 Research
 Open Access
 Published:
Applying fuzzy logic to assess the biogeographical risk of dengue in South America
Parasites & Vectors volume 12, Article number: 428 (2019)
Abstract
Background
Over the last decade, reports about dengue cases have increase worldwide, which is particularly worrisome in South America due to the historic record of dengue outbreaks from the seventeenth century until the first half of the twentieth century. Dengue is a viral disease that involves insect vectors, namely Aedes aegypti and Ae. albopictus, which implies that, to prevent and combat outbreaks, it is necessary to understand the set of ecological and biogeographical factors affecting both the vector species and the virus.
Methods
We contribute with a methodology based on fuzzy logic that is helpful to disentangle the main factors that determine favorable environmental conditions for vectors and diseases. Using favorability functions as fuzzy logic modelling technique and the fuzzy intersection, union and inclusion as fuzzy operators, we were able to specify the territories at biogeographical risk of dengue outbreaks in South America.
Results
Our results indicate that the distribution of Ae. aegypti mostly encompasses the biogeographical framework of dengue in South America, which suggests that this species is the principal vector responsible for the geographical extent of dengue cases in the continent. Nevertheless, the intersection between the favorability for dengue cases and the union of the favorability for any of the vector species provided a comprehensive map of the biogeographical risk for dengue.
Conclusions
Fuzzy logic is an appropriate conceptual and operational tool to tackle the nuances of the vectorillness biogeographical interaction. The application of fuzzy logic may be useful in decisionmaking by the public health authorities to prevent, control and mitigate vectorborne diseases.
Background
Dengue is one of the diseases with most epidemiological global relevance in the last decades [1,2,3,4,5,6,7,8,9]. Over this century, dengue has become a growing public health problem and about half of the world’s population is currently at risk of dengue infection [7, 8, 10,11,12]. This is especially a concern in South America, where historical records of dengue epidemic outbreaks report upsurges every three or five years from the seventeenth century until the first half of the twentieth century [13, 14].
Aedes mosquitoes, namely the yellow fever mosquito (Aedes aegypti) and the Asian tiger mosquito (Ae. albopictus), are the most important dengue vectors in the world [15,16,17]. The number of studies about the mosquitoes of the genus Aedes as transmission vectors of human infectious diseases has recently increased remarkably [7, 10, 18,19,20,21]. Different authors have studied relevant aspects of mosquitodengue relationships from phylogenetic [22], ecological [17,18,19], physicochemical [20, 23], genetic [21] and biogeographical perspectives [7, 10, 24, 25].
A biogeographical approach to the study of zoonotic diseases, known as pathogeography, has contributed with relevant advances in the knowledge of infectious disease macroecology and distribution [26,27,28,29]. It has also provided a proper analytical framework for the study of vectorillness interaction useful for management or surveillance. Species distribution models (hereinafter SDM) have been particularly used to investigate the environmental drivers for the distribution of Aedes species [25], to map the global distribution of the Aedes species according to the effect of temperature [10, 25], precipitation, and some land cover variables [7, 10], or to forecast the possible effects of climate change scenarios for Aedes species distributions [10]. Other studies also took into account economic information [4], or focused on predicting and determining the global burden of dengue [4, 24]. However, the biogeographical framework of vectorillness interaction that could reveal the largescale risk of dengue occurrence remains poorly understood.
The current range occupied by Aedes mosquitoes (Ae. aegypti and Ae. albopictus) in South America is wider than the known dengue cases. For some reason not yet fully elucidated, there are territories occupied by Aedes vectors with and without dengue cases. This suggests that the relationship between the occurrence of Aedes mosquito populations and cases of dengue is not clearcut, and that a fuzzylogic approach is worth considering. In contrast to crisp logic, Zadeh [30] proposed the fuzzy set theory, which avoids the use of discrete trueorfalse syllogisms, thus conferring a conceptual malleability suitable for reallife situations. Salski and Kandzia [31] emphasized the continuous character of nature, which implies that living beings are distributed in time and space essentially in a gradual and fuzzy manner. A fuzzy logic approach is consequently useful for processing and modelling environmental data [32]. Thus, the application of fuzzy logic could be helpful to recognize the biogeographical vectorillness interaction and the dynamism in the risk of dengue occurrence, and to establish the biogeographical framework in which the disease occurs.
Fuzzy logic led to the notion of environmental favorability, a concept related to, but different from, probability of occurrence [33]. Favorability functions can be used in SDM, and are particularly helpful when models of several species are involved in the study, as they allow the comparison between models for species or cases differing in prevalence, using fuzzy logic tools [28, 34,35,36,37,38].
In this study, our aims were to establish the biogeographical context in which dengue cases occur in South America and to map the areas favourable for new cases to occur. We assessed vectorillness biogeographical relationship using fuzzy logic to determine the different environmental drivers that favor the occurrence of both Aedes vectors and of dengue cases. We aimed to identify the territories more at biogeographical risk of dengue outbreaks, which may be helpful in order to apply measures for the management and control of this recurrent disease.
