Dengue Incidence Research Articles

Artificial Intelligence Approach for Severe Dengue Early Warning System.

Dengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms ranging from mild flu-like symptoms to deadly complications. Dengue fever is one of the global burden diseases which annually have 50-100 million cases with 500,000 cases of severe dengue fever, of which 22,000 deaths occur mostly in children. Despite the discovery of vaccines, vector control is still the main approach for prevention efforts. Early detection and accessibility to medical care can reduce severe Dengue mortality rate from 50% to 2%. In the previous study, both statistical and machine learning methods have the potential for predicting a Dengue outbreak, but the study is still fragmented and limited on implementing the generated model into an early warning system application. In this study, we developed an artificial intelligence model with spatiotemporal to predict Dengue outbreak and Dengue incidence case which is ready to be implemented into an early warning system application. Indonesia, especially Semarang City, has experienced an endemic Dengue. We used Semarang City spatiotemporal, meteorological, climatological, and Dengue surveillance epidemiology data from January 2014 to December 2021 in 16 districts of Semarang City. We reviewed 7208 samples from 16 districts and 1 city per week during 8 years. The entire dataset was divided into training (80%) and testing (20%) to develop a prediction model. We used machine learning and Long Short Term Memory (LSTM) to predict Dengue outbreak 1 week before the event for each district. and machine learning to predict Dengue incident cases 1 week before the event for each district. Accuracy, area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score were considered to evaluate the Dengue outbreak prediction model. The Dengue incidence cases prediction model will evaluate using Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R2). Extra Trees Classifier model shown outperform in Dengue outbreak prediction, with accuracy 0.8925, AUROC 0. 9529, Recall 0.6117, precision 0.8880, and F1 score 0.7238. CatBoost Regressor model is shown to outperform in Dengue incidence cases prediction, with R2 0.5621, MAE 0.6304, MSE 1.1997, and RMSE 1.0891. The study proves that Artificial Intelligence (AI) with a spatiotemporal approach can give higher performance in Dengue outbreak and incidence cases prediction. Utilization of AI approaches that are sensitive with spatiotemporal feasibility to implement in Dengue early warning system application may contribute to increase the policy makers and community attention to do accurate community-based vector control.

Physical Environment Characteristics With The Presebce Of Aedes Larvae At The Ummul Mukminin Islamic Boarding School Makassar, Indonesia

Objective: The research aimed to know characteristics of the physical environment with the presence of Aedes larvae at the Ummul Mukminin Islamic Boarding School in Makassar City. Methods: The type of research used is descriptive, the sample number is 33 containers with exhaustive sampling as a sampling technique. The research instrument used was an observation sheet with univariate analysis using SPSS and Excel program. The study was conducted in June 2022 at the Ummul Mukminin Islamic Boarding School. Results: A total of 33 containers were found at the study site, 31 (93.9%) containers were not found to have larvae (negative) and 2 (6.1%) containers were positive for Aedes larvae. Plastic container base material with 100% presentation. There are 24 (72.7%) containers were water reservoir (TPA) and 9 (27,3%) containers were non-water reservoir, 27 (81,8%) containers were located indoor and 6 (18,2%) containers were located outdoor. Conclusions: The larva-free dormitory program by the school health post (poskestren) should be continued and if possible the frequency of activities is increased, as well as toilets that are rarely used and in dark conditions are still considered because they have the potential to be Aedes larva breeding place. Research Implications: Such findings are expected to be a source of information for the community, especially the teachers and students of the Ummul Mukminin Islamic Boarding School regarding the importance of maintaining the cleanliness of the mosquito breeding environment, especially regarding the presence of Aedes larvae and efforts to prevent dengue incidents. And can be an information material for the Makassar City Health Office, especially in overcoming dengue problems and can be a source of reference and comparison for further research. Originality / value: This study discusses some characteristics of water reservoirs (containers) that have the potential to become breeding sites for Aedes larvae, as one of the references in efforts to eradicate Aedes mosquito nests as vectors for dengue fever transmission.

