• View in gallery

    Map of Kamphaeng Phet, Thailand. Insets show villages with high and low densities of houses in two subdistricts (Na Bo Kham and Kon Tee).

  • View in gallery

    Distribution of A, Aedes aegypti pupae per house and B, female Ae. aegypti adults per house in Kon Tec and Na Bo Kham, Thailand. Vertical dashed lines assign houses to three density categories (negative, low, and high; see text for additional details).

  • View in gallery

    Temporal dynamics in densities of positive Aedes aegypti containers, Ae. aegypti pupae, and adult female Ae. aegypti in Kon Tee (A and B) and Na Bo Kham (C and D), Thailand.

  • View in gallery

    Annual, seasonal, and house density effects on the distribution of Aedes aegypti pupal densities in Kon Tee (A, B, and C) and Na Bo Kham (D, E, and F), Thailand. P values indicate significance resulting from multinomial logistic regression.

  • View in gallery

    Annual, seasonal, and house density effects on the distribution of Aedes aegypti adult female densities in Kon Tee (A, B, and C) and Na Bo Kham (D, E, and F), Thailand. P values indicate significance resulting from multinomial logistic regression.

  • View in gallery

    Proportion of households visited by Public Health authorities during the survey in low house density villages and high density villages in Kon Tee (KT) and Na Bo Kham (NB), Thailand. Visits consisted of application of adulticides and/or larvicides.

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Spatial and Temporal Patterns in Pupal and Adult Production of the Dengue Vector Aedes aegypti in Kamphaeng Phet, Thailand

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  • 1 Department of Entomology, University of California, Davis, California; Department of Geography, University at Buffalo, Buffalo, New York; Department of Entomology, U.S. Medical Component, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand; Department of Geography, San Diego State University, San Diego, California

We investigated how temporal and spatial effects confound the functional relationship between pupal and adult populations of Aedes aegypti and thus the value of pupal numbers as predictors of dengue transmission risk in Kamphaeng Phet, Thailand. We found considerable seasonal shifts in productivity of key containers. Tires contained much less pupae in the dry season than in the wet season. Earthenware jars and cement tanks for washing purposes were consistent producers over the entire study period. Houses in the two villages, with approximately twice as many houses per unit area, were significantly more likely to have adults and pupae. No significant annual, seasonal, or spatial effects on the strength of correlations between pupal and adult populations were found. Except for 2 (of 16) occasions, pupal, and adult populations were correlated strongly in time and space. Our results are consistent with application of the pupal survey technique for assessing dengue transmission risk.

INTRODUCTION

The increase in and rapid recurrence of major outbreaks of dengue and dengue hemorrhagic fever in many tropical and subtropical regions have made control of this disease a top-priority for many public health authorities.1,2 In the absence of an effective vaccine, efforts have focused on control of the mosquito vector Aedes aegypti L. through reduction of larval development sites (i.e., source reduction) and/or application of insecticides to kill immature forms or adults. Addition of the larvicide temephos to water-holding containers, use of biologic control agents such as Bacillus thuringiensis var. israelensis and the copepod Mesocyclops, and garbage cleanup campaigns have been advocated and used as tools for nationwide vector control programs.24 Many of these programs treated container types equally, thereby not accounting for the fact that production of adult Ae. aegypti is often limited to a relatively small number of water-holding container types, so-called key containers.5 Therefore, we focused our recent research efforts on 1) whether pupal densities correlate with adult female densities, and 2) how the disproportionate distribution of immature mosquitoes can be used to the advantage of control programs for dengue prevention. The larger project, of which this study was part, was aimed at establishing the relationship between various measures of mosquito density and human infection.

