Dengue is the most rapidly spreading mosquito-borne viral disease in the World [1
]. Its incidence has increased 30-fold in the last 50 years and has extended to new areas, across both rural and urban environments [1
]. The South, South-East Asia and the Western Pacific WHO regions are among the most affected areas [1
]. Dengue or dengue-like transmission has been observed in southern Taiwan since the late 19th century [2
], initially as intermittent epidemics at intervals of up to 40 years [3
]. However, for the past 10 years, dengue epidemics have become an annual phenomenon with the cities of Kaohsiung and Fengshan as the main foci of activity.
The dengue viruses (DENVs) are transmitted to humans by Aedes (Ae.)
mosquitoes (vector), in particular Ae. aegypti
and Ae. albopictus
]. There is currently no effective treatment or available vaccine against dengue [5
], hence current prevention and control policies mainly aim to reduce human-mosquito contact or to decrease the vector population to levels where viral transmission is unsustainable. To ensure efficient prevention policies, it is important to understand the relative impact of vector and host density on the dispersal of DENVs within an area.
The relationships of the dengue incidence–mosquito abundance and dengue incidence-human density are still not well understood. Since the density of adult mosquitoes is difficult to estimate, immature vector data were widely used for evaluating the incidence–mosquito relationship [6
]. Some entomology studies have found no correlation between dengue incidence and immature vectors, neither temporally nor spatially [6
]. However, a study in Trinidad recently showed that high dengue incidences were significantly related to high mosquito larval densities during certain years [11
]. Other spatial studies in Cuba, Trinidad and Thailand have successfully demonstrated that the Breteau index (BI) and house index can be an indicator for incidence [12
]. The association of dengue incidence and human density are also ambiguous. Studies in Brazil have shown no correlation between dengue incidence and human density [15
], but researches in both Taiwan and Puerto Rico have proven that the spatial distribution of dengue incidence may be positively related to the population density [16
]. Population density and urbanization are also considered as risk factors for DENV spread in Argentina and Hawaii [18
]. Moreover, in hyperendemic areas of Thailand, DENVs transmission is more prevalent in children in localized neighborhoods [20
Until now, most studies of dengue-mosquito or dengue-human relationships have presented a global perspective by which any relationship was assumed to be spatially constant across the whole study area, thereby ignoring local variations. However, this assumption may be inappropriate since the dengue-mosquito or dengue-human relationships could be positively correlated in some study areas, but negatively or not correlated at all in other areas. For example, a small number of female mosquitoes in a very dense area is sufficient to cause an outbreak. This study was conducted to evaluate the hypothesis that spatial heterogeneity existed for dengue-mosquito and dengue-human relationships. We demonstrated that the variation of dengue incidences among study areas was reflected by the densities of both immature vectors and hosts. By capturing the local relationships across the space, the authorities can design more effective, locally-specific strategies. This understanding is especially important where the control and prevention resources are limited.
This study provides further indications that the relationships of dengue incidence-maximum BI and dengue incidence-population density were spatially non-stationary in Kaohsiung and Fengshan cities. In regression maps, it is clear that the intensity and directions of the influence of maximum BI and population density on dengue incidence were different in the study area. This result gives the policy makers more ideas how to better adopt specific control and prevention strategies to specific areas [26
The spatial heterogeneity of intercept results in Figure 5(a)
could imply that the DENVs seroprevalence was non-stationary. Our study found that the density of immature vectors was a significant predictor of dengue incidence in some areas with either positive or negative correlations. Reducing immature mosquito densities is currently the major control and prevention approach for dengue [1
]. However, the results from this study suggest that this strategy may not be spatially and universally suitable for the control of dengue, especially for those areas with negative incidence-maximum BI correlations. Possible reasons could be that other than immature mosquito density, local characteristics could affect dengue transmission as well. For instance, some rural areas with high vector density may lack common exposure sites to humans, thus making outbreaks less likely to occur. On the contrary, places where crowds gather easily like markets, parks, train stations and schools may propel huge dengue outbreaks even though the mosquito density is low [17
]. Human activities that promote host-vector contact increases the risks for people to be infected within a short distance [1
]. This study showed that the distribution of dengue incidence-BI relationships was very similar to the distribution of districts which implied the presence of additional risk factors, such as the age distribution in human population, human activity [15
], housing structures/patterns [30
], environmental factors [29
], and serosurveillance [32
]. These other factors should also be considered, since the diversities of these factors were large among the districts.
