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613 results for “Dengue”
Data-Driven Computational Intelligence Applied to Dengue Outbreak Forecasting: a case study at the scale of the city of Natal, RN-Brazil
<p><strong>The dataset comprises survey data from the following sources:dengue_incidence_data.csv: public data provided by Municipal Health Department of Natal, State of Rio Grande do Norte, Brazil; and data of Brazilian Notifiable Diseases Information System (Sinan). The objective of this paper was to analyze incidence data of dengue cases registered in each neighborhood of Natal city, weekly sampled (52 epidemiological weeks a year) between 2016 – 2019). </strong></p>
Dengue_WNV_vector_Models_modeledsuitability_EU
<p><strong>Abstract:</strong></p> <p>Ensembled spatial models were produced for disease vectors by combining Random Forest and Boosted Regression Trees spatial modelling outputs, implemented using the VECMAP modelling suite, using a standard set of covariates including Fourier Processed Remotely Sensed environmental variables, land use proportions, human population and elevation. The training data offered to the model process include point location and polygon data from the VectorNet project (<a href="https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net" target="_blank" rel="noopener">https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/vector-net</a>), from the Global Biodiversity Information Facility (<a href="http://www.gbif.org/" target="_blank" rel="noopener">www.gbif.org</a>) and a series of national databases. Absence data assembled from absence records, complemented by points assigned to unsuitable areas defined by environmental and climatic thresholds obtained from experts or from the literature</p> <p> </p> <p>Two sets of vectors have been modelled: </p> <ol> <li>Potential Dengue Vectors (Global= <em>Aedes albopictus</em>, <em>Aedes aegypti</em>; Europe = <em>Aedes koreicus</em> and <em>Aedes japonicus</em>) and a combination of Ae. albopictus and Ae. Aegypti calculated as Mean (aegypti +(albopictus/2)). Filename E4DENGUEVECTORMODELS.zip</li> <li>Potential WNV vectors (<em>Culex pipiens</em>, <em>Culex torrentium</em>, <em> Culex modestus, Aedes vexans) </em>Filename e4WNVVECTORMODELS.zip</li> </ol> <p> </p>
Dengue: Padrões de dinâmica de transmissão nos municípios do Brasil, 2010-2023
<p>Dengue é uma doença viral endêmica no Brasil, onde a transmissão é influenciada pela presença do <em>Aedes aegypti</em> e intensificada por condições climáticas favoráveis ao mosquito. A transmissão dessa doença ocorre de forma heterogênea, com variações significativas na incidência e gravidade dos surtos entre diferentes regiões do país. A temperatura do ar, a umidade, a precipitação, a urbanização desordenada e a infraestrutura de saúde precária contribuem para essa variabilidade, criando padrões de transmissão distintos, que vão desde a ausência de transmissão até a transmissão persistente. </p>
Data from: Long-term persistence of monotypic dengue transmission in small size isolated populations, French Polynesia, 1978-2014
<p>Understanding the transition of epidemic to endemic dengue transmission remains a challenge in regions where serotypes co-circulate and there is extensive human mobility. French Polynesia, an isolated group of 72 inhabited islands, distributed among five geographically separated subdivisions, has recorded mono-serotype epidemics since 1944, with long inter-epidemic periods of circulation. Laboratory confirmed cases have been recorded since 1978, enabling exploration of dengue epidemiology under monotypic conditions in an isolated, spatially structured geographical location. A database was constructed of confirmed dengue cases, geolocated to island for a 35-year period. Statistical analyses of viral establishment, persistence and fade-out as well as synchrony among subdivisions were performed. Seven monotypic and one heterotypic dengue epidemic occurred, followed by low-level viral circulation with a recrudescent epidemic occurring on one occasion. Incidence was asynchronous among the subdivisions. Complete viral die-out occurred on several occasions with invasion of a new serotype, but also in the absence of any novel serotype. Island population size had a strong impact on the establishment, persistence and fade-out of dengue cases and endemicity was estimated achievable only at a population size in excess of 175 000. Despite island remoteness and low population size, dengue cases were observed somewhere in French Polynesia almost constantly, in part due to the spatial structuration generating asynchrony among subdivisions. Long-term persistence of dengue virus in this group of island populations may be enabled by island hopping, although could equally be explained by a reservoir of sub-clinical infections on the most populated island, Tahiti.</p>
Data from: Blockade of dengue virus transmission from viremic blood to Aedes aegypti mosquitoes using human monoclonal antibodies
Background <p class="CxSpFirst">Dengue is the most prevalent arboviral disease of humans. Virus neutralizing antibodies are likely to be critical for clinical immunity after vaccination or natural infection. A number of human monoclonal antibodies (mAbs) have previously been characterized as able to neutralize the infectivity of dengue virus (DENV) for mammalian cells in cell-culture systems.</p> <p class="CxSpLast"> </p> Methodology/Principle findings <p class="CxSpFirst">We tested the capacity of 12 human mAbs, each of which had previously been shown to neutralize DENV in cell-culture systems, to abrogate the infectiousness of dengue patient viremic blood for mosquitoes. Seven of the twelve mAbs (1F4, 14c10, 2D22, 1L12, 5J7, 747(4)B7, 753(3)C10), almost all of which target quaternary epitopes, inhibited DENV infection of <i>Ae. aegypti</i>. The mAbs 14c10, 747(4)B7 and 753(3)C10 could all inhibit transmission of DENV in low microgram per mL concentrations. An Fc-disabled variant of 14c10 was as potent as its parent mAb.</p> <p class="CxSpLast"> </p> Conclusions/Significance <p class="CxSpFirst">The results demonstrate that mAbs can neutralize infectious DENV derived from infected human cells, in the matrix of human blood. Coupled with previous evidence of their ability to prevent DENV infection of mammalian cells, such mAbs could be considered attractive antibody classes to elicit with dengue vaccines, or alternatively, for consideration as therapeutic candidates.</p>
Fig. 2. A in Genetic differentiation in populations of Aedes aegypti (Diptera, Culicidae) dengue vector from the Brazilian state of Maranhão
Fig. 2. A priori estimate of the probable groups of populations produced by the BAPS (Bayesian Analysis of Population Structure v 6.0) program, indicating a total of two groups.
