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Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010
© Stewart-Ibarra et al.; licensee BioMed Central Ltd. 2014
Received: 12 June 2014
Accepted: 4 November 2014
Published: 25 November 2014
Dengue fever, a mosquito-borne viral disease, is a rapidly emerging public health problem in Ecuador and throughout the tropics. However, we have a limited understanding of the disease transmission dynamics in these regions. Previous studies in southern coastal Ecuador have demonstrated the potential to develop a dengue early warning system (EWS) that incorporates climate and non-climate information. The objective of this study was to characterize the spatiotemporal dynamics and climatic and social-ecological risk factors associated with the largest dengue epidemic to date in Machala, Ecuador, to inform the development of a dengue EWS.
The following data from Machala were included in analyses: neighborhood-level georeferenced dengue cases, national census data, and entomological surveillance data from 2010; and time series of weekly dengue cases (aggregated to the city-level) and meteorological data from 2003 to 2012. We applied LISA and Moran’s I to analyze the spatial distribution of the 2010 dengue cases, and developed multivariate logistic regression models through a multi-model selection process to identify census variables and entomological covariates associated with the presence of dengue at the neighborhood level. Using data aggregated at the city-level, we conducted a time-series (wavelet) analysis of weekly climate and dengue incidence (2003-2012) to identify significant time periods (e.g., annual, biannual) when climate co-varied with dengue, and to describe the climate conditions associated with the 2010 outbreak.
We found significant hotspots of dengue transmission near the center of Machala. The best-fit model to predict the presence of dengue included older age and female gender of the head of the household, greater access to piped water in the home, poor housing condition, and less distance to the central hospital. Wavelet analyses revealed that dengue transmission co-varied with rainfall and minimum temperature at annual and biannual cycles, and we found that anomalously high rainfall and temperatures were associated with the 2010 outbreak.
Our findings highlight the importance of geospatial information in dengue surveillance and the potential to develop a climate-driven spatiotemporal prediction model to inform disease prevention and control interventions. This study provides an operational methodological framework that can be applied to understand the drivers of local dengue risk.
KeywordsDengue fever Aedes aegypti GIS Social-ecological Climate Spatial Temporal Wavelet analysis Ecuador Early warning system
Dengue fever is the most significant mosquito-borne viral disease globally, and has rapidly increased in incidence, geographic distribution, and severity in recent decades -. The disease is caused by four distinct dengue virus serotypes (DENV 1-4) that are transmitted primarily by the female Aedes aegypti mosquito, with Aedes albopictus as a secondary vector. Common disease manifestations range from asymptomatic to moderate febrile illness, with a smaller proportion of patients who progress to severe illness characterized by hemorrhage, shock and death . Integrated vector control and surveillance remain the principle strategies for disease prevention and control in endemic regions, as no vaccine or specific medical treatment are yet available. Macro social and environmental drivers have facilitated the global spread and persistence of dengue, including growing vulnerable urban populations, global trade and travel, climate variability, and inadequate vector control -. However, we have a limited understanding of the relative effects of these drivers at the local level, restricting our ability to predict and respond to site-specific dengue outbreaks.
Early warning systems (EWS) for dengue and other climate-sensitive diseases are decision-support tools that are being developed to improve the ability of the public health sector to predict, prevent, and respond to local disease outbreaks ,. An EWS incorporates environmental data (e.g., climate, altitude, sea surface temperature), epidemiological surveillance data, and other social-ecological data in a spatiotemporal prediction model that generates operational disease risk forecasts, such as seasonal risk maps. Previous studies have demonstrated the utility of this approach for vector-borne diseases, including for dengue -, malaria - and rift valley fever . Maps and other model outputs are linked to an epidemic alert and response systems, triggering a chain of preventive interventions when an alert threshold is reached.
One of the first steps in developing an EWS is to characterize the spatiotemporal dynamics and the covariates associated with historical disease transmission. This is often done by developing GIS base maps of epidemiological, environmental, and social data to identify risk factors; and through time series analyses of epidemiological and climate data. These analyses require cross-institutional integration of expertise and data, including epidemiological and entomological data from ministries of health, climate information from national institutes of meteorology, and social-ecological spatial data from national census bureaus. Previous studies indicate that associations among climate, socioeconomic indicators and dengue risk vary by location and time, indicating the need for analyses of dengue risk that consider the local context to explain transmission mechanisms -. Importantly, these analyses also need to consider the spatial and temporal scales of ongoing data collection and surveillance activities to ensure that the model outputs can support an operational EWS.
The National Institute of Meteorology and Hydrology (INAMHI) of Ecuador is coordinating efforts with the Ministry of Health (Ministerio de Salud Pública – MSP) to develop an operational dengue EWS for coastal regions of Ecuador, where the disease is hyper-endemic . Our previous studies in southern coastal Ecuador demonstrated the potential to develop a dengue EWS that incorporates climate and non-climate information. We found that the magnitude and timing of dengue outbreaks were associated with anomalies in local climate, the El Niño Southern Oscillation (ENSO), the virus serotypes in circulation, and vector abundance . Local field studies showed that dengue risk also depended on household risk factors (e.g., access to piped water infrastructure, demographics, water storage behaviors, housing conditions) . Our recent advances in seasonal climate forecasts indicate that the forecasts in this region have considerable skill (i.e., predictive ability) ,. Building on these previous studies, this study was conducted to characterize the spatiotemporal dynamics, climatic and social-ecological risk factors associated with the largest dengue epidemic (2010) on record in the coastal city of Machala, Ecuador, an important site for dengue surveillance in the region.
Machala, El Oro Province, is a mid-sized coastal port city (pop. 241,606)  located in southern coastal Ecuador, 70 kilometers north of the Peruvian border and 186 kilometers south of the city of Guayaquil (the epicenter of historical dengue outbreaks in the region). Dengue is an emerging disease in this region, with the first cases of dengue hemorrhagic fever (DHF) reported in 2005. The disease is now hyper-endemic, with year round transmission and co-circulation of all four serotypes. Recent multi-country studies showed that Machala had the highest Ae. aegypti larval indices of ten sites in other countries in Latin America and Asia , Given the high burden of disease, the high volume of people and goods moving across the Ecuador-Peru border, and proximity to Guayaquil, Machala is a strategic location to monitor and understand dengue transmission dynamics.