Methods
Study area and species range
We analyzed Ae. aegypti, Ae. albopictus and dengue virus occurrences on a 0.5° × 0.5° grid (6430 cells of approximately 50 km × 50 km at the equator) to identify the biogeographical relationship between Aedes vectors and dengue cases. We used grids instead of geographical locations, thus solving a large part of the spatial autocorrelation problems derived from sampling bias or observation spatial clustering. Dengue virus occurrences were obtained from global occurrence records published from 1960 to 2012 in Messina et al. [5], with 731 grid cells with confirmed cases, which cover 11.37% of South America (Fig. 1). Aedes aegypti and Ae. albopictus occurrences were obtained from the global compendium of Aedes aegypti and Ae. albopictus occurrence [8] and from the Faculty of Science of the Republic University of Uruguay (inbuy.fcien.edu.uy, accessed in May 2017), with data spanning from 1960 to 2013, and from 1986 to 2014, respectively. Aedes aegypti was confirmed to occur in 1688 cells whereas Ae. albopictus presence was confirmed in 957 cells, covering 26.25 and 14.88% of South America, respectively (Fig. 1).
Environment predictors and distribution modelling
We modelled the distribution of the two vector species and of dengue virus occurrence (the target variables) on the basis of a set of explanatory variables that could potentially affect them at the spatial resolution here applied [39, 40] (Table 1). The explanatory variables were related to different environmental factors that could determine the area occupied by both Aedes species and the extent of dengue virus occurrence in South America: spatial configuration, topography, climate (rainfall and temperature), hydrology, land use and other human activities (Table 1).
To define the spatial structure of each distribution, we considered a polynomial trendsurface analysis [41] that included a quadratic and cube effect of latitude and longitude and interactions between them. Spatial structure is known to be functional in biogeography, as purely spatial trends derive from biological processes such as history, spatial ecology and population dynamics [42]. Spatial structure is known to be functional in biogeography, as purely spatial trends derive from biological processes such as history, spatial ecology and population dynamics [42]. To define the spatial structure of each distribution, we considered a polynomial trend surface analysis [41] that included a quadratic and cube effect of latitude and longitude and interactions between them. For this, we performed a logistic regression of Ae. aegypti, Ae. albopictus and dengue virus distribution data on latitude (Lat), longitude (Lon), Lat2, Lon2, Lat3, Lon3, Lat × Lon, Lat2 × Lon and Lat × Lon2. Specifically, we performed a backward stepwise logistic regression with each event (both Aedes vectors and dengue cases) and those nine spatial terms as predictor variables in order to remove the nonsignificant spatial terms from models [41]. In this way, in the modelling procedure we included the resulting lineal combinations (ysp) as the spatial variable without nonsignificant spatial terms.
We used this spatial lineal combination (ysp) and the rest of variables listed in Table 1 (environmental factors) to produce distribution models according to all the explanatory factors together. To do this, we first analysed the effect of each explanatory variable on each target variable on a bivariate basis, by performing a logistic regression of each target variable on each explanatory variable separately. So, as Miller et al. [43] indicated, including the variation of the response variables separated into environmental and spatial components (represented by a trend surface of geographical coordinates) is a way to quantify the spatial dependence in distribution models ([44, 45], among others). We controlled the false discovery rate (FDR) with the aim of avoiding the increase in type I errors arising from the number of variables used in the analyses [46]. An explanatory variable was selected only when it was significantly related to the target variable (P < 0.05) under a FDR of q < 0.05, with q being the false discovery rate. Then, we calculated Spearman correlation coefficients to control multicollinearity between the selected explanatory variables. Out of any group of explanatory variables whose pairwise correlation value was higher than 0.80, we retained the variable most significantly related with the distribution of the target variable. In this way we obtained a filtered set of potentially explanatory variables for each target variable.
Finally, we performed a forwardbackward stepwise logistic regression of the target variable on the polynomial combination of the spatial structure (ysp) and the filtered set of environmental variables, which produced increasingly more complex multivariate models while avoiding the inclusion of redundant variables. We used Akaike’s information criterion (AIC) to select the multivariate model that bestbalanced information and parsimony (AIC; [47]). All analyses mentioned so far were performed with the fuzzySim R package [38]. Then, we evaluated the relative weight of each variable included in the models through the Wald parameter [48] using the survey package [49, 50]. Variables with nonsignificant coefficients left in the model (Chisquare test, P < 0.05) were eliminated until we obtained a model with all the coefficients significantly different from zero according to Crawley’s [51] procedure.
Then we used the Favorability Function according to Real et al. [33] and Acevedo and Real [52].
where F is the environmental favorability (ranging between 0 and 1), P is the probability of occurrence obtained from the multivariate logistic regression performed for each target variable, n1 is the number of presences and n0 in the number of absences, in each case. This analysis was carried out with the fuzzySim R package [38].
Favorability values factor out the weight of the initial species presence/absences ratio, inherent to any probability function [33, 52] and, thus, depend exclusively on the effect of the environmental conditions of the territory on the distribution under analysis. In addition, local favorability reflects the degree of membership of the locality in the fuzzy set of areas favorable for the occurrence of the event, so allowing the comparison between models through fuzzy logic tools [36, 52].