Unraveling Dengue Serotype 3 Transmission in Brazil: Evidence for Multiple Introductions of the 3III_B.3.2 Lineage
<p>Dengue, caused by DENV 1-4, remains a global public health concern, with Brazil experiencing some of the largest epidemics. The reemergence of DENV-3 in Brazil between 2023 and 2024 has raised concerns about new outbreaks due to the absence of sustained circulation of this serotype in recent years. This study investigates the dynamics of DENV-3 in Brazil, focusing on the spread of the 3III_B.3.2 lineage within genotype 3III and its introduction routes. We analyzed 1,536 DENV-3 genomes, all classified as genotype 3III, the dominant DENV-3 genotype in Brazil since 2001. Phylogenetic analysis identified the 3III_B.3.2 lineage in all recent Brazilian cases, with detections also reported in Central America, the United States, and Europe. At least six independent introduction events of this lineage into Brazil were identified, with the Caribbean region and Costa Rica as the primary sources. The earliest introduction likely occurred in late 2022 in Roraima, followed by introductions in Sao Paulo, Minas Gerais, and Para. While one instance of interstate transmission was detected - from Sao Paulo to Minas Gerais - our findings indicate that external introductions, rather than domestic spread, were the primary drivers of DENV-3 circulation during this period. These results underscore the importance of continued genomic surveillance and coordinated public health strategies to monitor and mitigate future outbreaks</p>
Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing
<p>We produced a spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing from 2001-2024.</p> <p>GDP and building surface area are yearly data.</p> <p>PDSI is monthly data.</p>
Thermal adaptation in Aedes aegypti does not constrain temperature-sensitive growth of bacteria or dengue virus
<p>Data set and R script used for the following manuscript : </p> <p><strong>Thermal adaptation in <em>Aedes aegypti</em> does not constrain temperature-sensitive growth of bacteria or dengue virus</strong></p> <p><span lang="EN-US">Alida Kropf<sup>1*#</sup>, Stéphanie Dabo<sup>2</sup>, Marine Amann<sup>3</sup>, Louis Lambrechts<sup>2</sup>, Jacob C Koella<sup>1</sup></span></p> <p><span lang="EN-US">PROCEEDINGS OF THE ROYAL SOCIETY B THE ROYAL SOCIETY B BIOLOGICAL SCIENCES</span></p> <p><strong><em><span lang="IT-CH">DOI: 10.1098/rspb.2025-0832.R1 </span></em></strong></p>
Dengue incidence and climatic variables in Cali from 2015 to 2021
<p>In this work we studied the relationship between dengue incidence in Cali and the climatic variables that are known to have an impact on the mosquito and were available (precipitation, relative humidity, minimum, mean, and maximum temperature). Since the natural processes of the mosquito imply that any changes on climatic variables need some time to be visible on the dengue incidence, a lagged correlation analysis was done in order to choose the predictor variables of count regression models. A Principal Component Analysis was done to reduce dimensionality and study the correlation among the climatic variables. Finally, aiming to predict the monthly dengue incidence, three different regression models were constructed and compared using de Akaike information criterion. The best model was the negative binomial regression model, and the predictor variables were mean temperature with a 3-month lag and mean temperature with a 5-month lag as well as their interaction. The other variables were not significant on the models. And interesting conclusion was that according to the coefficients of the regression model, a 1°C increase in the monthly mean temperature will reflect as a 45% increase in dengue incidence after 3 months. The rises to a 64% increase after 5 months.</p>
Data for Seropositivity to Dengue virus DENV in three neighborhoods in the periphery of a city with a recent history of outbreaks in Argentina: what can we learn from unreported cases?