Introduction of Aedes aegypti mosquitoes carrying wAlbB Wolbachia sharply decreases dengue incidence in disease hotspots

Partial replacement of resident Aedes aegypti mosquitoes with introduced mosquitoes carrying certain strains of inherited Wolbachia symbionts can result in transmission blocking of dengue and other viruses of public health importance. Wolbachia strain wAlbB is an effective transmission blocker and stable at high temperatures, making it particularly suitable for hot tropical climates. Following trial field releases in Malaysia, releases using wAlbB Ae. aegypti have become operationalized by the Malaysian health authorities. We report here on an average reduction in dengue fever of 62.4% (confidence intervals 50-71%) in 20 releases sites when compared to 76 control sites in high-rise residential areas. Importantly the level of dengue reduction increased with Wolbachia frequency, with 75.8% reduction (61-87%) estimated at 100% Wolbachia frequency. These findings indicate large impacts of wAlbB Wolbachia invasions on dengue fever incidence in an operational setting, with incidence expected to further decrease as wider areas are invaded.

Development of Data-driven Machine Learning Models and their Potential Role in Predicting Dengue outbreak.

Dengue fever is one of the most widespread vector-borne viral infections in the world, resulting in increased socio-economic burdens. The WHO has reported that 2.5 billion people are infected with dengue fever across the world, resulting in high mortalities in tropical and subtropical regions. The current article endeavors to present an overview of predicting dengue outbreaks through data-based machine-learning models. This artificial intelligence model uses real-world data such as dengue surveillance, climatic variables, and epidemiological data and combines big data with machine learning algorithms to forecast dengue. Monitoring and predicting dengue incidences have been significantly enhanced through innovative approaches. This involves gathering data on various climatic factors, including temperature, rainfall, relative humidity, and wind speed, along with monthly records of dengue cases. The study functions as an efficient warning system, enabling the anticipation of dengue outbreaks. This early warning system not only alerts communities but also aids relevant authorities in implementing crucial preventive measures.

Two-step spatiotemporal anomaly detection corrected for lag reporting time with application to real-time dengue surveillance in Thailand

BackgroundDengue infection ranges from asymptomatic to severe and life-threatening, with no specific treatment available. Vector control is crucial for interrupting its transmission cycle. Accurate estimation of outbreak timing and location is essential for efficient resource allocation. Timely and reliable notification systems are necessary to monitor dengue incidence, including spatial and temporal distributions, to detect outbreaks promptly and implement effective control measures.MethodsWe proposed an integrated two-step methodology for real-time spatiotemporal cluster detection, accounting for reporting delays. In the first step, we employed space-time nowcasting modeling to compensate for lags in the reporting system. Subsequently, anomaly detection methods were applied to assess adverse risks. To illustrate the effectiveness of these detection methods, we conducted a case study using weekly dengue surveillance data from Thailand.ResultsThe developed methodology demonstrated robust surveillance effectiveness. By combining space-time nowcasting modeling and anomaly detection, we achieved enhanced detection capabilities, accounting for reporting delays and identifying clusters of elevated risk in real-time. The case study in Thailand showcased the practical application of our methodology, enabling timely initiation of disease control activities.ConclusionOur integrated two-step methodology provides a valuable approach for real-time spatiotemporal cluster detection in dengue surveillance. By addressing reporting delays and incorporating anomaly detection, it complements existing surveillance systems and forecasting efforts. Implementing this methodology can facilitate the timely initiation of disease control activities, contributing to more effective prevention and control strategies for dengue in Thailand and potentially other regions facing similar challenges.

Influence of temperature and precipitation on dengue incidence in Campinas, São Paulo State, Brazil (2013-2022).