The following data from Machala were included in analyses: neighborhood-level georeferenced dengue cases, national census data, and entomological surveillance data from 2010; and time series of weekly dengue cases (aggregated to the city-level) and meteorological data from 2003 to 2012. These data were examined to identify potential social-ecological and climate variables associated with the presence of dengue fever during the 2010 outbreak in Machala, Ecuador. Epidemiological data were provided by INAMHI through a collaborative project with the MSP that was sponsored by the Ecuadorian government. Accordingly, no formal ethical review was required, as the data used in this analysis were de-identified and aggregated to the neighborhood- and city-level, as described below.
INAMHI provided a map of georeferenced dengue cases from Machala in 2010, de-identified and aggregated to neighborhood-level polygons (n = 253) to protect the identity of individuals . This map was generated from individual records of clinically suspected cases of dengue fever and DHF (aggregated as total dengue fever) reported to a mandatory MSP disease surveillance system, and the map included 83% of all dengue cases (n = 1,674) reported in 2010. Reported dengue cases were defined based on a clinical diagnosis. INAMHI also provided data for weekly dengue cases from Machala from 2003 to 2012 for the wavelet analysis described below.
Social-ecological risk factors
Social-ecological parameters (mean and standard deviation - SD) tested in logistic regression models to predict dengue presence (1) and absence (0) at the neighborhood level in Machala in 2010
More than four people per bedroom (% households)
Population density (people per square kilometer)
More than one other household sharing the home (% households)
People per household
Receive remittances (% households)
People emigrate for work (% households)
Mean age of the head of the household (years)
Head of the household has primary education or less (% households)
Afro-Ecuadorian (% population)
Head of the household is unemployed (% households)
Head of household is a woman (% households)
Housing condition index (HCI), 0 to 1, where 1 is poor condition
No access to municipal garbage collection (% households)
No piped water inside the home (% households)
No access to sewerage (% households)
No access to paved roads (% households)
People drink tap water (% households)
Rental homes (% households)
Average distance to the central hospital (km)
Average Breteau Index during the first two quarters of 2010
Using individual and household census records, we recoded selected census variables and calculated parameters as the percent of households or percent of the population per census sector (n = 558 census sectors). The data element dictionary of recoded variables in Spanish is presented in Additional file 1: Table S1. To scale the sector-level polygon data to neighborhood-level polygons, we used the `isectpolypoly’ tool in Geospatial Modeling Environment ,. We estimated neighborhood population by calculating the area-weighted sum, and estimated all other parameters by calculating area-weighted means. The neighborhood population estimates were also used to calculate neighborhood dengue prevalence and population density parameters.
Vector surveillance data for Ae. aegypti from 2010 was obtained from the National Service for the Control of Vector-Borne Diseases of the MSP, and included quarterly House Indices (percent of households with Ae. aegypti juveniles) and Breteau Indices (number of containers with Ae. aegypti juveniles per 100 households). The average Breteau Index during the first two quarters of 2010 (January to June), was the vector index that was most strongly associated with dengue presence (1) or absence (0) (Pearson correlation, r = 0.2, p = 0.001) and this period corresponded with the peak of the epidemic; accordingly, we selected this variable to test in the multivariate model (Table 1).
Daily meteorological data (rainfall and minimum air temperature) during the study period were provided by the Granja Santa Ines weather station located in Machala (3°17'16” S, 79°54'5” W, 5 meters above sea level) and operated by INAMHI. The weekly climatology (1985-2013) and weather during the study period are shown in Figure 1B. Weekly average rainfall and minimum temperature from 2003 to 2012 were included in the wavelet analysis, since it has been shown that these two climate variables explain an important part of the total variance of dengue cases in coastal Ecuador .
Exploratory spatial analysis
We applied Moran’s I with inverse distance weighting (ArcMap 10.1) to epidemiological dengue data from 2010 to test the hypothesis that dengue cases were randomly distributed in space. Moran’s I is a global measure of spatial autocorrelation, that provides an index of dispersion from -1 to +1, where -1 is dispersed, 0 is random, and +1 is clustered. We identified the locations of significant dengue hot and cold spots using Anselin Local Moran’s I (LISA) with inverse distance weighting (ArcMap 10.1). The LISA is a local measure of spatial autocorrelation  that identifies significant clusters (hot or cold spots) and outliers (e.g., nonrandom groups of neighborhoods with above or below the expected dengue prevalence). Previous studies have used Moran’s I and LISA to test the spatial distribution of dengue transmission , including in Ecuador , allowing for comparison between studies.
Social-ecological risk factors
Census data aggregated to the neighborhood-level were examined to identify potential social-ecological variables associated with the presence of dengue fever, including population density, human demographic characteristics, and housing condition (Table 1). We hypothesized that the presence or absence of dengue was associated with one or more of these factors; each factor was presented as a suite of census variables, representing testable variable ensemble hypotheses in a model selection framework - a modeling strategy that has been previously described . Variables for the average distance to the public hospital (Teofilo Davila Hospital, the provincial hospital located in the city center) and the Breteau Index were also tested in the model to assess geographic differences, potential underreporting, and other factors (e.g., microclimate, vector control) not captured by the census variables.
Where k is the number of parameters in the model, n is the sample size, and L is the maximized likelihood function for the model.
The parameters included in the best-fit logistic regression models to predict the presence (1) or absence (0) of dengue in neighborhoods in Machala in 2010
0.46 – 1.05
Head of household is a woman
0.73 – 15.17
Age of head of household
0.00 – 0.21
Residual of HCI regressed on households with no access to piped water inside the home
3.98 – 14.37
Distance to central hospital
−0.0007 – 0.0
−14.24 – −1.32
Head of household is a woman
0.11 – 14.83
Age of head of household
0.002 – 0.26
No piped water inside the home
−6.08 – −0.4
4.06 – 14.56
Distance to central hospital
−0.001 – 0.0
The pre-processing of the time series data for Machala followed a two-step methodology described elsewhere in detail ,,. First, we quality-controlled the time series using a standard R package  to identify outliers and inconsistent values (e.g., minimum temperatures > maximum temperatures, negative precipitation values or negative frequency of dengue cases). Outliers were defined as data points at least three standard deviations above or below the mean. To account for real outliers (e.g., not artifacts produced by human, instruments, or transmission errors), we compared suspicious values with data from nearby climate stations. Entries that we deemed to be uncorrectable were flagged as missing values. Then we used the R package `RHTestsV4’ - to detect and correct temporal inhomogeneities in these variables. The climate time series did not need substantial corrections. Weekly dengue case data were transformed to weekly incidence using a linear interpolation of local population data from the 2001 and 2010 national censuses. The final step in data pre-processing involved the normalization of the three variables to constrain variability. Dengue incidence and rainfall time series had non-normal probability density functions, thus they were percentile-transformed .
