Estimating Effects of Temperature on Dengue Transmission in Colombian Cities

Background: Dengue fever is a viral disease that affects tropical and subtropical regions of the world. It is well known that processes related to virus transmission by mosquitoes are highly influenced by weather. Temperature has been described as one of the climatic variables that largely governs the development and survival of mosquito eggs as well as the survival of all insect stages. Previously, we noted that high temperatures in the Colombian city of Riohacha negatively affect the establishment of dengue virus (DENV) infection in mosquitoes; in Bello and Villavicencio cities, which have lower average temperatures, DENV infection rates in mosquitoes are positively associated with a gradual increase in temperature. Here, we test the hypothesis that a similar effect of temperature can be detected in the incidence in the human population inhabiting dengue-endemic cities in Colombia. Objective: Our objective was to evaluate the effect of climate variables related to temperature on DENV incidence in human populations living in DENV-endemic cities in Colombia. Methods: Epidemiologic data from the Instituto Nacional de Salud from 2012-2015 and 7 variables related to temperature were used to perform Spearman rank sum test analyses on 20 Colombian cities. Additionally, locally estimated scatterplot smoothing analyses were performed to describe the relationship among temperatures and incidence. Findings: Results indicated that Colombian cities with average and maximum temperatures greater than 28°C and 32°C, respectively, had an inversely related relationship to DENV incidence, which is in accordance with areas where higher temperatures are recorded in Colombia. ConclusionClimatic variables related to temperature affect dengue epidemiology in different way. According to the temperature of each city, transmission might be positively or negatively affected

APPLICATION OF SATELLITE IMAGES TO STUDY THE DISTRIBUTION OF Rhodnius pallescens BARBER 1932, VECTOR OF CHAGAS DISEASE

Chagas disease is the most disabling disease in Latin America. Because the current increase of global human migration threatens to widen the disease range, , including vector borne diseases,  to study the distribution of insect vectors becomes an important task. Rhodnius pallescens is the main Chagas disease vector in Panama, and a secondary one in Colombia, Nicaragua and Costa Rica. Given their wild habits, their distribution could be highly influenced by climatic factors. In this study we built a geographical distribution model of this vector using the method of maximum entropy (Maxent) to identify sites having the highest occurrence probability of finding the vector species. The results obtained demonstrate that the sites predicted by the model, as those with the greatest occurrence probability, fitted with those already recorded in the field as vector presence sites. The high sensitivity of the prediction was evidenced by the value of the area under the ROC curve (AUC=0.995)

Early Detection for Dengue Using Local Indicator of Spatial Association (LISA) Analysis.

Dengue is a viral disease caused by a flavivirus that is transmitted by mosquitoes of the genus Aedes. There is currently no specific treatment or commercial vaccine for its control and prevention; therefore, mosquito population control is the only alternative for preventing the occurrence of dengue. For this reason, entomological surveillance is recommended by World Health Organization (WHO) to measure dengue risk in endemic areas; however, several works have shown that the current methodology (aedic indices) is not sufficient for predicting dengue. In this work, we modified indices proposed for epidemic periods. The raw value of the epidemiological wave could be useful for detecting risk in epidemic periods; however, risk can only be detected if analyses incorporate the maximum epidemiological wave. Risk classification was performed according to Local Indicators of Spatial Association (LISA) methodology. The modified indices were analyzed using several hypothetical scenarios to evaluate their sensitivity. We found that modified indices could detect spatial and differential risks in epidemic and endemic years, which makes them a useful tool for the early detection of a dengue outbreak. In conclusion, the modified indices could predict risk at the spatio-temporal level in endemic years and could be incorporated in surveillance activities in endemic places

Mapping dengue fever transmission risk in the Aburrá Valley, Colombia

Int. J. Environ. Res. Public Health 2009, 6, 3040-3055; doi:10.3390/ijerph6123040Dengue fever (DF) is endemic in Medellín, the second largest Colombian city, and surrounding municipalities. We used DF case and satellite environmental data to investigate conditions associated with suitable areas for DF occurrence in 2008 in three municipalities (Bello, Medellín and Itagüí). We develop spatially stratified tests of ecological niche models, and found generally good predictive ability, with all model tests yielding results significantly better than random expectations. We concluded that Bello and Medellín present ecological conditions somewhat different from, and more suitable for DF than, those of Itagüí. We suggest that areas predicted by our models as suitable for DF could be considered as at-risk, and could be used to guide campaigns for DF prevention in these municipalities

Spatio-Temporal Distribution of Aedes aegypti (Diptera: Culicidae) Mitochondrial Lineages in Cities with Distinct Dengue Incidence Rates Suggests Complex Population Dynamics of the Dengue Vector in Colombia

Aedes aegypti is the primary vector of the four serotypes of dengue virus (DENV1-4), Chi- kungunya and yellow fever virus to human