Global dengue cases are rising, notably in Brazil. By using monthly data, we estimated linear regressions with ARIMA errors to measure the influence of temperature and precipitation on dengue incidence in the city of Campinas, São Paulo State, Brazil. Findings suggest that a 1°C increase in mean temperature can lead to a cumulative increase of up to 40% in dengue incidence within 2 months. Precipitation shows no significant impact. Results highlight the importance of temperature on the spread of dengue and potentially other mosquito-borne diseases.

Socioeconomic and environmental factors associated with dengue fever incidence in Guatemala: Rising temperatures increase dengue risk.

Dengue fever is a mosquito-borne illness that infects 390 million people annually. Dengue outbreaks in Guatemala have been occurring more often and at increased rates since the first dengue outbreak in Guatemala in the 1970s. This study will examine environmental and socioeconomic factors associated with dengue in Guatemala at the municipality (county) level. Socioeconomic factors included population density, Mayan population, economic activity, and attending school. Environmental factors included average minimum annual temperature and annual precipitation. The relationship between environmental and socioeconomic variables and dengue fever incidence was initially evaluated through univariate zero-inflated negative binomial models, and then again through three zero-inflated multivariate negative binomial regression models. For all three models, elevation was considered a predictor of zero-inflation. In the combined model, there was a positive relationship between minimum temperature, economic activity and dengue fever incidence, and a negative relationship between population density, Mayan population and dengue fever. Predicted rates of dengue fever incidence and adjusted confidence intervals were calculated after increasing minimum yearly temperature by 1°C and 2°C. The three municipalities with the highest minimum yearly temperature (El Estor, Iztapa, and Panzós) and the municipality of Guatemala, all had an increase in the magnitude of the risk of dengue fever incidence following 1°C and 2°C increase in temperature. This research suggests that these socioeconomic and environmental factors are associated with risk of dengue in Guatemala. The predicted rates of dengue fever also highlight the potential effect that climate change in the form of increasing temperature can have on dengue in Guatemala.

Relationship between air temperature and dengue incidence: time series study in Minas Gerais, Brazil (2010-2019)

Air temperature is a climatic factor that affects the incidence of dengue, with effects varying according to time and space. We investigated the relationship between minimum air temperature and dengue incidence in Minas Gerais, Brazil, and evaluated the influence of socioeconomic and geographic variables on this relationship. This is a time series study with analysis conducted in three distinct stages: modeling using a distributed lag non-linear model, meta-analysis of models obtained, and meta-regression with geographic and socioeconomic data. Minimum temperature was a protective factor at extreme cold temperatures (RR = 0.65; 95%CI: 0.56-0.76) and moderate cold temperatures (RR = 0.71; 95%CI: 0.64-0.79), and a risk factor at moderate hot temperatures (RR = 1.15; 95%CI: 1.07-1.24), but not at extreme hot temperatures (RR = 1.1; 95%CI: 0.99-1.22). Heterogeneity of the models was high (I2 = 60%), which was also observed in meta-regression. Moderate and extreme cold temperatures have a protective effect, while moderate hot temperatures increase the risk. However, minimum air temperature does not explain the variability in the region, not even with the other variables in meta-regression.

Dengue is a product of the environment: an approach to the impacts of the environment on the Aedes aegypti mosquito and disease cases.

Dengue is an arbovirus infection whose etiologic agent is transmitted by the Aedes aegypti mosquito. Since the early 1980s, when the circulation of the dengue virus (DENV) was confirmed in Brazil, the disease has become a growing multifactorial public health problem. This article presented the main factors that have contributed to the frequent dengue epidemics in recent years, such as the behavior of the vector, climate change, and social, political, and economic aspects. The intersection between these different factors in the dynamics of the disease is highlighted, including the increase in the mosquito population due to higher temperatures and rainy periods, as well as the influence of socioeconomic conditions on the incidence of dengue. Some mosquito control strategies are also addressed, including the use of innovative technologies such as drones and the Wolbachia bacterium, as well as the hope represented by the dengue vaccine. Nevertheless, the need for integrated and effective public policies to reduce social inequalities and the impacts of climate change on the spread of dengue is emphasized.