Spatial analyses and social-ecological risk factors
Multiple best-fit models were within the predetermined threshold criteria of ΔAICc ≤2 of the top model and weights greater than 1.5% (Additional file 4: Table S2). In addition to the parameters included in the top model, the following variables were included in competing best-fit models: Breteau Index, population density, households with people who emigrate for work, and households without access to paved roads.
Temporal climate analyses
We found that multiple temporal scales were involved in local dengue transmission dynamics, as shown in the wavelet power spectrum for dengue incidence (Figure 4B). In wavelet analyses, strong significant signals at a certain frequency are associated with persistent (quasi) periodic cycles in the time series (e.g., a 1-year band indicates presence of annual cycles). There was a strong and significant signal for the ~2-year periodic band for dengue incidence. There was also a significant signal for the ~1-year periodic band, although it was less frequent (e.g., 2003, 2006, and 2011). Signals around and above the 4-year periodic band were not considered, as they fell inside of the COI (Figure 4B). These results suggest that dengue periodicity in this locality is not only annual (~1 year), but that there is also an important biannual cycle (~2 year), that may reflect typical time scales of extrinsic (e.g., climate) and intrinsic (e.g., immunologic) processes involved in the occurrence of dengue for this region.
The rainfall and minimum temperature spectra in Figure 4C,D demonstrated a strong annual signal (1-year periodic band), in agreement with the annual dengue cycle in the region. There was no evidence of relevant changes in variability for minimum temperature; the corresponding signal in the power spectrum is continuous around the annual band. In contrast, there were fluctuations in the ~1-year band for precipitation, likely associated with periods of low precipitation. Beginning around 2006, both climate variables demonstrated significant power around the 2-year band, a feature that is most noticeable in the rainfall data, particularly in recent years.
The cross-wavelet power spectra for dengue and rainfall (Figure 5A), and dengue and minimum temperature (Figure 5B) showed regions in the time-frequency space with high common power in the 1-year and 2-year bands, suggesting a relationship between climate and dengue incidence at both time scales. The corresponding wavelet coherence spectra, however, indicated that the dengue and rainfall co-vary mostly in the 2-year band (Figure 6A), while dengue and minimum temperature co-vary mostly in the 1-year band (Figure 6B). This suggests that temperature and rainfall have well-differentiated roles in dengue transmission. The directions of the arrows in the plots indicate a slow change of phase in the co-variability of dengue and rainfall in the 2-year band, approaching in-phase behavior in late 2009, and we observed synchronized co-variability for the 1-year band in 2009 and at the start of 2010. These results highlight the distinct roles of these climate variables in dengue transmission at different temporal scales, and the importance of the phase and timing of climate variables with respect to dengue transmission.
We found that the 2010 epidemic episode could be characterized by a combination of annual and bi-annual signals in dengue transmission and climate variables. The outbreak was characterized by a combination of in-phase variability of above normal minimum temperatures, and quasi-in-phase above normal rainfall episodes associated with the late 2009 to early 2010 moderate El Niño event (see arrows pointing right along the 1-year band at the bottom of Figures 5 and 6). The times series (1995-2010) of monthly anomalies in dengue cases from El Oro province, ENSO, temperature and rainfall have been previously described (See Figure 2 in Stewart Ibarra & Lowe 2013) . This analysis demonstrated that the observed effect (quasi-simultaneity in the variability of dengue, temperature and rainfall) was present in early 2010, but not in any other year of the period under study (Figures 5 and 6).
Dengue is the most important mosquito-borne viral disease globally, and has increased in incidence and distribution despite ongoing vector control interventions during the last three decades -. To date, we have a limited understanding of the spatiotemporal dynamics of dengue transmission, particularly at the local scale, due to the complex, non-stationary relationships among dengue infection, climate, vector, and virus strain dynamics ,-; and the geographic and temporal variation in the social-ecological conditions that influence risk -. More robust analysis tools, such as wavelet analyses and multimodel inference, and the increasing availability of geospatial epidemiological, climate, and social-ecological data have increased our ability to explore these dynamics. Studies such as this provide critical information to improve disease surveillance and to develop an EWS and other evidence-based interventions.
In this study, we found that neighborhoods with certain social-ecological conditions were more likely to have cases of dengue during the largest outbreak to date in El Oro Province. Dengue cases were clustered in neighborhood-level transmission hotspots near the city center during the epidemic. Risk factors included poor housing condition, greater access to piped water inside the home, less distance to the central hospital, and demographics of the heads of households (i.e., older age, female gender). In analyses of 10 years of weekly epidemiological and climate data, we found that dengue, rainfall and minimum temperature co-varied and had common power at 1-year and 2-year cycles, with quasi-synchronized higher than average rainfall and minimum temperatures likely contributing to the 2010 dengue outbreak. This study contributes to ongoing efforts by INAMHI and the MSP of Ecuador to develop a dengue prediction model and early warning system. Findings from this study will inform the development of dengue vulnerability maps and climate-driven dengue seasonal forecasts that provide the MSP with information to target high-risk regions and seasons, allowing for more efficient use of scarce resources .
Spatial dynamics and social-ecological risk factors
In Machala, a relatively small and heterogeneous city, there was evidence of unequal exposure or unequal reporting of dengue. During the epidemic, dengue transmission was focused in hotspots in the west-central urban sector, a middle- to low-income residential area with moderate access to urban infrastructure. Although people had access to basic services, our previous studies suggest that dengue control in these communities may be limited by the cost of household vector control, lack of social cohesion, and limited engagement with local institutions . Previous studies that used spatial clustering statistics also found evidence of significant clustering of dengue transmission across the urban landscape ,-. A previous study in Guayaquil, Ecuador, identified neighborhood-level dengue hot and coldspots, and found that the location of hotspots shifted over the 5-year period, highlighting the spatially dynamic nature of dengue risk and the importance of multiyear studies . Longitudinal field studies in Thailand found evidence of fine-scale spatial and temporal clustering of dengue virus serotypes and transmission at the school and household levels ,. Focal transmission patterns are likely associated with the limited flight range of the Ae. aegypti mosquito. Recent studies in Peru demonstrated the importance of human movement patterns in determining spatial dengue transmission dynamics within an urban area ,. At a regional scale, dengue outbreaks are likely influenced by human movement north and south along the Ecuador-Peru border. Future studies should continue to investigate the regional effects of cross-border movement of people and goods, and the local effects of intra-urban movement between work, school, and home to better understand the spatial dynamics of dengue transmission.
