies, they are easy to operate and data can be compared ronmental Systems Research Institute, Redlands, CA, with collections made over more than 20 years of surveil- USA, http://www.esri.com). The border of each military lance in the ROK. installation was digitized using publically available instal- The final mosquito count at each military installation lation maps as aids. Buffers were used to designate the used in the main analysis of this study is an average from study inclusion area. The buffer area surrounding each August to September from 2002 to 2008. August and Sep- military installation was based on the average flight range tember were selected for analysis to capture the peak Cx. of Cx. tritaeniorhynchus [4], and was set at 1.5 km from tritaeniorhynchus breeding cycle [21]. To reduce the the digitized military installation border. Rice fields effects of sampling variation between years and installa- within the buffer area of each military installation were tions, data were standardized as the number of female then manually digitized as polygons. Manual digitization mosquitoes collected per trap night per month [mosqui- was chosen because the majority of images available for toes per trap night = total female mosquitoes collected/ this project were acquired during the winter months (number of traps * number of nights)]. The final mos- when rice fields were bare and spectrally similar to other quito capture rate for each military installation was calcu- land cover, making automated classification difficult. Rice lated by adding the number of mosquitoes per trap night field land cover was digitized as large polygons consisting for months August and September from 2002 to 2008, of many individual rice fields. Other non-rice features then dividing by the number of nights mosquito sampling such as irrigation ditches and small roads may have been was conducted during that time period. included in the rice field land cover. To minimize variation, all digitization was completed by one researcher in a High Resolution Satellite Data consistent manner. One IKONOS and ten QuickBird high resolution images, To obtain area measurements in kilometers, the digi- encompassing visible and near infrared data, taken tized military installations buffer area and rice field shape between 2004 and 2008 were used for land cover classifi- files were reprojected from a Geographic Coordinate Sys- cation. QuickBird is a commercial earth observation sat- tem (GCS) to a Universal Transverse Mercator (UTM) ellite operated by DigitalGlobe http:// map projection. The buffer area at each military installa- www.digitalglobe.com that records multispectral imagery tion was calculated and reported in square kilometers. at 2.6 meter resolution at nadir. IKONOS is a similar The digitized rice field polygons within the buffer area of commercial earth observation satellite operated by Geo- each military installation was calculated and reported in Eye http://www.geoeye.com that provides multispectral km2. To account for variation in installation size and thus images at four meter resolution. Although the QuickBird buffer area size, rice field densities were calculated as the images have a higher resolution than the IKONOS image, proportion of rice fields within the buffer area at each

Table 1: Number of Culex tritaeniorhynchus mosquitoes collected in the ROK from 2002 to 2008 by month*

MAY JUN JUL AUG SEP OCT Total

2002 1 28 3724 27515 17995 524 49787 2003 0 7 315 1790 2282 38 4432 2004 0 12 650 32957 9446 1487 44552 2005 0 2 294 4264 2502 136 7198 2006 0 6 40 580 1046 6 1678 2007 1 12 986 2976 845 122 4942 2008 0 5 584 2421 1584 33 4627 *Data are not adjusted for number of trap nights. The numbers in this table include both male and female mosquitoes and those trapped at Gunsan, which was later dropped from the study. Richards et al. International Journal of Health Geographics 2010, 9:32 Page 6 of 10 http://www.ij-healthgeographics.com/content/9/1/32

installation (rice fields proportion = rice field area/mili- A regression model was developed from the data to tary installation buffer area). An example of digitized rice predict mosquito abundance at Camp Humphreys after field data is presented in Figure 3. environmental changes in 2007 altered the amount of rice fields surrounding the installation. Rice field density in Rice Field Analysis the 2008 Camp Humphreys image was applied to the Simple linear regression analysis, using the SPSS® statisti- regression model to determine a predicted mosquito cal program (SPSS Inc., Chicago, IL, USA) was conducted count and then compared to the observed 2008 number on the data using two continuous variables, rice field den- of mosquitoes per trap night. This test was used to Cx. tritaenio- sity and log10 of the average number of explore the model's ability to predict the abundance of rhynchus per trap night from 2002-2008. The rice field female Cx. tritaeniorhynchus collected at an installation. density statistic representing the proportion of rice fields Mosquito prevalence trend analysis showed extreme surrounding Camp Humphreys was based on the 2006 variation over time. Years 2002 and 2004 had abnormally image only. high counts of Cx. tritaeniorhynchus when compared to