In the northern part of the study area, higher human densities were shown to contribute to higher dengue incidence rates. This positive relationship was expected as higher human density may lead to higher vector-host contact rates. A previous finding in Taiwan showed that the relative risk of accumulated dengue incidence for areas with more than 10,000 people/km2
was 10-fold compared to areas with less than 1,000 people/km2
]. Other studies in Florida and Puerto Rico showed that the human population had almost the same spatial pattern as the number of dengue cases during the study period [17
]. However, the GWR results also demonstrated that in some areas higher incidence related to lower human densities and vice versa [Figure 5(f)
]. One explanation could be that in scattered populated areas, mosquitoes tend to aggregate since fewer blood sources were available [33
]. Human travelling behaviors should also be taken into account for the link between higher incidence and lower population density. Travelers not only could initiate new indigenous epidemics, their travelling waves could also contribute to dengue occurrence in low population and rural areas [34
]. According to our findings and those from other studies mentioned above, the relationship between human population/density and dengue occurrence remains controversial. Further studies should take more spatial information into consideration such as dwelling density [31
], type of household [36
], socioeconomic status [15
], age and gender distributions [31
], pesticide spraying areas and frequencies, water storage habits and landscape [37
]. Successful dengue transmission requires that the virus, vector, and host exist in the same areas and interact properly. Understanding the relationship among them is necessary and urgent for more effective disease control.
The relationship between vector abundance (both immature and mature stages) and dengue occurrence has been discussed in many studies [6
]. This is a practical issue especially important for policy makers to decide the control and prevention measures. This study provides insight into the spatial heterogeneity of IR-immature mosquito density relationships at Li level; however, there were some limitations for applying entomology data. First, the traditional indicators (Stegomyia
indices) such as house index, container index and Breteau index are based on the immature stages of mosquitoes, but larvae/pupae quantities have no direct link with adult abundance and thus an estimate of dengue transmission risk may not be reliable [8
]. Moreover, these indices provide little information about the container productivity of vector. Assuming all positive habitats have equal vector contribution could lead the researchers to make false estimations of adult amount [41
]. The information like number of vector per person or per unit area, which also relates to dengue transmission is disregarded in these indices as well [42
]. Moreover, if we directly apply adult index for dengue risk assessment to avoid the limitations of immature stage data, the major problem would be the ratio of captured vectors to existing mosquitoes is still unknown. In this study we chose the monthly maximum BI in each Li as the measurement since we assumed the maximum BI was the best entomology indicator for the dengue cases. In addition to the vector indices problems, the susceptibility of the population to a specific dengue virus serotype is also a great contributor to the scale of epidemics. Once infected a person would acquire lifelong protective immunity to the infective serotype [43
]; in other words, the incidence estimation is hindered by a lack of information concerning the overall population immunity to certain serotypes. This makes the estimation of case-vector relationship more complicated. Finally, silent DENVs transmission was not considered in this study.
To improve the understanding of incidence-vector and incidence-host relationships, the followings could be further examined. First of all, the researchers could adopt GWR space-time analyses, such as stratifying the year of 2002 into different periods, or analyzing more than one epidemic year. This approach could provide more detailed patterns of spatial autocorrelation changes of incidence-vector and incidence-host associations. Secondly, the researchers could use other BI calculations such as minimum BI or average BI to see whether different incidence-BI relationships would be generated. Threshold effect of BI could also be considered. Thirdly, categorizing human by different age groups in the GWR model could assist policy makers to determine which actions are suitable for different populations. Finally, researchers could also separate Ae. aegypti and Ae. albopictus for relationship analyses to study the incidences associated with different vector ecologies.
The geographical heterogeneity was detected by the GWR method in the relationships of dengue incidence with immature mosquito and human density (Figure 5
). We used GWR since the conventional regression, OLS, cannot discriminate the spatial variation in relationships if geographical nonstationarity exists. The results of Adjusted R2
, AIC/AICc and ANOVA all indicated GWR was a better model to explain this dataset. GWR approaches have been applied in a lot of areas, such as public health and demography, as an exploring method for identifying the spatial variations [44
]. However, the GWR applications are limited for some reasons. First, the results conducted from GWR models were very sensitive to the chosen kernel type and bandwidth methods [47
]. Next, the non-linear term cannot be added in the GWR models and the model inferences cannot be done in GWR [24
]. Future research could use more advanced methods like Bayesian additive regression models, which are based on Markov chain Monte Carlo (MCMC) algorithms for parameter estimations and inferences to overcome the problems mentioned above [49