Results of a World Health Organization–Tropical Disease Research–funded study that was undertaken in nine Latin American, Asian, and African countries demonstrated that the pupal/demographic survey technique is able to identify “epidemiologically important and unimportant types of containers” for Ae. aegypti.6 The main motives to use this approach are that pupal mortality is slight, pupal numbers are expected to have a strong positive relationship with adult mosquito densities, and, in theory, sampling techniques can reliably estimate the total number of pupae at a given location. Adult Ae. aegypti are much more difficult to sample. Calculated pupal densities, in combination with dengue transmission models, can be compared with expected transmission thresholds based on ambient temperature and immunity in the human population.7

Results from studies on variation in adult production provide important insights into the ecology of Ae. aegypti. However, there is a need to better understand how temporal and spatial effects confound the functional relationship between pupal and adult populations and thus the value of pupal numbers as predictors of dengue transmission risk. Some previous studies focused on either immature811 or adult populations,12,13 whereas others investigated correlations between both life stages.1417 In this study, we examined temporal and spatial patterns in pupal productivity and adult abundance in a dengue endemic area in north central Thailand. Temporal variations were seasonal and annual, whereas spatial variation was the result of varying house density and different geography. We determined how productivity of pupae and adults is correlated and if there is a spatial and temporal pattern in these relationships. Specifically, we asked whether Ae. aegypti life stages are structured differently among seasons, years, and geographic locations? And, does house density play a role in those associations?

MATERIALS AND METHODS

Study area.

The study was conducted in Kamphaeng Phet Province, Thailand (Figure 1). All four dengue virus serotypes circulate in the study area and can cause mild to severe manifestations of dengue fever and dengue hemorrhagic fever.18,19 All studied households were part of a larger epidemiologic study that encompassed > 4,000 households in 19 villages in four subdistricts of Kamphaeng Phet.20 Houses were mapped at the onset of the study and demographic information (number of adults and children living in each house) was recorded.

Study design.

House density, defined as the number of neighboring houses within a 100-meter radius from a house, was calculated for each house of the 19 villages and then averaged for all houses within a village with MapInfo professional version 6.0 software (MapInfo, Troy, NY). We then selected two of the four subdistricts for a comparative study: Kon Tee (16°22′N, 99°38′E) and Na Bo Kham (16°24′N, 99°22′E). Kon Tee is located along the Ping River, which runs from northwest to southeast. Na Bo Kham is situated 20 km west of the river (Figure 1) and is surrounded by gently rolling hills that are covered with open deciduous forest. Irrigated rice fields and sugarcane plantations form the main type of land use in both subdistricts.

We selected the two subdistricts so that we could capture what we thought a priori would be region wide the spatial variation in distribution of houses. Within each subdistrict, two villages were selected of which one had a high density of houses and the other a low density of houses. Kon Tee, village 2 and Kon Tee village 13 had mean ± SD house densities of 27.5 ± 12.0 (n = 152) and 14.0 ± 6.8 (n = 123), respectively. Na Bo Kham village 5 and Na Bo Kham village 15 had mean ± SD house densities of 25.6 ± 8.0 (n = 175) and 16.6 ± 9.0 (n = 132), respectively. In 2004 and 2005, entomologic surveys were carried out twice a year in these four selected villages daily for approximately one month: once at the end of the dry season (from March through April; beginning of the dengue transmission season) and once at the end of the wet season (from September through October; end of the dengue transmission season). In this way, we could investigate the temporal effects of year and season and the spatial effects of subdistrict (geographic location) and house density. In total, 604 households were included over the entire period, of which 482 (79.8%) were visited on all four survey occasions.

Entomologic surveys.

Seven two-person teams inspected the households between 8:00 AM and 12:30 PM. Two teams were responsible for collection of adults and the remaining five teams for collection of immature forms. Each team consisted of one staff member of the United States Armed Forces Research Institute of Medical Sciences Entomology Laboratory in Kamphaeng Phet and one collector was recruited from the village being sampled.

All water-holding containers on the plot of a household were inspected for larvae or pupae and described on entomologic survey forms.15,21 If pupae were encountered, all were collected. If only larvae were encountered, a sample was taken, depending on their density (all if density ≤10, 10 if density between 11 and 100, and 20 if density > 100). Recorded variables included container dimensions, water depth, location (indoor/outdoor), filling method (rain/manual), cover status (with/without), temephos status (with/without), and fish status (with/without). In the laboratory, larvae and pupae were cross-checked with the survey forms and reared to adults. Emerged mosquitoes were killed by freezing and identified as Ae. aegypti, Ae. albopictus, Culex spp., or Toxorhynchites spp. Their numbers were recorded by the individual container they were collected from, sex, and species. At least one larva or pupa was identified from 95% of the samples that were positive for immature mosquitoes. Larvae or pupae died or escaped before identification of adults in the remainder of the samples.