In this way, we obtained favorability models for the occurrence of the two Aedes vectors (Ae. aegypti and Ae. albopictus) and of dengue in South America, FAe. aegypti, FAe. albopictus and Fdengue, respectively. We evaluated the discrimination and classification capacity of the models with the modEva R package [53]. The discrimination ability of the models was evaluated using the area under the curve (AUC) [54], and the classification capacity was estimated through the model sensitivity, specificity, kappa and correct classification rate (CCR), using the value of F = 0.5 as classification threshold. We checked the autocorrelation spatial using the Moran’s I spatial autocorrelation statistic from the residuals of the models [55].
According to the thresholds proposed by Muñoz and Real [56], we calculated the number of grid cells in each South American country classified as highly favorable (F ≥ 0.8), for which the favorability odds are more than 4:1 in favor, hereinafter at high risk, and of intermediate favorability (0.2 < F < 0.8), which odds are under 4:1 and above 1:4 in favor, hereinafter vulnerable, for Aedes vectors, for dengue cases, and for vectordengue cases simultaneously (see below).
Biogeographical vectordengue relationships and dengue risk maps
We used the fuzzy modelling approach to assess the vectordengue biogeographical interaction in South America. The logic underlying fuzzy sets was applied to the favorability function to indicate to what degree each grid cell belongs to the set of favorable areas for the presence of each species [52]. Then we used fuzzy logic tools to analyze the fuzzy vectordengue biogeographical interactions and to detect the territories at high risk or vulnerable to new dengue cases.
Based on the values of FAe. aegypti, FAe. albopictus and Fdengue models, we calculated the fuzzy intersection (minimum favorability value for two events at a given location) [30] to identify the fuzzy set of areas simultaneously favorable for dengue outbreaks and for any of the two species separately (i.e. FAe. aegypti ∩ Fdengue and FAe. albopictus ∩ Fdengue). Then, we analyzed how the favorability for each vector presence and for the occurrence of dengue cases changed along the gradient of favorability intersection (i.e. of shared favorability for both vector and disease). To this aim, we established 10 bins of equalrange FAe. aegypti ∩ Fdengue values and FAe. albopictus ∩ Fdengue values and calculated in each bin the average favorability values for the corresponding vector species and for dengue virus. If the vector species is a limiting factor in the distribution of the disease, then the favorability for dengue should be equal to or lower than that for the mosquito along the shared favorability range.
We also calculated to what extent the favorable areas for dengue (Fdengue) are contained in those for FAe. aegypti and for FAe. albopictus models, by applying the fuzzy inclusion equation [57]:
which indicates how much the set A is included in the set B. In this way, we calculated the inclusion of one into the other for the models FAe. aegypti, FAe. albopictus and Fdengue, and also for vectordengue intersections (i.e. FAe. aegypti ∩ Fdengue and FAe. albopictus ∩ Fdengue). Those fuzzy inclusion operations are defined in terms of the cardinal of each fuzzy set (i.e. the sum of the favorabilities values of all the grids). Thus, for example, the cardinal of FAe. aegypti ∩ Fdengue divided by the cardinal of Fdengue indicates the degree of inclusion of the distribution of dengue into that of Aedes aegypti.
To obtain the comprehensive biogeographical risk map for dengue in South America in the current context of vectordengue biogeographical relationship, we identified the fuzzy set of areas simultaneously favorable for dengue outbreaks and for any of the two vector species. To do this we first calculated the fuzzy union of the favorability for any vector species, FAe. aegypti ∪ FAe. albopictus (or maximum favorability value for any of them), which can identify the fuzzy set of areas favorable to either vector species [30]. Then, we calculated the fuzzy intersection between FAe. aegypti ∪ FAe. albopictus and Fdengue [(FAe. aegypti ∪ FAe. albopictus) ∩ Fdengue].
Results
Favorable conditions for vectors and dengue cases
The variables that were significantly associated with the occurrence of each vector species and with dengue cases are shown in Table 2 (Additional file 1: Table S1). All the factors explained to some extent the occurrence of both vectors and dengue, with the exception of the hydrology for Ae. albopictus and hydrology and land use for dengue.
The distribution of dengue was favored in territories of a certain elevation (435.06 m.a.s.l. on average), of predominant orientation towards the south, of high mean annual temperatures (23.13 °C on average), with low precipitation in the colder months (185.86 mm on average), few differences between maximum and minimum precipitations, high population density (221 inhabitants/km^{2} on average) and moderate distance to urban centers (7750 m on average).
The distribution of both Aedes vectors was favored by similar variables with similar effect (positive or negative), except for the land use factor. A high proportion of crops was favorable for Ae. aegypti while it was unfavorable for Ae. albopictus. According to the Wald test, in both Ae. aegypti and dengue models, the three most explanatory variables were the spatial structure, closeness to urban centers and mean annual temperature (Table 2). The spatial structure, NorthSouth orientation and proximity to urban centers were the three most explanatory variables for Ae. albopictus.
