Spatial and temporal dynamics of Aedes aegypti larval sites in Bello, Colombia

Counts of immature stages of the mosquito Aedes aegypti have been used to calculate several entomological indices of dengue vector abundance. Some studies have concluded that these indices can be used as indicators of dengue epidemic risk, while other studies have failed to find a predictive relationship. Ecological niche models have been able to predict distributional patterns in space and time, not only of vectors, but also of the diseases that they transmit. In this study, we used Landsat 7 ETM+ images and two niche-modeling algorithms to estimate the local-landscape ecological niche and the dynamics of Ae. aegypti larval habitats in Bello, Colombia, and to evaluate their potential spatial and temporal distribution. Our models showed low omission error with high confidence levels: about 13.4% of the area presents conditions consistently suitable for breeding across the entire study period (2002-2008). The proportion of neighborhoods predicted to be suitable showed a positive association with dengue case rates, whereas the vector-focused Bretau index had no relationship to case rates. As a consequence, niche models appear to offer a superior option for predictive evaluation of dengue transmission risk and anticipating the potential for outbreaks.     

Identification of Aedes albopictus larval index thresholds in the transmission of dengue in Guangzhou,China

Entomological indices have been used to quantitatively express vector density, but the threshold of larval indices of Aedes albopictus in dengue epidemics is still undefined. We conducted a case‐control study to identify the thresholds of Aedes albopictus larval indices in dengue epidemics. Two unit levels of analysis were used: district and street. The discriminative power of the indices was assessed by receiver operating characteristic (ROC) curves. The association between the entomologic indices and dengue transmission was further explored by a logistic regression model. At the district level, there was no significant difference in the Breteau index (BI) between districts that reported cases and those did not (t=0.164, p>0.05), but the Container index (CI) did show a significant difference (t=2.028, p<0.01). The AUC (Area Under the Curve) of BI, CI, and prediction value were 0.540, 0.630, and 0.533, respectively. Predicting at the street level, the AUC of BI, CI, and prediction values were 0.684, 0.660, and 0.685, respectively, and 0.861, 0.827, and 0.867 for outbreaks. BI=5.1, CI=5.4, or prediction value =0.491were suggested to control the epidemic efficiently with the fewest resources, where BI=4.0, CI=5.1, or PRE =0.483 were suggested to achieve effectiveness.     

An ensemble forecast system for tracking dynamics of dengue outbreaks and its validation in China

As a common vector-borne disease, dengue fever remains challenging to predict due to large variations in epidemic size across seasons driven by a number of factors including population susceptibility, mosquito density, meteorological conditions, geographical factors, and human mobility. An ensemble forecast system for dengue fever is first proposed that addresses the difficulty of predicting outbreaks with drastically different scales. The ensemble forecast system based on a susceptible-infected-recovered (SIR) type of compartmental model coupled with a data assimilation method called the ensemble adjusted Kalman filter (EAKF) is constructed to generate real-time forecasts of dengue fever spread dynamics. The model was informed by meteorological and mosquito density information to depict the transmission of dengue virus among human and mosquito populations, and generate predictions. To account for the dramatic variations of outbreak size in different seasons, the effective population size parameter that is sequentially updated to adjust the predicted outbreak scale is introduced into the model. Before optimizing the transmission model, we update the effective population size using the most recent observations and historical records so that the predicted outbreak size is dynamically adjusted. In the retrospective forecast of dengue outbreaks in Guangzhou, China during the 2011–2017 seasons, the proposed forecast model generates accurate projections of peak timing, peak intensity, and total incidence, outperforming a generalized additive model approach. The ensemble forecast system can be operated in real-time and inform control planning to reduce the burden of dengue fever.     