Adult mosquitoes were collected with battery-powered backpack aspirators (John W. Hock Company, Gainesville, FL).13,15 Collections were made from all indoor rooms, if permitted. Outdoor collections were made in separate collection cups from walls and objects located within three meters of the edge of the roof, but not from vegetation. For houses elevated on poles (approximately half of the houses), the outdoor area included the area underneath the house. Collections were also made from the toilet area. After collection of adult mosquitoes, we asked homeowners about the date of last visit of Public Health authorities and the purpose of their visit (application of temephos or spraying of adulticides). This information was recorded on forms that also included information on house structure and water use practices.

In the laboratory, adult mosquitoes were killed by freezing and identified as Ae. aegypti, Ae. albopictus, Culex spp., Armigeres spp. or Anopheles spp. Their numbers were recorded on entomologic survey forms by individual household, location (indoor/outdoor/toilet) and mosquito sex. All entomologic data were entered twice in Microsoft Office Access 2003 (Microsoft Corporation, Redmond, WA). The two databases were then synchronized and cleaned by checking empty cells, extreme values, and variables with inconsistent data when compared with other variables.

Data analyses.

To overcome the non-normal distribution of pupal and adult populations across houses (Figure 2), we investigated spatial and temporal effects with multinomial regression, whereby the 2,123 samples that were obtained from houses over the entire study were assigned to one of three pupal and adult density categories: 1) no pupae or adults (negative), 2) less than the median (low), and 3) more than the median of positive houses (high). For pupae, the median value of positive houses was 2.25 pupae per person per house, whereas for adults the median was 0.5 females per person per house. A total of 1,167 (53.3%) were assigned to the negative pupal density category, 488 (22.3%) to the low pupal density category, and 477 (21.8%) to the high pupal density category. For adults, 1,334 (60.9%) were assigned to the negative category, 453 (20.7%) to the low category, and 345 (15.7%) to the high category (Figure 2).

RESULTS

During four entomologic surveys, 37,663 water-holding containers were characterized. Of those, 3,914 (10.4%) were positive with Ae. aegypti immature forms and 1,866 (5.0%) were positive for pupae. Most water-holding containers were located outdoors (66.1%), did not have a lid (72.1%), and were manually filled (77.9%) by the occupants of the household. In contrast, 56.8% of the positive containers were located outdoors, 83.7% did not have a lid, and 83.7% were manually filled. Aedes albopictus immature forms were found in 864 (2.3%) of inspected containers and 222 (25.7%) of these were shared with Ae. aegypti.

Container productivity.

Container numbers and total production were divided by container class for three consecutive surveys and are shown in Table 1. We had not developed a consistent method of container classification for the first survey in March–April 2004. Therefore, those data are not included here. A standardized classification scheme was applied to the remaining three surveys.21 Total production of pupae from all containers ranged from 1.29 to 1.92 pupae per person. Ranking container classes by pupae productivity showed that earthenware jars and cement tanks, both used for general washing purposes, were consistently the two most productive container classes. Their relative contributions to the total pupae standing crop were 42.3%, 59.0%, and 42.6% for September–October 2004, March–April 2005, and September–October 2005, respectively. Comparing only the wet season surveys (September–October) showed that rubber tires (no use) and cement tanks used for flushing toilets were consistently productive in 2004 and 2005, whereas these container classes were much less productive during the dry season (March–April 2005, ranks 5 and 19). Relative production was consistent throughout the three surveys with 58.7%, 63.8%, and 59.4% in September–October 2004, March–April 2005, and September–October 2005, respectively, being produced by earthenware jars used for washing, cement tanks used for washing and flushing, and tires. In March–April 2005, the four most productive containers (ranks 1–4) accounted for 70.3% of pupae production.

Temporal and spatial patterns in vector abundance.