In Fig. 2 we show the cartographic favorability models for the vector species and for dengue (FAe. aegypti, FAe. albopictus and Fdengue) separately, with values grouped in three favorability classes: F values lower than 0.2 indicate low favorability, values between 0.2 and 0.8 indicate vulnerable areas, and values higher than 0.8 indicate areas at high risk [58]. The FAe. aegypti model depicted a large principal nucleus of high risk in Brazil, and some dispersed highrisk cells in Venezuela, Colombia, Peru, Paraguay, Argentina and Uruguay. The FAe. albopictus map revealed a main highrisk nucleus in Brazil, and some dispersed highrisk cells in Colombia and Peru. The Fdengue model detected two main nuclei of high risk for the occurrence of dengue cases, one in Brazil and another, more dispersed, in Venezuela, Colombia and Ecuador. Some dispersed highrisk cells are also found in Peru, Guyana, Surinam, Paraguay and Uruguay. The Fdengue model detected at least one vulnerable grid cell (0.2 < F < 0.8) in every South American country. Although only 11.37% (731 squares) of the total analyzed squares (n = 6430) have recorded dengue cases, 60.14% (3867) of the squares showed at least vulnerable conditions (F > 0.2) according the Fdengue model, while 8.94% of the cells (575) were at high risk (F ≥ 0.8).
Both Aedes and dengue favorability models attained general acceptable scores according to the parameters considered to assess discrimination and classification capacities (Table 3). Discrimination (AUC) was always higher than 0.86, which is “excellent” according to Hosmer and Lemeshow [59]. Sensitivity values were always higher than 0.79, specificity was always higher than 0.74 and CCR was higher than 0.75. Kappa was higher than 0.6 for both Aedes vectors, which is “substantial” according to Landis and Kock [60]; it was 0.31, which is “fair”, for dengue cases. On the other hand, according to the analysis of residuals, we detected a minor autocorrelation (Moran’s I < 0.019), or approximately zero, below 1600 km and only in the Ae. albopictus model. These results indicate that there is no relevant spatial autocorrelation resulting from sampling bias with the grid system employed [55]. None of the Moran’s Ivalues were significant in the Ae. aegypti or the dengue models. The residuals did not show problems of spatial autocorrelation in our models and therefore we did not find relevant effects of spatial autocorrelation that invalidate our results.
Vectordengue biogeographical interactions
Compared to the Fdengue model, both vectordisease intersections improved classification capacity according to kappa, CCR and specificity, whereas sensitivity and discrimination capacity decreased (Table 3).
In Fig. 3 we show the fuzzy intersection between the favorability for dengue and vector species for both Ae. aegypti and Ae. albopictus in South America. Aedes aegypti and dengue favorability values increased together until a fuzzy intersection of 0.5 was reached; then, both continued to increase with higher favorability values for the mosquito (Fig. 3a). The intersection between Ae. albopictus and dengue favorability values indicated that dengue cases had higher favorability values than the mosquito up to fuzzy intersection = 0.3; after that point, the vector showed higher favorability values than the disease.
According to the intersection between FAe. aegypti and Fdengue (Table 4), nine of the 14 South American countries have more than 50% of the country surface area at least vulnerable (F > 0.2) to denguecases occurrence transmitted by Ae. aegypti. In contrast, only Brazil has more than 50% of the country at least vulnerable (F > 0.2) to denguecases occurrence transmitted by Ae. albopictus. Seven South American countries have some locations at high risk (F ≥ 0.8) based on the intersection between FAe. aegypti and Fdengue. Two countries, Brazil and Peru, have locations at high risk of dengue occurrence due to Ae. albopictus, based on the intersection between the FAe. albopictus and Fdengue models (Table 4).
Fuzzyinclusion relationships between models
In Fig. 2 we show the values for FAe. aegypti, FAe. albopictus and Fdengue inclusion into one another and in Fig. 3 the values of the inclusion of the two vectordengue intersections one into the other. The main results were that Fdengue was included in a higher proportion into FAe. aegypti (0.75) than into FAe. albopictus (0.55), and that the intersection FAe. albopictus ∩ Fdengue was more included into the FAe. aegypti ∩ Fdengue (0.99) model than vice versa (0.69).
Dengue risk map in the current biogeographical context of vectordengue interaction
In Fig. 4, we show the comprehensive map of areas at high risk and vulnerable to dengue cases due to the two vector species combined, resulting from Fdengue ∩ (FAe. aegypti ∪ FAe. albopictus).
Discussion
Environmental drivers of Aedes vectors and dengue cases in South America
Many studies have explained the occurrence of both Aedes species in South America in terms of only climate [10, 25], climate and some land cover variables [7], or climate and economic information [4]. In general, they detected that temperature was the main factor limiting the distribution of the two Aedes species. In contrast, our favorability models detected a more complex pattern of drivers for the presence of these vectors (Table 2). Spatial structure and closeness to urban centers were among the most relevant variables for both Aedes species, while mean annual temperature was more important for Ae. aegypti and topography was more relevant for Ae. albopictus.
The most important drivers of dengue cases, according to our model, are the same as those favoring Ae. aegypti, including temperature (Table 2), which coincides with the conclusion of Capinha et al. [61]. As Campbell et al. [10] suspected, requirements for the presence of Ae. aegypti in South of America better reflect the risky environmental conditions for dengue occurrence than those for Ae. albopictus. Our results also concur with what Messina et al. [62] and Brady et al. [25] suggested, that the distribution of dengue occurrences is better modelled by incorporating drivers of different nature, such as climate, topography and human activities.