We found that the combination of HCI and access to piped water was the most important risk factor for dengue transmission, as indicated by the magnitude of the best-fit model parameter estimate (Table 2 Model A); this parameter was also a significant variable in all other top models (Additional file 4: Table S2). Neighborhoods were more likely to report dengue if they had poor housing conditions (likely associated with lower income) and greater access to piped water inside the home (likely associated with older, established communities with access to urban infrastructure). This apparent paradoxical relationship suggests that household water storage behaviors played an important role in the 2010 dengue outbreak. In our experience low-income households in Machala with access to piped water tend to store water in containers in the patio as a secondary water source, since water supply interruptions are common. These secondary water containers are often uncovered, and the containers become ideal Ae. aegypti larval habitat during the rainy season. In contrast, low-income households without access to piped water are likely to store water in containers as their primary water source (e.g., 55 gallon drums), frequently filling and emptying the containers and thus preventing Ae. aegypti from developing into adult mosquitoes.
Neighborhoods were also at greater risk of dengue if they were closer to the central hospital, reflecting either spatially biased reporting and/or a true increase in transmission near the city center. This variable was also significant in all of the top models (Additional file 4: Table S2). Given the small size of the city of Machala (~5 km across) and easy access to low-cost public transportation, travel time to the hospital was not likely to be a limiting factor. However, people from lower income communities may be less likely to seek medical care due to the cost of medicine and the high cost of missing work, leading to underreporting from the urban periphery. It is also possible that people residing near the city center in Machala were at greater risk because they may have been less willing to cooperate with vector control technicians (E. Beltran, pers. comm.), due in part to the misconception that dengue is a problem of poor communities at the urban periphery . Households in these areas may also be at greater risk because they store water as a secondary water source, as described above. These findings highlight the complexity of the cultural and behavioral factors influencing dengue risk and the importance of local-level studies that consider the social context.
Our findings are consistent with a previous longitudinal field study of household risk factors for Ae. aegypti in Machala, where it was found that poor housing condition and access to piped water inside the home were positively associated with the presence of Ae. aegypti pupae . This prior study found that Ae. aegypti were more abundant in the central urban area that had better access to infrastructure than in the urban periphery . Interestingly, the same risk factors emerged in the study presented here and the prior field study despite differences in rainfall (i.e., the field study was conducted one year after the epidemic, during a drier than average year) and differences in spatial scale (i.e., household- versus neighborhood level). These findings indicate that high-risk households could be identified and targeted using a combination of census data and a locally adapted rapid survey of housing conditions, similar to the Premise Condition Index, an aggregate index measuring house condition, patio condition, and patio shade, which has been validated in other countries ,. The HCI and the combined HCI-water access variables developed in this study should be explored and validated as dengue predictors in future studies in this region.
Ae. aegpyti juvenile indices were included in two of the top seven best-fit models to predict the presence of dengue in neighborhoods (Additional file 4: Table S2). A previous study in El Oro Province found that Ae. aegypti indices (House Index) were positively associated with dengue outbreaks at the province level . Although pupal or adult indices are considered better predictors of dengue risk than larval indices , our findings suggest that larval indices may have some predictive power in this region. In Ecuador, entomological surveillance is limited to larval indices, and neighborhoods are rarely sampled in consecutive periods in a given year due to limited resources. These findings highlight the need for additional studies of the vector-dengue dynamics in this region and local evaluations of the robustness of vector abundance measures in order to strengthen cost-effective entomological surveillance systems.
Climate and dengue periodicity
The wavelet analysis in this study provided a nuanced understanding of the relationships among local dengue transmission and climate variables at multiple temporal scales. The analysis of 10 years of weekly epidemiological and climate data from Machala provided evidence of significant 1-year and 2-year cycles in dengue, rainfall and minimum temperature. The 1-year cycles of minimum temperature and rainfall likely contributed to the annual dengue cycles observed in the power spectrum. This finding was expected, as previous studies have documented significant annual dengue cycles in this region . Interestingly, we also found evidence of 2-year cycles in the rainfall wavelet power spectrum that were likely associated with biannual cycles of dengue transmission, a pattern that was previously undocumented in Ecuador.
Indeed, our analyses suggest that the 2010 dengue epidemic could be related to a timely coincidence of above normal minimum temperatures and above normal rainfall episodes during the moderate 2009 to 2010 El Niño event. Previous studies in this region have shown that Ae. aegypti abundance is associated with rainfall and minimum temperature . In 2010, rainfall from February and March, the peak of dengue season, was almost double the long-term average (89% and 81% above average, respectively), likely increasing the availability of mosquito larval habitat. Temperature and temperature fluctuations influence rates of mosquito development and virus replication -. The slow rate of climate phase change observed in this analysis suggests the potential to monitor the climate in this region to identify future time periods with synchronous climate conditions similar to 2010, that may increase the risk of a dengue outbreak.
Our results indicate that the 2-year band in precipitation is an important component in the co-variability of dengue incidence for the period under study, although its role in the 2010 dengue epidemic requires further investigation. This periodic band is not unique to Machala. Two to three-year cycles of dengue transmission have been reported in other parts of the world ,, particularly in years associated with El Niño events. The statistically significant 2-year band is present in the dengue power spectrum for the entire time series. This is not true for rainfall or minimum temperature, whose variability in the region is strongly associated with El Niño-Southern Oscillation (ENSO). This suggests that although ENSO has a strong influence in the occurrence of dengue epidemics in coastal Ecuador, other variables (e.g., immunity) are also involved in the process and/or that there is a persistent mechanism for the climate’s biannual contribution in the dengue spectrum. It is interesting to note that similar 2-year cycles have been reported for dengue and malaria in mountain locations in Peru , but not along the Peruvian Coast or Amazon . We hypothesize that the biannual signal found in Peru and Machala is related to an additional climate mode present over the Andes in this region  in addition to ENSO. Machala may be uniquely situated to capture climate signals from ENSO and the so-called  Andean mode, given its proximity to the Andean foothills and the strong coupled climate-ocean system (i.e., teleconnections) present in the region.