Figure 3 shows the temporal dynamics of the number of positive containers per house, pupae per person, and adult female Ae. aegypti per person for each of the four villages. Numbers shown are pupal and adult female densities at the village level calculated as total pupae/adults per village divided by total persons per village. Numbers of positive containers were higher than 2.0 (equivalent to a Breteau index of 200) on 7 of 8 occasions in Kon Tee subdistrict, whereas they were always less than 2.0 in Na Bo Kham. Pupal densities ranged from 1.7 to 3.6 pupae per person in Kon Tee and from 0.24 to 2.8 pupae per person in Na Bo Kham. Adult densities were more constant over the seasons and two years of study, especially in Kon Tee.

To overcome problems with analysis of the highly skewed distribution of pupae and adults at the household level (many houses are devoid of pupae and few houses have many, Figure 2), we further investigated spatial and temporal effects with multinomial regression. In this case, pupal/adult densities were calculated as their numbers per house divided by the number of persons living in that house. Thus, the 2,123 house samples that were obtained over the entire study were assigned to one of three pupal and adult density categories; i.e., negative, low, and high, as described earlier.

First, we modeled the effects of year, season, subdistrict, and house density and their two-way interactions. That analysis showed strong interacting effects of subdistrict × year and subdistrict × season for the multinomial pupal density model. There was a strong interacting effect of subdistrict × season for the multinomial adult density model (all P < 0.001, by likelihood ratio test).

Next, we modeled annual, seasonal, and house density effects separately for the two subdistricts. Distributions of the relative number of houses in each density category are shown in Figures 4 and 5 for pupae and adults, respectively. The pupal density model for Kon Tee showed no significant effects of year or season (χ2 = 2.511, degrees of freedom [df] = 2, P = 0.285 and χ2 = 0.892, df = 2, P = 0.640, respectively; Figures 4A and B). In Na Bo Kham, the second year had significantly more houses without pupae at the expense of both low and high density category houses (χ2 = 22.812, df = 2, P < 0.001; Figure 4D). The seasonal effect in Na Bo Kham was strong and opposite of what was expected: the dry season had significantly more houses belonging to the low and high categories (χ2 = 83.759, df = 2, P < 0.001; Figure 4E). The effect of house density was consistent for both subdistricts. In villages where houses were more distantly spaced apart (i.e., lower density), houses were more likely to be negative (χ2 = 7.222, df = 2, P = 0.027 and χ2 = 9.138, df = 2, P = 0.010 for Kon Tee and Na Bo Kham, respectively; Figures 4C and F).

In Kon Tee, the adult density model showed no significant shifts between years, which was consistent with pupal distributions (χ2 = 4.181, df = 2, P = 0.124; Figure 5A). In Na Bo Kham, during the second year there was a significant increase in houses with low densities of adult female Ae. aegypti compared with the first year (χ2 = 6.455, df = 2, P = 0.040; Figure 5D), opposite of what was seen for the pupal population (Figure 4D).

Although the relative proportion of negative houses remained the same between seasons, there was an increase in high-density houses at the expense of low-density houses in the wet season in Kon Tee (χ2 = 9.397, df = 2, P = 0.009; Figure 5B). Densities were lower in the wet season in Na Bo Kham (χ2 = 171.802, df = 2, P < 0.001; Figure 5E), which is consistent with the pupal distributions. The effects of house density were highly significant in both subdistricts: low-density villages had more negative houses and less houses in the low and high density categories (χ2 = 30.826, df = 2, P < 0.001 and χ2 = 14.147, df = 2, P = 0.001 for Kon Tee and Na Bo Kham, respectively; Figures 5C and F), which is consistent with the effect of house density in the pupal population.

Correlations between pupal and adult populations.

Pearson correlation coefficients among numbers of Ae. aegypti pupae per person and adult female Ae. aegypti per person were highly significant, except on two occasions. The two wet season surveys in Na Bo Kham village 5 (high density) showed no correlation between pupae and adult numbers (Table 2). Because this village had many houses without pupae or adults during the wet season (Figures 4E and 5E), we confirmed our findings by calculating Kendall’s τ between nominal pupal and adult categories for these two wet season surveys. Values of τ remained insignificant (τ = −0.013, P = 0.875, τ = 0.14, P = 0.096). The remaining Pearson correlation coefficients ranged between 0.246 and 0.460. We did not find any variable (year, season, subdistrict, or house density) that significantly explained variation in observed coefficients (P > 0.24, by Mann-Whitney test).