Natural,Persistent Oscillations in a Spatial Multi-Strain Disease System with Application to Dengue

Many infectious diseases are not maintained in a state of equilibrium but exhibit significant fluctuations in prevalence over time. For pathogens that consist of multiple antigenic types or strains, such as influenza, malaria or dengue, these fluctuations often take on the form of regular or irregular epidemic outbreaks in addition to oscillatory prevalence levels of the constituent strains. To explain the observed temporal dynamics and structuring in pathogen populations, epidemiological multi-strain models have commonly evoked strong immune interactions between strains as the predominant driver. Here, with specific reference to dengue, we show how spatially explicit, multi-strain systems can exhibit all of the described epidemiological dynamics even in the absence of immune competition. Instead, amplification of natural stochastic differences in disease transmission, can give rise to persistent oscillations comprising semi-regular epidemic outbreaks and sequential dominance of dengue''s four serotypes. Not only can this mechanism explain observed differences in serotype and disease distributions between neighbouring geographical areas, it also has important implications for inferring the nature and epidemiological consequences of immune mediated competition in multi-strain pathogen systems.     

Climate-based models for understanding and forecasting dengue epidemics

Background

Dengue dynamics are driven by complex interactions between human-hosts, mosquito-vectors and viruses that are influenced by environmental and climatic factors. The objectives of this study were to analyze and model the relationships between climate, Aedes aegypti vectors and dengue outbreaks in Noumea (New Caledonia), and to provide an early warning system.

Methodology/Principal Findings

Epidemiological and meteorological data were analyzed from 1971 to 2010 in Noumea. Entomological surveillance indices were available from March 2000 to December 2009. During epidemic years, the distribution of dengue cases was highly seasonal. The epidemic peak (March–April) lagged the warmest temperature by 1–2 months and was in phase with maximum precipitations, relative humidity and entomological indices. Significant inter-annual correlations were observed between the risk of outbreak and summertime temperature, precipitations or relative humidity but not ENSO. Climate-based multivariate non-linear models were developed to estimate the yearly risk of dengue outbreak in Noumea. The best explicative meteorological variables were the number of days with maximal temperature exceeding 32°C during January–February–March and the number of days with maximal relative humidity exceeding 95% during January. The best predictive variables were the maximal temperature in December and maximal relative humidity during October–November–December of the previous year. For a probability of dengue outbreak above 65% in leave-one-out cross validation, the explicative model predicted 94% of the epidemic years and 79% of the non epidemic years, and the predictive model 79% and 65%, respectively.

Conclusions/Significance

The epidemic dynamics of dengue in Noumea were essentially driven by climate during the last forty years. Specific conditions based on maximal temperature and relative humidity thresholds were determinant in outbreaks occurrence. Their persistence was also crucial. An operational model that will enable health authorities to anticipate the outbreak risk was successfully developed. Similar models may be developed to improve dengue management in other countries.     

Predicting dengue incidence leveraging internet-based data sources. A case study in 20 cities in Brazil

The dengue virus affects millions of people every year worldwide, causing large epidemic outbreaks that disrupt people’s lives and severely strain healthcare systems. In the absence of a reliable vaccine against dengue or an effective treatment to manage the illness in humans, most efforts to combat dengue infections have focused on preventing its vectors, mainly the Aedes aegypti mosquito, from flourishing across the world. These mosquito-control strategies need reliable disease activity surveillance systems to be deployed. Despite significant efforts to estimate dengue incidence using a variety of data sources and methods, little work has been done to understand the relative contribution of the different data sources to improved prediction. Additionally, scholarship on the topic had initially focused on prediction systems at the national- and state-levels, and much remains to be done at the finer spatial resolutions at which health policy interventions often occur. We develop a methodological framework to assess and compare dengue incidence estimates at the city level, and evaluate the performance of a collection of models on 20 different cities in Brazil. The data sources we use towards this end are weekly incidence counts from prior years (seasonal autoregressive terms), weekly-aggregated weather variables, and real-time internet search data. We find that both random forest-based models and LASSO regression-based models effectively leverage these multiple data sources to produce accurate predictions, and that while the performance between them is comparable on average, the former method produces fewer extreme outliers, and can thus be considered more robust. For real-time predictions that assume long delays (6–8 weeks) in the availability of epidemiological data, we find that real-time internet search data are the strongest predictors of dengue incidence, whereas for predictions that assume short delays (1–3 weeks), in which the error rate is halved (as measured by relative RMSE), short-term and seasonal autocorrelation are the dominant predictors. Despite the difficulties inherent to city-level prediction, our framework achieves meaningful and actionable estimates across cities with different demographic, geographic and epidemic characteristics.     

Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles

BackgroundWith enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare.Methods and findingsWe introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002–2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6–148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5–80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102–575) than those made with the baseline model (CRPS = 125, 95% CI 120–168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data.ConclusionsThis study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.     

Estimating absolute and relative case fatality ratios from infectious disease surveillance data

Knowing which populations are most at risk for severe outcomes from an emerging infectious disease is crucial in deciding the optimal allocation of resources during an outbreak response. The case fatality ratio (CFR) is the fraction of cases that die after contracting a disease. The relative CFR is the factor by which the case fatality in one group is greater or less than that in a second group. Incomplete reporting of the number of infected individuals, both recovered and dead, can lead to biased estimates of the CFR. We define conditions under which the CFR and the relative CFR are identifiable. Furthermore, we propose an estimator for the relative CFR that controls for time-varying reporting rates. We generalize our methods to account for elapsed time between infection and death. To demonstrate the new methodology, we use data from the 1918 influenza pandemic to estimate relative CFRs between counties in Maryland. A simulation study evaluates the performance of the methods in outbreak scenarios. An R software package makes the methods and data presented here freely available. Our work highlights the limitations and challenges associated with estimating absolute and relative CFRs in practice. However, in certain situations, the methods presented here can help identify vulnerable subpopulations early in an outbreak of an emerging pathogen such as pandemic influenza.     

A Novel Statistical Model to Estimate Host Genetic Effects Affecting Disease Transmission

There is increasing recognition that genetic diversity can affect the spread of diseases, potentially affecting plant and livestock disease control as well as the emergence of human disease outbreaks. Nevertheless, even though computational tools can guide the control of infectious diseases, few epidemiological models can simultaneously accommodate the inherent individual heterogeneity in multiple infectious disease traits influencing disease transmission, such as the frequently modeled propensity to become infected and infectivity, which describes the host ability to transmit the infection to susceptible individuals. Furthermore, current quantitative genetic models fail to fully capture the heritable variation in host infectivity, mainly because they cannot accommodate the nonlinear infection dynamics underlying epidemiological data. We present in this article a novel statistical model and an inference method to estimate genetic parameters associated with both host susceptibility and infectivity. Our methodology combines quantitative genetic models of social interactions with stochastic processes to model the random, nonlinear, and dynamic nature of infections and uses adaptive Bayesian computational techniques to estimate the model parameters. Results using simulated epidemic data show that our model can accurately estimate heritabilities and genetic risks not only of susceptibility but also of infectivity, therefore exploring a trait whose heritable variation is currently ignored in disease genetics and can greatly influence the spread of infectious diseases. Our proposed methodology offers potential impacts in areas such as livestock disease control through selective breeding and also in predicting and controlling the emergence of disease outbreaks in human populations.     

The Effects of Tertiary and Quaternary Infections on the Epidemiology of Dengue

The epidemiology of dengue is characterised by irregular epidemic outbreaks and desynchronised dynamics of its four co-circulating virus serotypes. Whilst infection by one serotype appears to convey life-long protection to homologous infection, it is believed to be a risk factor for severe disease manifestations upon secondary, heterologous infection due to the phenomenon of Antibody-Dependent Enhancement (ADE). Subsequent clinical infections are rarely reported and, since the majority of dengue infections are generally asymptomatic, it is not clear if and to what degree tertiary or quaternary infections contribute to dengue epidemiology. Here we investigate the effect of third and subsequent infections on the transmission dynamics of dengue and show that although the qualitative patterns are largely equivalent, the system more readily exhibits the desynchronised serotype oscillations and multi-annual epidemic outbreaks upon their inclusion. More importantly, permitting third and fourth infections significantly increases the force of infection without resorting to high basic reproductive numbers. Realistic age-prevalent patterns and seroconversion rates are therefore easier reconciled with a low value of dengue''s transmission potential if allowing for more than two infections; this should have important consequences for dengue control and intervention measures.     