Figure 3 Digitized rice field data example. The above is an image of a military installation located in the ROK taken in October. The base is shown with a solid grey cover; the extent of the buffer zone area is depicted with a dark blue line; rice fields are shown with a solid yellow cover. The buffer zone area extends 1.5 km from the military installation perimeter. Richards et al. International Journal of Health Geographics 2010, 9:32 Page 7 of 10 http://www.ij-healthgeographics.com/content/9/1/32

all other years (Table 1). An observational retrospective increase of female Cx. tritaeniorhynchus per trap night study of mosquito prevalence at U.S. military installations can be expected. in the ROK found that Cx. tritaeniorhynchus was the In an exploratory analysis the regression model was most common mosquito species collected in 2002 [38]. In used to predict the abundance of Cx. tritaeniorhynchus at 2002, due to a high number of mosquitoes collected at Camp Humphreys in 2008. Despite major changes to the Gunsan Air Base, the overall average of Cx. tritaenio- Camp Humphreys area, the proportion of rice fields sur- rhynchus populations in the ROK increased by 27.2%, rounding the installation increased by only 1%. The num- while populations of other mosquito species remained ber of predicted mosquitoes per trap night was 18.2 for relatively constant during the same time period [38]. The the 2008 Camp Humphreys image; the number of increased number of Cx. tritaeniorhynchus caught at observed mosquitoes per trap night was 22.2. Gunsan Air Base in 2002 and 2004 could be due to the use of more efficient collection methods during these two Discussion years. At Gunsan, NJ light traps were baited with dry ice This research addresses the issues of mosquito abun- during collections in 2002 and the Mosquito Magnet® was dance and spatial patterns of agricultural land use. used for collections in 2004 [38]. The Mosquito Magnet is Emphasis has been put on determining the relationship the most effective trap for collecting Cx. tritaenio- between average Cx. tritaeniorhynchus collected per trap rhynchus; capturing up to 650 times as many mosquitoes night and density of rice fields surrounding U.S. military as the NJ light trap [23]. Primarily because trapping installations in the ROK; as well as assessing the value of methods were substantially different at Gunsan than that relationship for inclusion in a larger disease risk those at all other installations in the study sample, Gun- model. The future application of rice field density as a san Air Base was excluded from the final analysis. parameter in models used to predict JEV disease risks can A priori statistical power analysis stated that a sample be based on the strength of linear association, as it is pre- of sixteen sites carried 80% power to detect the propor- sented here. tion of variability between mosquito abundance and rice Results revealed that 62% of the variation in the log10 of field density in the model with a coefficient of determina- Cx. tritaeniorhynchus collected per trap night on base tion (R2) of 0.65. For our purposes, an R2 of 0.65 was was accounted for by the density of rice fields in the area desired. The correlation coefficient (r) was also used in surrounding the military base. The remaining 38% can be this analysis to elucidate the relationship between mos- explained by inherent variability or other variables, such quito abundance and the rice field density seen in the as species breeding cycle, proximity to blood source, or data. regional climatic conditions [17,19-21,31]. Our data reaf- firms past JE and Cx. tritaeniorhynchus research and sug- Results gests that rice fields account for a large portion of the Average mosquito counts per trap night and the corre- mosquito's abundance but that populations cannot be sponding rice field density statistics are presented for predicted by rice fields alone [29-31,35,36]. each installation in Table 2. The area surrounding half of A statistically significant linear association was demon- the installations in this study were comprised of less than strated between rice field density and mosquito popula- 10% rice fields. This is in sharp contrast to installations tion. Given the strong relationship demonstrated that were surrounded by the most rice fields; coverage between rice field density and mosquito populations, this ranged between 30-56% of the land surrounding five variable should be considered when modelling Cx. tritaeniorhynchus installations. Mosquito data were log10 transformed prior propagation or JEV transmission risks. to regression analysis. Analysis conducted using log10 Specific limitations to this study with regard to image transformed mosquito data from 2002-2008 demon- data include the inability to automatically extract rice strated a statistically significant linear relationship fields due to the lack of ground vegetation in many of the between rice field density and mosquito abundance (R2 = images used. Due to cloud cover and other limiting fac- 0.62, r = .79, F = 22.72, p < 0.001). A scatter plot of the tors, the remotely sensed imagery used in analysis were data with the regression line is presented in Figure 4. not all acquired during the rice growing season, making The resulting linear model [y = 2.71 × -0.28; where y automated land cover extraction software unable to accu- equals the log10 of the average mosquitoes per trap night, rately identify rice fields. While rice fields were extracted and × equals the proportion of rice field land cover] was manually, the potential for misclassification of land cover used to predict the abundance of post-construction Cx. remained. Field shape, image interpretation, and area tritaeniorhynchus at Camp Humphreys using the 2008 photographs guided rice field identification. This study image. The data reveals that for every 10% increase of rice was also limited by the lack of images at each installation field land cover surrounding an installation, an 86% for each year or month within the study period. Thus, the authors were unable to explore variation in the correla- Richards et al. International Journal of Health Geographics 2010, 9:32 Page 8 of 10 http://www.ij-healthgeographics.com/content/9/1/32