To investigate the role of ongoing vector control activities, we calculated the proportion of houses where Public Health authorities had visited during the survey and either applied adulticides or larvicides (Figure 6). Although there were marked differences between subdistricts and seasons, these differences could not explain the absence of correlation between pupal and adult populations in Na Bo Kham village 5 (high density) during the two wet season surveys.

DISCUSSION

The identification of epidemiologically important containers for Ae. aegypti pupal productivity is a focus of evaluation for improving contemporary vector control programs.6 In our study area, there were four container classes that consistently produced approximately 60% of Ae. aegypti pupae; i.e., jars and tanks used for general washing purposes, tanks used for flushing bathrooms, and tires. Although these were the top four producers near the end of the wet season (September–October of 2004 and 2005), two container classes (i.e., jars and tanks that were not in use), ranked 3 and 4 at the end of the dry season in 2005. There are two factors contributing to this finding. First, rain-filled tires were much less numerous in the dry season, and thus dropped out of the top of pupae producers. Simultaneously, jars and tanks not in use became more productive although they were not more abundant (Table 1). The reason for this finding is not entirely clear, but may be related to a more economical use of water during the dry season. Water is scarce when it cannot be collected from rain and thus may be stored and stand in containers for a longer time with less turnover. As a result, water storage jars during the dry season may accumulate more food and become more productive.22 Unfortunately, we were not able to confirm if the same trend was true in 2004 because of inconsistently classified containers.

Depending on assumptions regarding initial seroprevalence of dengue antibody in the human population and number of viral introductions, our values of pupal densities per person (ranging from 1.7 to 3.6 in Kon Tee and from 0.2 to 2.8 in Na Bo Kham) are either above or below transmission thresholds predicted from dengue transmission threshold models (Container Inhabiting Mosquito Simulation Model and Dengue Transmission Simulation Model).7 For example, at 28°C, the threshold with 67% seroprevalence and monthly introductions is approximately 1.3 pupae per person, whereas the same threshold with 33% prevalence is approximately 0.6. In theory, both threshold values would lead to control efforts in Kon Tee, but not necessarily in Na Bo Kham. This finding further stresses the importance of the collection of seroprevalence (more specifically serotype-specific herd immunity) and climate data in addition to entomologic density estimates in reaching well-informed decisions regarding the goals for vector control efforts.

Because of the highly skewed distributions of the number of pupae and adults per person per house, we used multinomial logistic regression to detect spatial and temporal patterns in pupal and adult densities. As Figure 2 illustrates, splitting the household densities into three groups, i.e., one major group of negative houses, a second group with lower than the median of positive houses, and a third group that contained the extreme values of the tail of the distribution (higher than the median of positive houses), enabled us to distinguish changes in density that would not be possible with logistic (binomial) regression in which houses are classified as either positive or negative. For example, a decrease in density could result from low houses becoming negative or from extreme producing houses from the high group becoming low producing. We would also be able to detect significant shifts in density distribution that would not be detectable when investigating only mean population densities. For example, in one season all houses could be in the low category, whereas in the next some of these could become negative and others extremely productive. Although in that case a change in mean density might not be detected, some houses could have increased numbers of pupae that may be of epidemiologic significance. Our data did not enable a linear regression of transformed pupal and adult densities per person at the specified spatial and temporal levels, as for example, was done by Morrison and others.8 The latter study included multiple locations (8 geographic zones) and sample events (12 sampling circuits over 4 years) to determine spatial and temporal patterns in mosquito productivity.

Our analyses showed that house density had the strongest and most consistent effect on pupal and adult densities. Houses in the two villages with approximately twice as many houses per surface area were significantly more likely to have adults and pupae. There are at least two explanations for this observation. First, for reasons that at this time are not understood, increased housing density may provide increased opportunities for Ae. aegypti larval development and adult survival. Second, for adult Ae. aegypti, documented patterns of dispersal may be an important factor. In Iquitos, Peru (a crowded urban environment where most houses are close to one another or are adjoining) a spatial analysis demonstrated that adults clustered within a 30-meter radius.14 Mark-release-recapture studies in rural Thailand villages and Puerto Rico showed little dispersal of Ae. aegypti beyond neighboring houses.23 In terms of distances between houses, we calculated that in our high-density villages 5.2% and 6.9% of houses were spaced > 30 meters apart, whereas in the low-density villages 15.5% and 16.5% of houses were spaced > 30 meters apart. Because of the closer distance between houses in high-density villages, adult mosquitoes that emerge in one house may be more likely to disperse to a neighboring house. Thus the proportion of houses with adult Ae. aegypti may be relatively higher in these high-density villages. Confirmation of this proposition requires additional field research.