Distribution of favourable areas
Although our explanatory models were more complex than those previously described, we detected favorable regions for both Aedes species coarsely similar to those described by other authors [7, 10, 25]. Areas highly favorable for Ae. albopictus were mostly located in Brazil and Paraguay (Fig. 2). Highfavorability territories for Ae. aegypti were more concentrated in eastern South America, in Brazil, and some highfavorability territories reached further south than those indicated by Kraemer et al. [7], particularly in Uruguay (Fig. 2).
The dengue favorability model got lower discrimination and classification scores than the vector models (Table 3). This may result from the fact that, as other authors have pointed out [63, 64], accuracy of distribution models gets worse when a distribution is more poorly known. The published distribution data of this disease shows a scattered pattern that point to some possible bias in the quality of denguevirus infection reports, despite the effort of Messina et al. [14]. Nevertheless, Bhatt et al. [24], by using descriptors based on climate, vegetation and human variables, described a pattern of dengue risk in South America similar to our Fdengue model (Fig. 2). However, they did not define risk areas in southern countries such as Chile and Uruguay, while we obtained areas vulnerable or at high risk in these countries. These areas represent a risk for dengue that was hidden up to now (Fig. 2). In the case of Chile, the vulnerable zones are restricted to a few lowaltitude grids that were also favorable for Ae. aegypti. It should be noted that we found areas at high risk and vulnerable in many squares neighboring those with reported cases. This suggests that, although these areas are apparently denguefree, they are in fact at high risk, and extreme precautions and management, control and prevention plans should be applied there.
The greatest risk for the disease in South America may be considered to occur in areas favourable to dengue (Fdengue) with reported presence of vectors Aedes aegypti and/or Ae. albopictus (Figs. 2, 4): much of Brazil and scattered regions of Venezuela, Colombia, Peru and Paraguay for Ae. aegypti; and much of Brazil for Ae. albopictus. The case of Uruguay is particularly interesting. In this country, the Fdengue model detected vulnerable locations in areas where no dengue cases had been reported for a century [11]. Uruguay was classified by Brady et al. [65] as with complete or good evidence consensus on dengue absence. However, in the summer of 2016, about 20 cases of indigenous dengue occurred in Montevideo city [11], specifically where our Fdengue model indicated a high risk of dengue occurrence (Fig. 4). Taking into account that these cases have not been considered as presences for model training in this work, this supports the predictive capacity of our model. According to Real et al. [66], the favorability function may be considered to be, for the distribution of species, analogous to what the wave function is for the distribution of quantum particles, a mathematical conceptualization of the forces that are behind the distribution of the species. This being the case, favorability values could be a better representation of the distribution of a species than the dataset of specific observations, which are always incomplete and contingent on the observation effort.
The biogeographical context of dengue risk
In agreement with the proposal of Messina et al. [62], our approach of biogeographical modelling and fuzzy logic applied to the interaction with Aedes vectors has proved to be a useful method for unravelling the biogeographical context of dengue cases. The maximum simultaneous vectordengue favorability occurs in much of Brazil for both species (Fig. 3). Additionally, some scattered areas of Colombia, Venezuela, Paraguay, Peru and Uruguay are simultaneously favorable for Aedes aegypti and dengue occurrence, all of them areas where disease cases have been recorded.
The Fdengue model was more included into the FAe. aegypti model than into FAe. albopictus (Fig. 2), which indicates that the favorability for Ae. aegypti explained to a higher extent than that for Ae. albopictus the dengue cases in South America. Coinciding with Campbell et al. [10], these results indicate that the distribution of Ae. aegypti mostly encompasses the biogeographical framework of dengue in South America, which also suggests that this species is the principal vector responsible for the dengue cases in the continent. Some authors already reported that the increase in the cases of dengue in Brazil and Argentina, for example, was directly linked to the expansion of Ae. aegypti [67,68,69]. Brathwaite et al. [13] also found a relationship between an increase in dispersion of Ae. aegypti between 2001 and 2010 in America and a corresponding increase in dengue virus circulation. In our analyses, compared to the model built on dengue cases alone, the model based on the intersection between dengue and Ae. aegypti included 26% more nocaserecord locations within areas of low risk (F ≤ 0.2), i.e. had a higher specificity (Table 3). This corroborates that incorporating vector information in the biogeographical analysis of disease drivers provides a more plausible explanation about the pattern of cases occurrence [28, 29], which was previously suggested specifically for dengue as well [10, 62].
Although both mosquito species are known to act as vectors of dengue, 99% of the FAe. albopictus ∩ Fdengue model was included in the FAe. aegypti ∩ Fdengue model (Fig. 3). In addition, while the favorability for Ae. aegypti seems to effectively limit that for dengue (Fig. 3a), this does not happen with the favorability for Ae. albopictus, particularly when the favorability for the mosquito is 0.3 or lower (Fig. 3b). Consequently, in South America, in order to manage the epidemiological risk of new dengue cases, the intersection between Ae. aegypti and dengue favorability should be used as a the most parsimonious map of dengue risk. Nevertheless, a few territories at risk of dengue were attributed exclusively to the FAe. albopictus model. Therefore, the most appropriate risk map should include the interaction of all vectors and cases of dengue, which can be readily obtained using fuzzy logic. The fuzzy intersection between the favorability for dengue cases and the fuzzy union of the favorability for any of the vector species provided a comprehensive map of the biogeographical risk for dengue (Fig. 4). This proposal of a risk map for dengue in South America is based on the geographicalenvironmental, diseasetrait and human profiling that constitute the starting point for risk assessments in pathogeography [29].