Development of a geographical distribution model of Rhodnius pallescens Barber, 1932 using environmental data recorded by remote sensing

Rhodnius pallescens, main vector of Trypanosoma cruzi in Panama and secondary vector in Colombia, Costa Rica and Nicaragua, represents an important epidemiological risk in those countries. It occupies sylvatic ecotopes, and because of this its distribution and abundance could be conditioned by environmental factors. In this work, we integrated environmental variables recorded by remote sensing and data of R. pallescens presence in the countries mentioned above in order to know the environmental variables with better capacity to describe the insects' distribution, which will help to entomological surveillance and control. Standard discriminant analysis (DA) was used to determine if there is a significant difference in the environmental variation between the presence and the absence sites of R. pallescens. Forward stepwise discriminant analysis (fDA) was used to determine the environmental variables which better discriminated between presence and absence sites, and to construct a predictive map of geographical distribution. Univariate analyses were used to determine the discriminatory power of individual variables. The model derived from DA showed 89% of sensitivity and 92.8% of specificity. Multivariate and univariate analyses showed the vapor pressure deficit minimum as the principal variable among the nine most important to describe the distribution of the species, which is in agreement with the R. pallescens stenohydric status. Map shows insects' distribution predicted by environmental variables, and moreover includes the distribution of most species belonging to Rhodnius genus, except R. domesticus, R. nasutus and R. neglectus. We interpreted these results as a reflection of the common evolution of the most Rhodnius species, except for the last ones that probably evolved isolated due to particular environmental conditions. In conclusion, this study showed that a reduced number of environmental variables can predict the distribution of R. pallescens and related species. This methodology can be very useful to make critical decisions for vectorial surveillance and control of Chagas disease vectors.Fil: Arboleda, Sair. Universidad de Antioquia; ColombiaFil: Gorla, David Eladio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de Catamarca. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Secretaría de Industria y Minería. Servicio Geológico Minero Argentino. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Provincia de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; ArgentinaFil: Porcasi Gomez, Ximena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Universidad Nacional de Catamarca. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Secretaría de Industria y Minería. Servicio Geológico Minero Argentino. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja. - Provincia de La Rioja. Centro Regional de Investigaciones Científicas y Transferencia Tecnológica de La Rioja; ArgentinaFil: Saldaña, Azael. Instituto Conmemorativo Gorgas de Estudios de la Salud; PanamáFil: Calzada, Jose. Instituto Conmemorativo Gorgas de Estudios de la Salud; PanamáFil: Jaramillo, Nicolas. Universidad de Antioquia; Colombi

Potential distribution of mosquito vector species in a primary malaria endemic region of Colombia

Rapid transformation of natural ecosystems changes ecological conditions for important human disease vector species; therefore, an essential task is to identify and understand the variables that shape distributions of these species to optimize efforts toward control and mit- igation. Ecological niche modeling was used to estimate the potential distribution and to assess hypotheses of niche similarity among the three main malaria vector species in north- ern Colombia: Anopheles nuneztovari, An. albimanus, and An. darlingi. Georeferenced point collection data and remotely sensed, fine-resolution satellite imagery were integrated across the Urabá –Bajo Cauca–Alto Sinú malaria endemic area using a maximum entropy algorithm. Results showed that An. nuneztovari has the widest geographic distribution, occupying almost the entire study region; this niche breadth is probably related to the ability of this species to colonize both, natural and disturbed environments. The model for An. dar- lingi showed that most suitable localities for this species in Bajo Cauca were along the Cauca and Nechı́ river. The riparian ecosystems in this region and the potential for rapid adaptation by this species to novel environments, may favor the establishment of popula- tions of this species. Apparently, the three main Colombian Anopheles vector species in this endemic area do not occupy environments either with high seasonality, or with low season- ality and high NDVI values. Estimated overlap in geographic space between An. nuneztovari and An. albimanus indicated broad spatial and environmental similarity between these spe- cies. An. nuneztovari has a broader niche and potential distribution. Dispersal ability of these species and their ability to occupy diverse environmental situations may facilitate sym- patry across many environmental and geographic contexts. These model results may be useful for the design and implementation of malaria species-specific vector control interven- tions optimized for this important malaria region

Principal coordinate analysis (PCoA) of combined COI-ND4 genes in <i>Ae. aegypti</i> mosquitoes of Colombia.

<p>(A) Dotted line represents inferred genetic group 1 and 2 of Colombian mosquitoes projected on the first (x-axis) and second principal coordinates (y-axis), which were derived from a PCoPA analysis. PC1 explains 79.2% of the variance whereas PC2 explains 8%; the color indicates the collection origin: BE (green), RI (blue) and VI (red); the circles, triangles and squares represent A, B, and C samplings, respectively (see <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003553#pntd.0003553.t001" target="_blank">Table 1</a> for details). (B) Box plot of PC1-eigenvalues of group 1 (n = 239) and group 2 (n = 58) derived from PCoA.</p