Although this study revealed patterns of climate and social-ecological conditions as important drivers of dengue transmission, this study has some limitations. It should be noted that non-climate factors that were undocumented in this study (e.g., population immunity, vector control interventions) are also key drivers of interannual variability in dengue ,, and most likely influenced the 2010 outbreak. The 1-year of spatially explicit epidemiological data constrained our ability to assess whether the social-ecological factors associated with the spatial distribution of dengue transmission were consistent in time. The 10-year time series of weekly dengue data was not available at the appropriate spatial scale for this analysis. With multiple years of data, we could evaluate whether dengue transmission at the beginning of the dengue season or at the beginning of an epidemic is more likely to begin in neighborhoods with similar characteristics, to assess whether there are persistent high-risk, hotspot neighborhoods that trigger outbreaks. The analyses were also limited by a lack of laboratory confirmation for cases or information about the immune, nutritional, or health status of the population. We are currently collaborating with the MSP to improve dengue diagnostic infrastructure in the region and to reduce the time lag between epidemiological reporting and vector control interventions. Importantly, the MSP is undergoing a reorganization and decentralization process to merge the health and vector control divisions at the local level, with the goal of improving information flows and linking responses to evidence-based interventions.
The results of this study highlight the importance of incorporating climate and social-ecological information with georeferenced and clinically validated epidemiological data in a dengue surveillance system. Investigators in Ecuador are exploring the development of web-based GIS for national dengue surveillance using open-access software. GIS is an effective tool to integrate diverse data streams, such as dynamic, real-time epidemiological and climate data with static vulnerability maps generated from census data. Open access tools are especially important in resource-limited settings, and analysis packages targeted to dengue are becoming available . Web-based GIS tools have been developed for global dengue surveillance, such as the CDC’s DengueMap, and for local dengue surveillance research projects ,. National-level dengue GIS initiatives have been developed in countries such as Mexico , where Ministry of Health practitioners and software developers jointly designed the software platform. This collaborative approach to integrate diverse data streams will ideally provide public health decision-makers with information to assess intervention programs, allocate resources more efficiently, and provide the foundation for an operational dengue EWS.
Many thanks to colleagues at the MSP and INAMHI for supporting ongoing climate–health initiatives in Ecuador. This work was funded by the National Secretary of Higher Education, Science, Technology and Innovation of Ecuador (SENESCYT), grant to INAMHI for the project “Surveillance and climate modeling to predict dengue in urban centers (Guayaquil, Huaquillas, Portovelo, Machala),” and the Global Emerging Infections Surveillance and Response System (GEIS), grant #P0001_14_UN. AGM used computational resources from the Latin American Observatory of Extreme Events (www.ole2.org) and Centro de Modelado Científico (CMC), Universidad del Zulia. The following institutes that participated in this study also form part of the Latin American Observatory partnership (http://ole2.org): International Research Institute for Climate and Society (IRI), Earth Institute, Columbia University, New York, NY, USA; and Centro de Modelado Científico (CMC), Universidad del Zulia, Maracaibo, Venezuela; Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador; National Institute of Meteorology and Hydrology, Guayaquil, Ecuador.
- Dick OB, Martín JLS, Montoya RH, del Diego J, Zambrano B, Dayan GH: The history of dengue outbreaks in the Americas. Am J Trop Med Hyg. 2012, 87: 584-593. 10.4269/ajtmh.2012.11-0770.View ArticleGoogle Scholar
- San Martín JL, Brathwaite O, Zambrano B, Solórzano JO, Bouckenooghe A, Dayan GH, Guzmán MG: The epidemiology of dengue in the Americas over the last three decades: a worrisome reality. Am J Trop Med Hyg. 2010, 82: 128-135. 10.4269/ajtmh.2010.09-0346.View ArticlePubMedPubMed CentralGoogle Scholar
- Global Strategy for Dengue Prevention and Control, 2012-2020. 2012, WHO, GenevaGoogle Scholar
- Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control. 2009, WHO, GenevaGoogle Scholar
- Gubler DJ: Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends Microbiol. 2002, 10: 100-103. 10.1016/S0966-842X(01)02288-0.View ArticlePubMedGoogle Scholar
- Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, Drake JM, Brownstein JS, Hoen AG, Sankoh O, Myers MF, George DB, Jaenisch T, Wint GRW, Simmons CP, Scott TW, Farrar JJ, Hay SI: The global distribution and burden of dengue. Nature. 2013, 496: 504-507. 10.1038/nature12060.View ArticlePubMedPubMed CentralGoogle Scholar
- Messina JP, Brady OJ, Scott TW, Zou C, Pigott DM, Duda KA, Bhatt S, Katzelnick L, Howes RE, Battle KE, Simmons CP, Hay SI: Global spread of dengue virus types: mapping the 70 year history. Trends Microbiol. 2014, 22: 138-146. 10.1016/j.tim.2013.12.011.View ArticlePubMedPubMed CentralGoogle Scholar
- Kyle JL, Harris E: Global spread and persistence of dengue . Annu Rev Microbiol. 2008, 62: 71-92. 10.1146/annurev.micro.62.081307.163005.View ArticlePubMedGoogle Scholar