Conclusions
Our results corroborate that incorporating vector information in the biogeographical analysis of disease drivers provides a more plausible explanation about the pattern of cases occurrence, and confirm that fuzzy logic is an appropriate conceptual and operational tool to deal with the nuances of the vectorillness biogeographical interactions. Thus, the application of fuzzy logic may help health authorities to better prevent, control and mitigate vectorborne diseases.
Availability of data and materials
We have included the data table used as Additional file 1: Table S1.
Abbreviations
 SDM:

species distribution models
 AIC:

Akaike’s information criterion
 F:

environmental favorability
 AUC:

area under the curve
 CCR:

correct classification rate
References
 1.
Gubler DJ. Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends Microbiol. 2002;10:100–3.
 2.
Gubler DJ. The changing epidemiology of yellow fever and dengue, 1900 to 2003: full circle? Comp Immunol Microbiol Infect Dis. 2004;27:319–30.
 3.
Delatte H, Gimonneau G, Triboire A, Fontenille D. Influence of temperature on immature development, survival, longevity, fecundity, and gonotrophic cycles of Aedes albopictus, vector of chikungunya and dengue in the Indian Ocean. J Med Entomol. 2009;46:33–41.
 4.
Aström C, Rocklöv J, Hales S, Béguin A, Louis V, Sauerborn R. Potential distribution of dengue fever under scenarios of climate change and economic development. EcoHealth. 2013;9:448–54.
 5.
Messina JP, Brady OJ, Pigott DM, Brownstein JS, Hoen AG, Hay SI. Global dengue occurrence database: 1960–2012. 2014. https://doi.org/10.6084/m9.figshare.902845. Accessed 1 Mar 2018.
 6.
Morin CW, Comrie AC, Ernst K. Climate and dengue transmission: evidence and implications. Environ Health Perspect. 2013;121:1264–72.
 7.
Kraemer MU, Sinka ME, Duda KA, Mylne AQ, Shearer FM, Barker CM, et al. The global distribution of the arbovirus vectors Aedes aegypti and A. albopictus. eLife. 2015;4:e08347.
 8.
Kraemer MUG, Sinka ME, Duda KA, Mylne A, Shearer FM, Barker CM, et al. The global compendium of Aedes aegypti and Ae. albopictus occurrence. Sci Data. 2015;2:150035.
 9.
Zeller H, Van Bortel W, Sudre B. Chikungunya: its history in Africa and Asia and its spread to new regions in 2013–2014. J Infect Dis. 2016;214:436–40.
 10.
Campbell LP, Luther C, MooLlanes D, Ramsey JM, DanisLozano R, Peterson AT. Climate change influences on global distributions of dengue and chikungunya virus vector. Philos Trans R Soc London B Biol Sci. 2015;370:20140135.
 11.
Smink V. Cómo llegó el dengue a Uruguay después de 100 años sin el virus. 2016. https://www.bbc.com/mundo/noticias/2016/02/160225_uruguay_dengue_brote_vs. Accessed 1 June 2016.
 12.
Gubler DJ, Vasilakis N, Musso D. History and emergence of Zika virus. J Infect Dis. 2017;216:860–7.
 13.
Brathwaite DO, San Martín JL, Montoya RH, del Diego J, Zambrano B, Dayan GH. Review: the history of dengue outbreaks in the Americas. Am J Trop Med Hyg. 2012;87:584–93.
 14.
Messina JP, Bradu OJ, Scott TW, Zou C, Pigott DM, Duda KA, et al. Global spread of dengue virus types: mapping the 70 year history. Trends Microbiol. 2014;22:138–46.
 15.
Powell JR, Tabachnick WJ. Genetics and the origin of a vector population: Aedes aegypti, a case study. Science. 1980;208:1385–7.
 16.
Braks MAH, Honório N, Lounibos L, LourençodeOliveira R, Juliano S. Interspecific competition between two invasive species of container mosquitoes, Aedes aegypti and Aedes albopictus (Diptera: Culicidae), in Brazil. Ann Entomol Soc Am. 2004;97:130–9.
 17.
Ferguson NM, Kien DT, Clapham H, Aguas R, Trung VT, Chau TN, et al. Modeling the impact on virus transmission of Wolbachiamediated blocking of dengue virus infection of Aedes aegypti. Sci Transl Med. 2015;7:279ra37.
 18.
Brady OJ, Johansson MA, Guerra CA, Bhatt S, Golding N, Pigott DM, et al. Modelling adult Aedes aegypti and Aedes albopictus survival at different temperatures in laboratory and field settings. Parasites Vectors. 2013;6:351.
 19.