<p>This release include the datasets, R scripts and interactive maps generated for the manuscript: Seropositivity to Dengue virus DENV in three neighborhoods in the periphery of a city with a recent history of outbreaks in Argentina: what can we learn from unreported cases? Authors: DA Mendicino, T Ricardo, MA Cristaldi, M Maglianesi, G Guzmán S Claussen, RG Chiaraviglio, CA Ávalos, MA Previtali. (2024).</p>
Datos de control y seguimiento del dengue
<p>Datos utilizado en el proyecto "Control y seguimiento de la transmisión del dengue a través del mosquito tigre". Se incluyen los datos de todas las fases del proceso de limpieza y curación.</p>
Fig. 3 in Contrasting patterns of insecticide resistance and knockdown resistance (kdr) in Aedes aegypti populations from Jacarezinho (Brazil) after a Dengue Outbreak
Fig. 3. Allelic frequencies of 1016Val and 1016Ile in the Nav of A.aegypti populations from Jacarezinho in 2011 and 2012. Besides, the allelic frequencies of the Val1016Ile mutation by regions (Region I–IV) for 2012 are presented.
Fig. 2 in Contrasting patterns of insecticide resistance and knockdown resistance (kdr) in Aedes aegypti populations from Jacarezinho (Brazil) after a Dengue Outbreak
Fig. 2. Jacarezinho map shows the collection sites by regions in the urban area used for the analysis of the Val1016Ile mutation in 2012. Additionally, the main roads that cross Jacarezinho are presented.
Figure 6 in Indiscriminate ingestion of entomopathogenic nematodes and their symbiotic bacteria by Aedes aegypti larvae: a novel strategy to control the vector of Chikungunya, dengue and yellow fever
Figure 6. Aedes aegypti larval mortality when exposed to 1000 infective juveniles (IJs) of Heterorhabditis bacteriophora at different depths of water.
Figure 2 in Indiscriminate ingestion of entomopathogenic nematodes and their symbiotic bacteria by Aedes aegypti larvae: a novel strategy to control the vector of Chikungunya, dengue and yellow fever
Figure 2. Susceptibility of Aedes aegypti larvae to different species of EPN. Five 3rd instar larvae exposed to 1000 infective juveniles (IJs) and mortality assessed daily over 3-day period (DPI).
Figure 4 in Indiscriminate ingestion of entomopathogenic nematodes and their symbiotic bacteria by Aedes aegypti larvae: a novel strategy to control the vector of Chikungunya, dengue and yellow fever
Figure 4. Melanization of Heterorhabditis bacteriophora within Aedes aegypti larvae (3rd instar). A melanized H. bacteriophora within dead Ae. aegypti larvae (a), close up picture of melanized nematode upon larval dissection (b), nematodes representing different stages of melanization recovered from one dead Ae. aegypti larvae (c). Arrows indicate melanized nematode within Ae. aegypti larvae.
Figure 7 in Indiscriminate ingestion of entomopathogenic nematodes and their symbiotic bacteria by Aedes aegypti larvae: a novel strategy to control the vector of Chikungunya, dengue and yellow fever
Figure 7. Aedes aegypti larval mortality when exposed to supernatants and cell suspensions of Xenorhabdus nematophila (X. n.) and Photorhabdus laumondii (P. l.) in 24 well plates. Different uppercase or lower letters above error bars indicate statistical significance (Tukey's test p ≤ 0.05).
Figure 3 in Indiscriminate ingestion of entomopathogenic nematodes and their symbiotic bacteria by Aedes aegypti larvae: a novel strategy to control the vector of Chikungunya, dengue and yellow fever
Figure 3. Different stages of Heterorhabditis bacteriophora colonization of Aedes aegypti larvae (3rd instar). H. bacteriophora within larvae at 2-day post inoculation (a), H. bacteriophora emerging out of larvae upon larval dissection at 7-day post inoculation) (b), adult H. bacteriophora within larvae along with large number of infective juveniles (IJs) released from another adult H. bacteriophora (c). Black arrows indicate adult H. bacteriophora, whereas green arrows indicate newly emerged IJs.
Fig. 2 a in Circulating dengue virus serotypes and vertical transmission in AEdES larvae during outbreak and inter-outbreak seasons in a high dengue risk area of Sri Lanka
Fig. 2 a Distribution of dengue cases in the Kegalle District and Mawanella MOH area, Sri Lanka from December 2015 to March 2017. b Distribution of DENV serotypes in patients and distribution of Aedes mosquito larvae in and around the residences of dengue patients in Mawanella from December 2015 to March 2017. Abbreviations: DENV1, -2, -3, -4, DENV serotypes 1, 2, 3, 4
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International Brain Laboratory public data
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OpenNeuro
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