HLA-A*01 allele: a risk factor for dengue haemorrhagic fever in Brazil's population

Severe forms of dengue, such as dengue haemorrhagic fever (DHF) and dengue shock syndrome, are examples of a complex pathogenic mechanism in which the virus, environment and host immune response interact. The influence of the host's genetic predisposition to susceptibility or resistance to infectious diseases has been evidenced in several studies. The association of the human leukocyte antigen gene (HLA) class I alleles with DHF susceptibility or resistance has been reported in ethnically and geographically distinct populations. Due to these ethnic and viral strain differences, associations occur in each population, independently with a specific allele, which most likely explains the associations of several alleles with DHF. As the potential role of HLA alleles in the progression of DHF in Brazilian patients remains unknown, we then identified HLA-A alleles in 67 patients with dengue fever and 42 with DHF from Rio de Janeiro, Brazil, selected from 2002-2008 by the sequence-based typing technique. Statistical analysis revealed an association between the HLA-A*01 allele and DHF [odds ratio (OR) = 2.7, p = 0.01], while analysis of the HLA-A*31 allele (OR = 0.5, p = 0.11) suggested a potential protective role in DHF that should be further investigated. This study provides evidence that HLA class I alleles might be important risk factors for DHF in Brazilian patients.     

The Prevalence and Endemic Nature of Dengue Infections in Guangdong,South China: An Epidemiological,Serological, and Etiological Study from 2005–2011

Objectives

Frequent outbreaks of dengue are considered to be associated with an increased risk for endemicity of the disease. The occurrence of a large number of indigenous dengue cases in consecutive years indicates the possibility of a changing dengue epidemic pattern in Guangdong, China.

Methods

To have a clear understanding of the current dengue epidemic, a retrospective study of epidemiological profile, serological response, and virological features of dengue infections from 2005–2011 was conducted. Case data were collected from the National Notifiable Infectious Diseases Reporting Network. Serum samples were collected and prepared for serological verification and etiological confirmation. Incidence, temporal and spatial distribution, and the clinical manifestation of dengue infections were analyzed. Pearson''s Chi-Square test was used to compare incidences between different age groups. A seroprevalence survey was implemented in local healthy inhabitants to obtain the overall positive rate for the specific immunoglobulin (Ig) G antibody against dengue virus (DENV).

Results

The overall annual incidence rate was 1.87/100000. A significant difference was found in age-specific incidence (Pearson''s Chi-Square value 498.008, P<0.001). Children under 5 years of age had the lowest incidence of 0.28/100000. The vast majority of cases presented with a mild manifestation typical to dengue fever. The overall seroprevalence of dengue IgG antibody in local populations was 2.43% (range 0.28%–5.42%). DENV-1 was the predominant serotype in circulation through the years, while all 4 serotypes were identified in indigenous patients from different outbreak localities since 2009.

Conclusions

A gradual change in the epidemic pattern of dengue infection has been observed in recent years in Guangdong. With the endemic nature of dengue infections, the transition from a monotypic to a multitypic circulation of dengue virus in the last several years will have an important bearing on the prevention and control of dengue in the province and in the neighboring districts.     

The spatiotemporal trajectory of a dengue epidemic in a medium-sized city

Understanding the transmission dynamics of infectious diseases is important to allow for improvements of control measures. To investigate the spatiotemporal pattern of an epidemic dengue occurred at a medium-sized city in the Northeast Region of Brazil in 2009, we conducted an ecological study of the notified dengue cases georeferenced according to epidemiological week (EW) and home address. Kernel density estimation and space-time interaction were analysed using the Knox method. The evolution of the epidemic was analysed using an animated projection technique. The dengue incidence was 6.918.7/100,000 inhabitants; the peak of the epidemic occurred from 8 February-1 March, EWs 6-9 (828.7/100,000 inhabitants). There were cases throughout the city and was identified space-time interaction. Three epicenters were responsible for spreading the disease in an expansion and relocation diffusion pattern. If the health services could detect in real time the epicenters and apply nimbly control measures, may possibly reduce the magnitude of dengue epidemics.     