- Kuhn K, Campbell-Lendrum D, Haines A, Cox J: Using climate to predict infectious disease epidemics. 2005, World Health Organization, GenevaGoogle Scholar
- Thomson MC, Garcia-Herrera R, Beniston M (Eds.): Seasonal Forecasts, Climatic Change and Human Health: Health and Climate. Springer; 2008.Google Scholar
- Lowe R, Bailey TC, Stephenson DB, Jupp TE, Graham RJ, Barcellos C, Carvalho MS: The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in southeast Brazil. Stat Med. 2013, 32: 864-883. 10.1002/sim.5549.View ArticlePubMedGoogle Scholar
- Racloz V, Ramsey R, Tong S, Hu W: Surveillance of dengue fever virus: a review of epidemiological models and early warning systems. PLoS Negl Trop Dis. 2012, 6: e1648-10.1371/journal.pntd.0001648.View ArticlePubMedPubMed CentralGoogle Scholar
- Yu H-L, Yang S-J, Yen H-J, Christakos G: A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan. Stoch Environ Res Risk Assess. 2011, 25: 485-494. 10.1007/s00477-010-0417-9.View ArticleGoogle Scholar
- Grover-Kopec E, Kawano M, Klaver RW, Blumenthal B, Ceccato P: An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa. Malar J. 2013, 4: 5-Google Scholar
- Ruiz D, Connor SJ, Thomson MC: A multimodel framework in support of malaria surveillance and control. In Seasonal Forecasts, Climatic Change and Human Health: Health and Climate. Springer; 2008:101-125.Google Scholar
- Thomson MC, Mason SJ, Phindela T, Connor SJ: Use of rainfall and sea surface temperature monitoring for malaria early warning in Botswana. Am J Trop Med Hyg. 2005, 73: 214-221.PubMedGoogle Scholar
- Anyamba A, Chretien J-P, Small J, Tucker CJ, Formenty PB, Richardson JH, Britch SC, Schnabel DC, Erickson RL, Linthicum KJ: Prediction of a Rift Valley fever outbreak. Proc Natl Acad Sci. 2009, 106: 955-959. 10.1073/pnas.0806490106.View ArticlePubMedPubMed CentralGoogle Scholar
- Almeida AS, Medronho RA, Valencia LIO: Spatial analysis of dengue and the socioeconomic context of the city of Rio de Janeiro (southeastern Brazil). Rev Saúde Publica. 2009, 43: 666-673. 10.1590/S0034-89102009000400013.View ArticlePubMedGoogle Scholar
- De Mattos Almeida MC, Caiaffa WT, Assunçao RM, Proietti FA: Spatial vulnerability to dengue in a Brazilian urban area during a 7-year surveillance. J Urban Health. 2007, 84: 334-345. 10.1007/s11524-006-9154-2.View ArticlePubMedPubMed CentralGoogle Scholar
- Mondini A, de Moraes Bronzoni RV, Nunes SHP, Neto FC, Massad E, Alonso WJ, Lázzaro ES, Ferraz AA, de Andrade Zanotto PM, Nogueira ML: Spatio-temporal tracking and phylodynamics of an urban dengue 3 outbreak in Sao Paulo, Brazil. PLoS Negl Trop Dis. 2009, 3: e448-10.1371/journal.pntd.0000448.View ArticlePubMedPubMed CentralGoogle Scholar
- da Teixeira GM, da Costa MCN, Ferreira LDA, Vasconcelos PFC, Cairncross S: Dynamics of dengue virus circulation: a silent epidemic in a complex urban area. Trop Med Int Health. 2002, 7: 757-762. 10.1046/j.1365-3156.2002.00930.x.View ArticleGoogle Scholar
- Stewart Ibarra AM, Ryan SJ, Beltrán E, Mejía R, Silva M, Muñoz Á: Dengue vector dynamics (Aedes aegypti) influenced by climate and social factors in Ecuador: implications for targeted control. PloS One. 2013, 8: e78263-10.1371/journal.pone.0078263.View ArticlePubMedPubMed CentralGoogle Scholar
- Moore CG, Cline BL, Ruiz-Tiben E, Lee D, Romney-Joseph H, Rivera-Correa E: A edes aegypti in Puerto Rico: environmental determinants of larval abundance and relation to dengue virus transmission . Am J Trop Med Hyg. 1978, 27: 1225-1231.PubMedGoogle Scholar
- Barrera R, Amador M, MacKay AJ: Population dynamics of Aedes aegypti and dengue as influenced by weather and human behavior in San Juan, Puerto Rico. PLoS Negl Trop Dis. 2011, 5: e1378-10.1371/journal.pntd.0001378.View ArticlePubMedPubMed CentralGoogle Scholar
- Ministerio de Salud Publica de Ecuador Dirección Nacional de Vigilancia Epidemiológica: Anuario Epidemiológico:, 1994-2014. Quito; 2014. , [http://www.salud.gob.ec/direccion-nacional-de-vigilancia-epidemiologica/]
- Stewart Ibarra AM, Lowe R: Climate and non-climate drivers of dengue epidemics in southern coastal ecuador. Am J Trop Med Hyg. 2013, 88: 971-981. 10.4269/ajtmh.12-0478.View ArticlePubMedPubMed CentralGoogle Scholar
- Muñoz ÁG, Stewart-Ibarra AM, Ruiz Carrascal D: Desarrollo de modelos de pronóstico experimental: análisis socio ecolÁGico de riesgo a dengue y análisis estadístico de patrones climáticos, entomolÁGicos y epidemiolÁGicos en modelos de dengue. Technical Report for the CLIDEN Project. 2013, INAMHI-SENESCYT, QuitoGoogle Scholar
- Recalde-Coronel GC, Barnston AG, Muñoz ÁG: Predictability of December-April rainfall in coastal and Andean Ecuador. J Appl Meteorol Climatol. 2014, 53 (6): 1471-1493. 10.1175/JAMC-D-13-0133.1.View ArticleGoogle Scholar
- Instituto Nacional de Estadística y Censos (INEC): Censo de Poblacion Y Vivienda. Quito: 2010 , [http://www.ecuadorencifras.gob.ec/]
- Arunachalam N, Tana S, Espino F, Kittayapong P, Abeyewickrem W, Wai KT, Tyagi BK, Kroeger A, Sommerfeld J, Petzold M: Eco-bio-social determinants of dengue vector breeding: a multicountry study in urban and periurban Asia. Bull World Health Organ. 2010, 88: 173-184. 10.2471/BLT.09.067892.View ArticlePubMedPubMed CentralGoogle Scholar
- Quintero J, Brochero H, Manrique-Saide P, Barrera-Pérez M, Basso C, Romero S, Caprara A, Cunha JCDL, Ayala EB, Mitchell-Foster K, Kroeger A, Sommerfeld J, Petzold M: Ecological, biological and social dimensions of dengue vector breeding in five urban settings of Latin America: a multi-country study. BMC Infect Dis. 2014, 14: 38-10.1186/1471-2334-14-38.View ArticlePubMedPubMed CentralGoogle Scholar