We hypothesized that the strength of correlations between pupal and adult populations depends on season, year, or geographic location. We also hypothesized that houses that were more closely spaced would show less correlation because of a more pronounced smoothing effect as a result of adult dispersal that is more likely if houses are spaced closer. However, no significant main effects on the strength of the correlations were found. Pupal and adult populations correlated strongly, except for two occasions. These occasions were from the two wet season surveys in Na Bo Kham village 5 (high density). Because the study was designed in such a way that it did not interfere with ongoing vector control activities by public health authorities, which mostly consisted of the application of temephos, we suspected that vector control activities may be the cause of the absence of correlation between pupal and adult populations during the wet, high transmission seasons in Na Bo Kham village 5. Although there was more vector control activity during the wet season of year 1 in Na Bo Kham village 5 (Figure 6), the other village in Na Bo Kham had what appeared to be a similar amount of vector control and showed a significant correlation between pupal and adult populations. An earlier study in the same villages found that households in Na Bo Kham were more likely to protect containers (but this included temephos use provided by public health authorities) and use preventive measures against adult mosquitoes.20 This finding may partially explain the absence of correlation in current study.

Our results are consistent with the concept of adaptive Ae. aegypti interventions. Across different villages and sampling time periods, we detected considerable shifts in productivity of key containers. For example, tires were main producers in the wet season, whereas they did not contribute to pupal production in the dry season. However, two types of containers (earthenware jars and cement tanks for washing purposes) were consistent producers over the entire study period. The strong correlation between pupal and adult populations is consistent with application of the pupal survey technique for assessing dengue transmission risk. However, development of a precise Ae. aegypti control package for a given location will require tailoring control activities to site-specific conditions, patterns of mosquito vector production, and susceptibility of the human population to dengue infection.

Table 1.

Container classes (as defined by shape, use, and material) and their numbers per house (CT/HSE), numbers pupae positive per house (PPC/HSE), contribution to Aedes aegypti pupae per person (PU/PER), and their relative rank during three surveys in Kamphaeng Phet Province, Thailand

Sep/Oct 2004Mar/Apr 2005Sep/Oct 2005
ShapeUseMaterialCT/HSEPPC/HSEPU/PERRankCT/HSEPPC/HSEPU/PERRankCT/HSEPPC/HSEPU/PERRank
JarWashingEarthenware1.830.220.4611.840.250.6411.880.130.272
TankWashingCement0.690.060.1420.840.140.5020.830.070.281
TankFlushingCement0.790.050.1330.640.060.0850.660.040.084
TireNo useRubber0.330.060.1140.190.010.01190.610.080.143
Ant trapPrevent ant infestationEarthenware0.500.070.0950.420.060.0670.430.030.0213
JarNo useEarthenware0.270.050.0760.280.040.1230.340.030.047
Ant trapPrevent ant infestationPlastic0.630.070.0670.410.040.0580.630.030.039
BucketWashingPlastic0.970.030.0581.010.040.0760.750.010.0115
TankNo useCement0.130.010.0490.080.010.1040.150.010.048
BucketNo usePlastic0.260.020.03100.210.010.00280.330.020.0212
All containers17.030.891.4216.590.891.9219.130.671.29
Table 2.