Study of the relationship between Aedes (Stegomyia) aegypti egg and adult densities, dengue fever and climate in Mirassol, state of São Paulo, Brazil

The purpose of this study was to examine the relationship between Aedes aegypti egg and adult density indices, dengue fever and climate in Mirassol, state of S?o Paulo, Brazil, between November 2004-November 2005. Weekly collections of adults and eggs were made using, respectively, manual aspirators and oviposition traps that produced four entomological indices (positivity and average of females and eggs). Weekly incidence coefficients were calculated based on dengue cases. Each week, the data obtained from entomological indices were related to each other, dengue, and climate variables. The first index to show an association with dengue transmission was the female average, followed by female positivity and egg average. Egg positivity did not show a relationship with risk for dengue, but was sensitive to identifying the presence of the vector, principally in dry seasons. The relationship between climatic factors, the vector and the disease found in this study can be widely employed in planning and undertaking dengue surveillance and control activities, but it is a tool that has not been considered by the authorities responsible for controlling the disease. In fact, this relationship permits the use of information about climate for early detection of epidemics and for establishing more effective prevention strategies than currently exist.     

Re-assess Vector Indices Threshold as an Early Warning Tool for Predicting Dengue Epidemic in a Dengue Non-endemic Country

BackgroundDespite dengue dynamics being driven by complex interactions between human hosts, mosquito vectors and viruses that are influenced by climate factors, an operational model that will enable health authorities to anticipate the outbreak risk in a dengue non-endemic area has not been developed. The objectives of this study were to evaluate the temporal relationship between meteorological variables, entomological surveillance indices and confirmed dengue cases; and to establish the threshold for entomological surveillance indices including three mosquito larval indices [Breteau (BI), Container (CI) and House indices (HI)] and one adult index (AI) as an early warning tool for dengue epidemic.Conclusion/SignificanceThere was little evidence of quantifiable association among vector indices, meteorological factors and dengue transmission that could reliably be used for outbreak prediction. Our study here provided the proof-of-concept of how to search for the optimal model and determine the threshold for dengue epidemics. Since those factors used for prediction varied, depending on the ecology and herd immunity level under different geological areas, different thresholds may be developed for different countries using a similar structure of the two-stage model.     

Ovitrap surveillance of dengue vector mosquitoes in Bandung City,West Java Province,Indonesia

Larval surveillance is the central approach for monitoring dengue vector populations in Indonesia. However, traditional larval indices are ineffective for measuring mosquito population dynamics and predicting the dengue transmission risk. We conducted a 14-month ovitrap surveillance. Eggs and immature mosquitoes were collected on a weekly basis from an urban village of Bandung, namely Sekejati. Ovitrap-related indices, namely positive house index (PHI), ovitrap index (OI), and ovitrap density index (ODI), were generated and correlated with environmental variables, housing type (terraced or high-density housing), ovitrap placement location (indoor or outdoor; household or public place), and local dengue cases. Our results demonstrated that Aedes aegypti was significantly predominant compared with Aedes albopictus at each housing type and ovitrap placement location. Ovitrap placement locations and rainfall were the major factors contributing to variations in PHI, OI, and ODI, whereas the influences of housing type and temperature were subtle. Indoor site values were significantly positively correlated to outdoor sites’ values for both OI and ODI. OI and ODI values from households were best predicted with those from public places at 1- and 0-week lags, respectively. Weekly rainfall values at 4- and 3-week lags were the best predictors of OI and ODI for households and public places, respectively. Monthly mean PHI, OI, and ODI were significantly associated with local dengue cases. In conclusion, ovitrap may be an effective tool for monitoring the population dynamics of Aedes mosquitoes, predicting dengue outbreaks, and serving as an early indicator to initiate environmental clean-up. Ovitrap surveillance is easy for surveyors if they are tasked with a certain number of ovitraps at a designated area, unlike the existing larval surveillance methodology, which entails identifying potential breeding sites largely at the surveyors’ discretion. Ovitrap surveillance may reduce the influence of individual effort in larval surveillance that likely causes inconsistency in results.     