- Real J, Mosquera C: Detección del virus dengue en el ecuador. una visión epidemiológica. Periodo, 1988-2012. 2012, Instituto Nacional de Higiene y Medicina Tropical del Ministerio de Salud Publica, GuayaquilGoogle Scholar
- Servicio Nacional para el Control de Enfermedades Transmitidas por Artropodos (SNEM), Ministerio de Salud Publica (MSP): Informe general por provincias, cantones/localidades. programa de control y vigilancia del Aedes aegypti. Indices Breteau y Casa por ciclos. Guayaquil.Google Scholar
- Hartter J, Ryan SJ, MacKenzie CA, Parker JN, Strasser CA: Spatially explicit data: stewardship and ethical challenges in science. PLoS Biol. 2013, 11: e1001634-10.1371/journal.pbio.1001634.View ArticlePubMedPubMed CentralGoogle Scholar
- Beyer HL: Geospatial Modelling Environment v0. 7.0. 2012.Google Scholar
- Beyer HL, Jenness J, Cushman SA: Components of spatial information management in wildlife ecology: software for statistical and modeling analysis. Spatial Complexity, Informatics, and Wildlife Conservation. 2010, Springer, Japan, 245-253. 10.1007/978-4-431-87771-4_14.View ArticleGoogle Scholar
- Anselin L: Local indicators of spatial association—LISA. Geogr Anal. 1995, 27: 93-115. 10.1111/j.1538-4632.1995.tb00338.x.View ArticleGoogle Scholar
- de Oliveira MA, Ribeiro H, Castillo-Salgado C, de Oliveira MA, Ribeiro H, Castillo-Salgado C: Geospatial analysis applied to epidemiological studies of dengue: a systematic review. Rev Bras Epidemiol. 2013, 16: 907-917. 10.1590/S1415-790X2013000400011.View ArticleGoogle Scholar
- Castillo KC, Körbl B, Stewart A, Gonzalez JF, Ponce F: Application of spatial analysis to the examination of dengue fever in Guayaquil, Ecuador. Procedia Environ Sci. 2011, 7: 188-193. 10.1016/j.proenv.2011.07.033.View ArticleGoogle Scholar
- Calcagno V, de Mazancourt C: Glmulti: an R Package for easy automated model selection with (generalized) linear models . J Stat Softw. 2010, 34: 1-29.View ArticleGoogle Scholar
- Cazelles B, Chavez M, McMichael AJ, Hales S: Nonstationary influence of El Niño on the synchronous dengue epidemics in Thailand. PLoS Med. 2005, 2: e106-10.1371/journal.pmed.0020106.View ArticlePubMedPubMed CentralGoogle Scholar
- Cazelles B, Chavez M, de Magny GC, GuÁGan J-F, Hales S: Time-dependent spectral analysis of epidemiological time-series with wavelets. J R Soc Interface. 2007, 4: 625-636. 10.1098/rsif.2007.0212.View ArticlePubMedPubMed CentralGoogle Scholar
- Muñoz ÁG, Ruiz D, Ramírez P, León G, Quintana J, Bonilla A, Torres W, Pastén M, Sánchez O: Risk management at the Latin American Observatory. In Risk Manag - Curr Issues Chall. Edited by Banaitiene N. InTech; 2012:532-556.Google Scholar
- Muñoz ÁG, López P, Velásquez R, Monterrey L, León G, Ruiz F, Recalde C, Cadena J, Mejía R, Paredes M: An environmental watch system for the Andean Countries: El Observatorio Andino. Bull Am Meteorol Soc. 2010, 91: 1645-1652. 10.1175/2010BAMS2958.1.View ArticleGoogle Scholar
- Aguilar E, Sigró J, Brunet M: RClimdex con funcionalidades extras de control de calidad. Manual de Uso, version 1.0 2009:12. , [http://cmc.org.ve/descargas/Cursos/CRRH/Manual_rclimdex_extraQC.r.pdf]
- RHTestsV4 software , [http://etccdi.pacificclimate.org/RHtest/RHtestsV4_UserManual_20July2013.pdf]
- Wang XL, Chen H, Wu Y, Feng Y, Pu Q: New techniques for detection and adjustment of shifts in daily precipitation data series. J Appl Meteorol Climatol. 2010, 49: 2416-2436. 10.1175/2010JAMC2376.1.View ArticleGoogle Scholar
- Wang XL: Accounting for autocorrelation in detecting mean shifts in climate data series using the penalized maximal t or f test. J Appl Meteorol Climatol. 2008, 47: 2423-2444. 10.1175/2008JAMC1741.1.View ArticleGoogle Scholar
- Wang XL: Penalized maximal f test for detecting undocumented mean shift without trend change. J Atmospheric Ocean Technol. 2008, 25: 368-384. 10.1175/2007JTECHA982.1.View ArticleGoogle Scholar
- Torrence C, Compo GP: A practical guide to wavelet analysis. Bull Am Meteorol Soc. 1998, 79: 61-78. 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2.View ArticleGoogle Scholar
- Grinsted A, Moore JC, Jevrejeva S: Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process Geophys. 2004, 11: 561-566. 10.5194/npg-11-561-2004.View ArticleGoogle Scholar
- Lourenço J, Recker M: Natural, persistent oscillations in a spatial multi-strain disease system with application to dengue. PLoS Comput Biol. 2013, 9: e1003308-10.1371/journal.pcbi.1003308.View ArticlePubMedPubMed CentralGoogle Scholar
- Reiner RC, Stoddard ST, Forshey BM, King AA, Ellis AM, Lloyd AL, Long KC, Rocha C, Vilcarromero S, Astete H, Bazan I, Lenhart A, Vazquez-Prokopec GM, Paz-Soldan VA, McCall PJ, Kitron U, Elder JP, Halsey ES, Morrison AC, Kochel TJ, Scott TW: Time-varying, serotype-specific force of infection of dengue virus. Proc Natl Acad Sci. 2014, 111: E2694-E2702. 10.1073/pnas.1314933111.View ArticlePubMedPubMed CentralGoogle Scholar
- Cummings DAT, Irizarry RA, Huang NE, Endy TP, Nisalak A, Ungchusak K, Burke DS: Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand. Nature. 2004, 427: 344-347. 10.1038/nature02225.View ArticlePubMedGoogle Scholar
- Stewart Ibarra AM, Luzadis VA, Borbor Cordova MJ, Silva M, Ordoñez T, Beltrán Ayala E, Ryan SJ: A social-ecological analysis of community perceptions of dengue fever and Aedes aegypti in Machala, Ecuador. BMC Public Health. 2014, 14: 1135-10.1186/1471-2458-14-1135.View ArticlePubMedPubMed CentralGoogle Scholar
- Galli B, Chiaravalloti Neto F: Temporal-spatial risk model to identify areas at high-risk for occurrence of dengue fever. Rev Saúde Pública. 2008, 42: 656-663. 10.1590/S0034-89102008000400011.View ArticlePubMedGoogle Scholar