Gama ZP, Nakagoshi N, Islamiyah M. Distribution patterns and relationship between elevation and the abundance of Aedes aegypti in Mojokerto city 2012. Open J Anim Sci. 2013;3:11–6.
 20.
Goindin D, Delannay C, Ramdini C, Gustave J, Fouque F. Parity and longevity of Aedes aegypti according to temperatures in controlled conditions and consequences on dengue transmission risks. PLoS ONE. 2015;10:e0135489.
 21.
Rašić G, EndersbyHarshman N, Tantowijoyo W, Goundar A, White V, Yang Q, et al. Aedes aegypti has spatially structured and seasonally stable populations in Yogyakarta, Indonesia. Parasites Vectors. 2015;8:610.
 22.
Brown JE, Evans BR, Zheng W, Obas V, BarreraMartinez L, Egizi A, et al. Human impacts have shaped historical and recent evolution in Aedes aegypti, the dengue and yellow fever mosquito. Evolution. 2014;68:14–25.
 23.
Chatterjee S, Chakraborty A, Sinha SK. Spatial distribution and physicochemical characterization of the breeding habitats of Aedes aegypti in and around Kolkata, West Bengal, India. Indian J Med Res. 2015;142:79–86.
 24.
Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature. 2013;496:504–7.
 25.
Brady OJ, Golding N, Pigott DM, Kraemer MU, Messina JP, Reiner RC, et al. Global temperature constraints on Aedes aegypti and Ae. albopictus persistence and competence for dengue virus transmission. Parasites Vectors. 2014;7:338.
 26.
Cliff AD, Haggett P. The epidemiological significance of islands. Health Place. 1995;1:199–209.
 27.
Smith KF, Guégan JF. Changing geographic distributions of human pathogens. Annu Rev Ecol Evol Syst. 2010;41:231–50.
 28.
Olivero J, Fa JE, Real R, Farfán MA, Márquez AL, Vargas JM, et al. Mammalian biogeography and the Ebola virus in Africa. Mammal Rev. 2017;47:24–37.
 29.
Murray KA, Olivero J, Roche B, Tiedt S, Guégan JF. Pathogeography: leveraging the biogeography of human infectious diseases for global health management. Ecography. 2018;41:1411–27.
 30.
Zadeh LA. Fuzzy sets. Inform Control. 1965;8:338–53.
 31.
Salski A, Kandzia P. Fuzzy sets and fuzzy logic in ecological modelling. EcoSys. 1996;4:85–97.
 32.
Salski A. Ecological applications of fuzzy logic. In: Recknagel F, editor. Ecological informatics: scope, techniques and applications. Berlin: Springer; 2006. p. 3–14.
 33.
Real R, Barbosa AM, Vargas JM. Obtaining environmental favourability functions from logistic regression. Environ Ecol Stat. 2006;13:237–45.
 34.
Acevedo P, Ward AI, Real R, Smith GC. Assessing biogeographical relationships of ecologically related species using favourability functions: a case study on British deer. Divers Distrib. 2010;16:515–28.
 35.
Romero D, Báez JC, FerriYáñez F, Bellido JJ, Real R. Modelling favourability for invasive species encroachment to identify areas of native species vulnerability. Sci World J. 2014;2014:519710.
 36.
Romero D, Olivero J, Brito JC, Real R. Comparison of approaches to combine species distribution models based on different sets of predictors. Ecography. 2016;39:561–71.
 37.
Romo H, GarcíaBarros E, Márquez AL, Moreno JC, Real R. Effects of climate change on the distribution of ecologically interacting species: butterflies and their main food plants in Spain. Ecography. 2014;37:1063–72.
 38.
Barbosa AM. fuzzySim: applying fuzzy logic to binary similarity indices in ecology. Methods Ecol Evol. 2015;6:853–8.
 39.
Willis KJ, Whittaker RJ. Species diversity—scale matters. Science. 2002;295:1245–8.
 40.
Pearson RG, Dawson TP. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob Ecol Biogeogr. 2003;12:361–71.
 41.
Legendre P, Legendre L. Numerical ecology. 2nd ed. Amsterdam: Elsevier Science; 1998.
 42.
Legendre P. Spatial autocorrelation: trouble or new paradigm? Ecology. 1993;74:1659–73.
 43.
Miller J, Franklin J, Aspinall R. Incorporating spatial dependence in predictive vegetation models. Ecol Model. 2007;202:225–42.
 44.
Lobo JM, Lumaret JP, JayRobert P. Modelling the species richness distribution of French dung beetles (Coleoptera, Scarabaeidae) and delimiting the predictive capacity of different groups of explanatory variables. Glob Ecol Biogeogr. 2002;11:265–77.
 45.
NoguésBravo D, MartinezRica JP. Factors controlling the spatial species richness pattern of four groups of terrestrial vertebrates in an area between two different biogeographic regions in northern Spain. J Biogeogr. 2004;31:629–40.
 46.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc B. 1995;57:289–300.
 47.
Akaike H. Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csaki F, editors. Proceedings of the second international symposium on information theory. Budapest, Hungary: Akademiai Kiado; 1973. p. 267–81.
 48.
Wald A. Tests of statistical hypotheses concerning several parameters with applications to problems of estimation. Trans Am Math Soc. 1943;54:426–82.