Pearson correlation coefficients between ln-transformed numbers of Aedes aegypti pupae per person and Ae. aegypti females per person in Kamphaeng Phet, Thailand*

YearSeasonSubdistrictHouse densityPearson correlation coefficientNP
* KT = Kon Tee subdistrict; NB = Na Bo Kham subdistrict.
1DryKTLow0.2541120.007
1DryKTHigh0.322137< 0.001
1DryNBLow0.420125< 0.001
1DryNBHigh0.460134< 0.001
1WetKTLow0.3121150.001
1WetKTHigh0.308141< 0.001
1WetNBLow0.337126< 0.001
1WetNBHigh0.0331400.699
2DryKTLow0.411110< 0.001
2DryKTHigh0.380142< 0.001
2DryNBLow0.335126< 0.001
2DryNBHigh0.2471700.001
2WetKTLow0.349114< 0.001
2WetKTHigh0.389143< 0.001
2WetNBLow0.401125< 0.001
2WetNBHigh−0.0071720.929
Figure 1.
Figure 1.

Map of Kamphaeng Phet, Thailand. Insets show villages with high and low densities of houses in two subdistricts (Na Bo Kham and Kon Tee).

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 79, 2; 10.4269/ajtmh.2008.79.230

Figure 2.
Figure 2.

Distribution of A, Aedes aegypti pupae per house and B, female Ae. aegypti adults per house in Kon Tec and Na Bo Kham, Thailand. Vertical dashed lines assign houses to three density categories (negative, low, and high; see text for additional details).

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 79, 2; 10.4269/ajtmh.2008.79.230

Figure 3.
Figure 3.

Temporal dynamics in densities of positive Aedes aegypti containers, Ae. aegypti pupae, and adult female Ae. aegypti in Kon Tee (A and B) and Na Bo Kham (C and D), Thailand.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 79, 2; 10.4269/ajtmh.2008.79.230

Figure 4.
Figure 4.

Annual, seasonal, and house density effects on the distribution of Aedes aegypti pupal densities in Kon Tee (A, B, and C) and Na Bo Kham (D, E, and F), Thailand. P values indicate significance resulting from multinomial logistic regression.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 79, 2; 10.4269/ajtmh.2008.79.230

Figure 5.
Figure 5.

Annual, seasonal, and house density effects on the distribution of Aedes aegypti adult female densities in Kon Tee (A, B, and C) and Na Bo Kham (D, E, and F), Thailand. P values indicate significance resulting from multinomial logistic regression.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 79, 2; 10.4269/ajtmh.2008.79.230

Figure 6.
Figure 6.

Proportion of households visited by Public Health authorities during the survey in low house density villages and high density villages in Kon Tee (KT) and Na Bo Kham (NB), Thailand. Visits consisted of application of adulticides and/or larvicides.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 79, 2; 10.4269/ajtmh.2008.79.230

*

Address correspondence to Constantianus J. M. Koenraadt, Laboratory of Entomology, Department of Plant Sciences, Wageningen University, PO Box 8031, 6700 EH, Wageningen, The Netherlands. E-mail: sander.koenraadt@wur.nl

Authors’ addresses: Constantianus J. M. Koenraadt, Laboratory of Entomology, Department of Plant Sciences, PO Box 8031, 6700 EH, Wageningen University, Waginingen, The Netherlands, E-mail: sander.koenraadt@wur.nl. Jared Aldstadt, Department of Geography, University at Buffalo, 105 Wilkeson Road, Buffalo, NY 14261, E-mail: geojared@buffalo.edu. Udom Kijchalao, Ratana Sithiprasasna, and James W. Jones, Department of Entomology, U.S. Army Medical Component, Armed Forces Research Institute of Medical Sciences, 315/6 Rajvithi Road, Bangkok 10400, Thailand, E-mails: rsithiprasasna@yahoo.com, udomk@afrims.org, and james.jones@afrims.org. Arthur Getis, Department of Geography, San Diego State University, San Diego, CA 92182, E-mail: arthur.getis@sdsu.edu. Thomas W. Scott, Department of Entomology, University of California, 1 Shields Avenue, Davis, CA 95616, E-mail: twscott@ucdavis.edu.

Acknowledgments: We thank the residents of Kon Tee and Na Bo Kham, Kamphaeng Phet, Thailand for participating in the surveys and allowing us to collect mosquitoes in their houses. The collaboration with the staff at the Public Health Offices of Kon Tee and Na Bo Kham and the local public health volunteers is greatly appreciated. We also thank the staff of the Kamphaeng Phet Entomology Laboratory for their assistance.

Financial support: This study was supported by grant AI-034533 from the National Institutes of Health

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