- Vazquez-Prokopec GM, Kitron U, Montgomery B, Horne P, Ritchie SA: Quantifying the spatial dimension of dengue virus epidemic spread within a tropical urban environment. PLoS Negl Trop Dis. 2010, 4: e920-10.1371/journal.pntd.0000920.View ArticlePubMedPubMed CentralGoogle Scholar
- Hu W, Clements A, Williams G, Tong S: Spatial analysis of notified dengue fever infections. Epidemiol Infect. 2011, 139: 391-399. 10.1017/S0950268810000713.View ArticlePubMedGoogle Scholar
- Endy TP, Nisalak A, Chunsuttiwat S, Libraty DH, Green S, Rothman AL, Vaughn DW, Ennis FA: Spatial and temporal circulation of dengue virus serotypes: a prospective study of primary school children in Kamphaeng Phet, Thailand. Am J Epidemiol. 2002, 156: 52-59. 10.1093/aje/kwf006.View ArticlePubMedGoogle Scholar
- Yoon I-K, Getis A, Aldstadt J, Rothman AL, Tannitisupawong D, Koenraadt CJM, Fansiri T, Jones JW, Morrison AC, Jarman RG, Nisalak A, Mammen MP, Thammapalo S, Srikiatkhachorn A, Green S, Libraty DH, Gibbons RV, Endy T, Pimgate C, Scott TW: Fine scale spatiotemporal clustering of dengue virus transmission in children and Aedes aegypti in rural Thai villages. PLoS Negl Trop Dis. 2012, 6: e1730-10.1371/journal.pntd.0001730.View ArticlePubMedPubMed CentralGoogle Scholar
- Stoddard ST, Morrison AC, Vazquez-Prokopec GM, Soldan VP, Kochel TJ, Kitron U, Elder JP, Scott TW: The role of human movement in the transmission of vector-borne pathogens. PLoS Negl Trop Dis. 2009, 3: e481-10.1371/journal.pntd.0000481.View ArticlePubMedPubMed CentralGoogle Scholar
- Vazquez-Prokopec GM, Stoddard ST, Paz-Soldan V, Morrison AC, Elder JP, Kochel TJ, Scott TW, Kitron U: Usefulness of commercially available GPS data-loggers for tracking human movement and exposure to dengue virus. Int J Health Geogr. 2009, 8: 68-10.1186/1476-072X-8-68.View ArticlePubMedPubMed CentralGoogle Scholar
- Andrighetti MTM, Galvani KC, da Gracca Macoris ML: Evaluation of Premise Condition Index in the context of Aedes aegypti control in Marília, São Paulo. Brazil. Dengue Bull. 2009, 33: 167-Google Scholar
- Tun-Lin W, Kay BH, Barnes A: The Premise Condition Index: a tool for streamlining surveys of Aedes aegypti . Am J Trop Med Hyg. 1995, 53: 591-594.PubMedGoogle Scholar
- Focks DA: A review of entomological sampling methods and indicators for dengue vectors. 2003, World Health Organization, GenevaGoogle Scholar
- Pant CP, Yasuno M: Field studies on the gonotrophic cycle of Aedes aegypti in Bangkok, Thailand. J Med Entomol. 1973, 10: 219-223.View ArticlePubMedGoogle Scholar
- Watts DM, Burke DS, Harrison BA, Whitmire RE, Nisalak A: Effect of temperature on the vector efficiency of Aedes aegypti for dengue 2 virus. Am J Trop Med Hyg. 1986, 36: 143-152.Google Scholar
- Rueda LM, Patel KJ, Axtell RC, Stinner RE: Temperature-dependent development and survival rates of Culex quinquefasciatus and Aedes aegypti (Diptera: Culicidae). J Med Entomol. 1990, 27: 892-898.View ArticlePubMedGoogle Scholar
- Bar-Zeev M: The effect of temperature on the growth rate and survival of the immature stages of Aedes aegypti (L.). Bull Entomol Res. 1958, 49: 157-163. 10.1017/S0007485300053499.View ArticleGoogle Scholar
- Thu HM, Aye KM, Thein S: The effect of temperature and humidity on dengue virus propagation in Aedes aegypti mosquitos. Southeast Asian J Trop Med Public Health. 1998, 29: 280-284.PubMedGoogle Scholar
- Lambrechts L, Paaijmans KP, Fansiri T, Carrington LB, Kramer LD, Thomas MB, Scott TW: Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti . Proc Natl Acad Sci. 2011, 108: 7460-7465. 10.1073/pnas.1101377108.View ArticlePubMedPubMed CentralGoogle Scholar
- Chowell G, Cazelles B, Broutin H, Munayco CV: The influence of geographic and climate factors on the timing of dengue epidemics in Perú, 1994-2008. BMC Infect Dis. 2011, 11: 164-10.1186/1471-2334-11-164.View ArticlePubMedPubMed CentralGoogle Scholar
- Chowell G, Munayco CV, Escalante AA, McKenzie FE: The spatial and temporal patterns of falciparum and vivax malaria in Perú: 1994-2006. Malar J. 2009, 8: 142-10.1186/1475-2875-8-142.View ArticlePubMedPubMed CentralGoogle Scholar
- Hay SI, Myers MF, Burke DS, Vaughn DW, Endy T, Ananda N, Shanks GD, Snow RW, Rogers DJ: Etiology of interepidemic periods of mosquito-borne disease. Proc Natl Acad Sci. 2000, 97: 9335-9339. 10.1073/pnas.97.16.9335.View ArticlePubMedPubMed CentralGoogle Scholar
- Wearing HJ, Rohani P: Ecological and immunological determinants of dengue epidemics. Proc Natl Acad Sci. 2006, 103: 11802-11807. 10.1073/pnas.0602960103.View ArticlePubMedPubMed CentralGoogle Scholar
- Delmelle EM, Zhu H, Tang W, Casas I: A web-based geospatial toolkit for the monitoring of dengue fever. Appl Geogr. 2014, 52: 144-152. 10.1016/j.apgeog.2014.05.007.View ArticleGoogle Scholar
- Chang AY, Parrales ME, Jimenez J, Sobieszczyk ME, Hammer SM, Copenhaver DJ, Kulkarni RP: Combining Google Earth and GIS mapping technologies in a dengue surveillance system for developing countries. Int J Health Geogr. 2009, 8: 49-10.1186/1476-072X-8-49.View ArticlePubMedPubMed CentralGoogle Scholar
- Moreno-Sanchez R, Hayden M, Janes C, Anderson G: A web-based multimedia spatial information system to document Aedes aegypti breeding sites and dengue fever risk along the US–Mexico border. Health Place. 2006, 12: 715-727. 10.1016/j.healthplace.2005.10.001.View ArticlePubMedGoogle Scholar
- Hernández-Ávila JE, RodrÁGuez M-H, Santos-Luna R, Sánchez-Castañeda V, Román-Pérez S, Ríos-Salgado VH, Salas-Sarmiento JA: Nation-wide, web-based, geographic information system for the integrated surveillance and control of dengue fever in Mexico. PLoS ONE. 2013, 8: e70231-10.1371/journal.pone.0070231.View ArticlePubMedPubMed CentralGoogle Scholar
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