Temporal distribution of dengue virus serotypes in Colombian endemic area and dengue incidence. Re-introduction of dengue-3 associated to mild febrile illness and primary infection

We have investigated the temporal distribution of dengue (DEN) virus serotypes in the department (state) of Santander, Colombia, in relation to dengue incidence, infection pattern, and severity of disease. Viral isolation was attended on a total of 1452 acute serum samples collected each week from 1998 to 2004. The infection pattern was evaluated in 596 laboratory-positive dengue cases using an IgG ELISA, and PRNT test. The dengue incidence was documented by the local health authority. Predominance of DEN-1 in 1998 and DEN-3 re-introduction and predominance in 2001-2003 coincided with outbreaks. Predominance of DEN-2 in 2000-2001 coincided with more dengue hemorrhagic fever (DHF). DEN-4 was isolated in 2000-2001 and 2004 but was not predominant. There was an annual increase of primary dengue infections (from 13.7 to 81.4%) that correlated with frequency of DEN-3 (r = 0.83; P = 0.038). From the total number of primary dengue infections DEN-3 (81.3%) was the most frequent serotype. DHF was more frequent in DEN-2 infected patients than in DEN-3 infected patients: 27.5 vs 10.9% (P < 0.05). DEN-3 viruses belonged to subtype C (restriction site-specific-polymerase chain reaction) like viruses isolated in Sri-Lanka and other countries in the Americas. Our findings show the importance of continuous virological surveillance to identify the risk factors of dengue epidemics and severity.     

Dengue hemorrhagic fever epidemiology in Thailand: description and forecasting of epidemics

Despite the use of a variety of control strategies, dengue hemorrhagic fever (DHF) control is a major and permanent challenge for public health services in Thailand and in Southeast Asia. In order to improve the efficiency of DHF control in Thailand, these activities have to concentrate on areas and populations at higher risk, which implies early identification of higher incidence periods. A retrospective study of spatial and temporal variations of DHF incidence in all 73 provinces of Thailand (1983-1995) allowed discrimination between seasonal (endemic) transmission dependent on climatic variations and vector density and non-seasonal (epidemic) transmission, mainly due to the occurrence of a new virus serotype in a population with low immunity. To identify epidemic months, which appear significantly clustered, a significant deviation from the monthly average incidence was defined. The occurrence of two consecutive epidemic months in a given area has a high probability (P = 0.66) of being followed by a cluster of 2-18 epidemic months (average: 7.7 months). This observation is proposed as a warning of epidemic outbreak enabling an early launch of control activities. As an example, when this method is retrospectively applied to the studied period, 11,388 province months (73 provinces x 156 months), 579 epidemic outbreaks (5.1% of the total) are identified. Control activities can thus be improved through early management and prevention of the 308,636 supplementary cases occurring during epidemics (37.0% of the total recorded).     

Association of human leukocyte antigen DQ1 and dengue fever in a white Southern Brazilian population

Dengue is an infectious disease of viral etiology transmitted by the mosquitoes Aedes aegypti, A. albopictus, and A. scutellaris. It can develop either as a benign form or as a severe hemorrhagic form. Previous work showed an association of the hemorrhagic form with human leukocyte antigens (HLA), suggesting a role of genetic factors in disease susceptibility. Nevertheless, data on HLA association with the classical form of the disease is scarce in literature. Sixty-four patients and 667 normal individuals, living in the state of Parana, Southern Brazil, were used as test and control group, respectively. The patients developed the disease during a virus 1 dengue outbreak either in Maringa city in 1995 (47) or in Paranavai city in 1999 (17). The diagnostic was confirmed through serology and/or viral culture. HLA class I and II typing was performed by the classical microlynfocitotoxicity test using monoclonal antisera and fluorobeads. Qui-square statistical analysis confirmed a positive association with HLA-DQ1 (76.6% vs 57.7%; p = 0.005243; pc = 0.026215). HLA-DR1 also presented an increased frequency in the test group, not statistically significant after p correction though (32.8% vs 15.9%; p = 0.005729; pc = 0.080206). In conclusion, genetic factors may play a role on the susceptibility to the classical dengue, virus 1, in the Brazilian population. Further independent studies should be performed in the Brazilian population to confirm these preliminary data.