Table 2: Rice field density and average number of mosquitoes collected per trap night, by installation**

Installation Proportion of Rice Monthly Average Number Female Fields in Buffer Area Mosquitoes per Trap Night 2002-2008 (Aug-Sept)

Camp Jackson 0.00 0.62 Camp Casey 0.01 0.83 Camp Hovey 0.02 0.74 Camp Essoyons 0.02 0.73 Camp Nimble 0.02 0.44 Camp Mobile 0.02 1.23 Camp Red Cloud 0.03 0.82 Camp Castle 0.05 0.86 Camp Stanley 0.11 0.57 Camp Long 0.13 0.36 Camp Carroll 0.18 2.36 Camp Eagle 0.21 0.23 Gwangju Air Base 0.30 14.40 Warrior Base 0.38 14.81 Osan Air Base 0.44 3.64 Gunsan AB 0.53 222.42 Camp Humphreys 0.56 22.20 **Gunsan was dropped from subsequent analysis. tion between rice field density and Cx. tritaeniorhynchus This limitation restricts analysis and does not allow the throughout time. This study, and potentially all studies geographical variability among installations to be conducted on military bases in the ROK, will be limited explored. by the number of installations and number of installa- Despite limitations, this study has a number of tions within the USFK mosquito surveillance program. strengths. It utilized USFK mosquito surveillance data collected consistently for over 8 years in the ROK. This data continues to be collected and can be similarly used in the future. It also uses high resolution remotely sensed imagery to determine rice field land cover. Using remotely sensed data to map rice field land cover is less time consuming than conventional ground survey methods used in the past to calculate agricultural land use.

Conclusions osquito per trap night trap per osquito

m Using the data presented here, an overall statistically significant linear relationship between rice field density and Cx. tritaeniorhynchus abundance was found. Use of the regression model to predict mosquito abundance was

of the average of the average inconsistent. Future studies to develop JE risk prediction 10

log models for non-indigenous personnel living at military installations in the ROK should include agriculture land use and irrigation patterns as variables. proportion of rice fields There are a number of implications in this study for future research in JE risk prediction and Cx. tritaenio- Figure 4 Fitted curve of model. The above is a scatter plot of the data rhynchus and the resulting regression line (y = 2.71 × -0.28). Each dot represents population modelling in the vicinity of U.S. mil- data from one installation. itary installations in the ROK. First, automated risk Richards et al. International Journal of Health Geographics 2010, 9:32 Page 9 of 10 http://www.ij-healthgeographics.com/content/9/1/32

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doi: 10.1186/1476-072X-9-32 Cite this article as: Richards et al., The relationship between mosquito abundance and rice field density in the Republic of Korea International Journal of Health Geographics 2010, 9:32