 49.
Lumley T. Analysis of complex survey samples. J Stat Softw. 2004;9:1–19.
 50.
Lumley T. Survey: analysis of complex survey samples. R package version 3.35. 2018.
 51.
Crawley MJ. The R book. Chichester: Wiley; 2007.
 52.
Acevedo P, Real R. Favourability: concept, distinctive characteristics and potential usefulness. Naturwissenschaften. 2012;99:515–22.
 53.
Barbosa AM, Brown JA, JimenezValverde A, Real R. modEvA: model evaluation and analysis. R package version 1.3.2. 2016. https://CRAN.Rproject.org/package=modEvA.
 54.
Lobo J, JiménezValverde A, Real R. AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr. 2008;17:145–51.
 55.
Cliff AD, Ord JK. Spatial processes: models and applications. London: Pion; 1981. p. 2–10.
 56.
Muñoz AR, Real R. Assessing the potential range expansion of the exotic monk parakeet in Spain. Divers Distrib. 2006;12:656–65.
 57.
Kunchenva LI. Using measures of similarity and inclusion for multiple classifier fusion by decision templates. Fuzzy Set Syst. 2001;122:401–7.
 58.
Muñoz AR, Real R, Barbosa AM, Vargas JM. Modelling the distribution of Bonelli’s eagle in Spain: implications for conservation planning. Divers Distrib. 2005;11:477–86.
 59.
Hosmer DW, Lemeshow S. Applied logistic regression. 2nd ed. New York: Wiley; 2000.
 60.
Landis JR, Koch GC. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159–74.
 61.
Capinha C, Rocha J, Sousa CA. Macroclimate determines the global range limit of Aedes aegypti. EcoHealth. 2014;11:420–8.
 62.
Messina JP, Brady OJ, Pigott DM, Golding N, Kraemer MUG, Scott TW, et al. The many projected futures of dengue. Nat Rev Microbiol. 2015;13:230–9.
 63.
Stockwell DRB, Peterson AT. Effects of sample size on accuracy of species distribution models. Ecol Model. 2002;148:1–13.
 64.
Gurutzeta GA. Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities. Ecography. 2017;40:281–95.
 65.
Brady OJ, Gething PW, Bhatt S, Messina JP, Brownstein JS, Hoen AG, et al. Refining the global spatial limits of dengue virus transmission by evidencebased consensus. PLoS Negl Trop Dis. 2012;6:e1760.
 66.
Real R, Barbosa AM, Bull JW. Species distributions, quantum theory, and the enhancement of biodiversity measures. Syst Biol. 2017;66:453–62.
 67.
Vezzani D, Carbajo AE. Aedes aegypti, Aedes albopictus, and dengue in Argentina: current knowledge and future directions. Mem Inst Oswaldo Cruz. 2008;103:66–74.
 68.
Serpa LLN, Marques GRAM, de Lima AP, Voltolini JC, de Brito Arduino M, Barbosa GL, et al. Study of the distribution and abundance of the eggs of Aedes aegypti and Aedes albopictus according to the habitat and meteorological variables, municipality of São Sebastião, São Paulo State, Brazil. Parasites Vectors. 2013;6:321.
 69.
da Rocha Taranto MF, Pessanha JE, dos Santos M, dos Santos Pereira Andrade AC, Camargos VN, Alves SN, et al. Dengue outbreaks in Divinopolis, southeastern Brazil and the geographic and climatic distribution of Aedes albopictus and Aedes aegypti in 2011–2012. Trop Med Int Health. 2015;20:77–88.
Acknowledgements
Not applicable.
Funding
DR was supported by a grant from the Agencia Nacional de Investigación e Innovación de Uruguay (ANII) (2016–2018), and from the Graduate Academic Commission (CAP, from Spanish acronym Comisión Académica de Posgrado) of the Universidad de la República (2018–2020). This study was supported by the project CGL201676747R (Ministerio de Economía y Competitividad, España, y el Fondo Europeo de Desarrollo Regional, Unión Europea).
Author information
Affiliations
Contributions
DR processed and analyzed part of the data, participated in and designed the field work and wrote the first draft. JO worked on the discussion section and made revisions to the manuscript. RR participated in the design of the field work and revised the last version of the manuscript. JCG elaborated the database, analyzed part of the data and defined the study aims. All authors read and approved the final manuscript.
Corresponding author
Correspondence to David Romero.
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Additional file 1: Table S1. Dengue cases, Aedes aegypti and Ae. albopictus occurrences (presences as 1, and absences as 0 in each South America grid), values of the significant variables in the models by South America grid, and favorability values for each model by grid (Fdengue; FAe. aegypti and FAe. albopictus). Variable abbreviations are given in Table 1.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Cite this article
Romero, D., Olivero, J., Real, R. et al. Applying fuzzy logic to assess the biogeographical risk of dengue in South America. Parasites Vectors 12, 428 (2019) doi:10.1186/s1307101936915
Received:
Accepted:
Published:
Keywords
 Aedes aegypti
 Ae. albopictus
 Favorability function
 Fuzzy operators
 Vectorillness interaction
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Please note that comments may be removed without notice if they are flagged by another user or do not comply with our community guidelines.