Demographic-environmental effect on dengue outbreaks in 11 countries.

Dengue outbreaks are common in tropical or temperate countries, and climate change can exacerbate the problem by creating conditions conducive to the spread of mosquitoes and prolonging the transmission season. Warmer temperatures can allow mosquitoes to mature faster and increase their ability to spread disease. Additionally, changes in rainfall patterns can create more standing water, providing a breeding ground for mosquitoes. The objective of this study is to investigate the correlation between environmental and demographic factors and the dissemination of dengue fever. The study will use yearly data from 2000 to 2021 from 11 countries highly affected by dengue, considering multiple factors such as dengue cases, temperatures, precipitation, and population to better understand the impact of these variables on dengue transmission. In this research, Poisson regression (PR) and negative binomial regression (NBR) models are used to model count data and estimate the effect of different predictor variables on the outcome. Also, histogram plots and pairwise correlation plots are used to provide an initial overview of the distribution and relationship between the variables. Moreover, Goodness-of-fit tests, t-test analysis, diagnostic plots, influence plots, and residual vs. leverage plots are used to check the assumptions and validity of the models and identify any outliers or influential observations that may be affecting the results. The findings indicate that mean temperature and log(Urban) had a positive impact on dengue infection rates, while maximum temperature, log(Precipitation), and population density had a negative impact. However, minimum temperature, log(Rural), and log(Total population) did not demonstrate any significant effects on the incidence of dengue. The impact of demographic-environmental factors on dengue outbreaks in 11 Asian countries is illuminated by this study. The results highlight the significance of mean temperature (Tmean), maximum temperature (Tmax), log(Urban), log(Precipitation), and population density in influencing dengue incidence rates. However, further research is needed to gain a better understanding of the role of additional variables, such as immunity levels, awareness, and vector control measures, in the spread of dengue.

Global, regional, and national burden of dengue, 1990–2021: Findings from the global burden of disease study 2021

BackgroundDengue is an acute viral infectious disease caused by the dengue virus and transmitted through mosquitoes. Although numerous studies have examined the global burden of dengue, comprehensive and systematic global analyses remain limited. This study uses data from the Global Burden of Disease (GBD) Study 2021 to systematically analyze the global epidemiological trends and disease burden of dengue from 1990 to 2021. MethodsThis study utilized data from the GBD Study 2021 Study to analyze trends in the incidence, prevalence, mortality, and disability-adjusted life years (DALYs) of dengue across 204 countries and territories, stratified by age, sex, and Socio-demographic Index (SDI) levels. In addition, a Bayesian age-period-cohort model was employed to predict the future burden of dengue. The average annual percent change (AAPC) was used to describe the overall trend in rates or counts of indicators between 1990 and 2021. ResultsIn 2021, the global ASIR of dengue was 752.04 per 100,000 population (95 % UI: 196.33–1363.35), and ASMR was 0.38 per 100,000 population (95 % UI: 0.23–0.51). From 1990 to 2021, both the ASIR (AAPC = 7.89, 95 % CI: 7.89–8.91) and ASMR (AAPC = 0.01, 95 % CI: 0.00–0.01) showed an increasing trend. Adolescents under 14 years had the highest ASIR, age-standardized prevalence rate (ASPR), and age-standardized disability-adjusted life years (DALYs) for dengue. By 2035, the projected ASIR of dengue is 862.23 per 100,000 population (95 % CI: 627.84–1096.62 per 100,000 population), the ASPR is 51.60 per 100,000 population (95 % CI: 37.70–65.50 per 100,000 population), and the ASMR is 0.43 per 100,000 population (95 % CI: 0.29–0.56 per 100,000 population). The ASIR, ASPR, ASMR, and age-standardized DALYs for dengue are expected to continue rising in the next 10 years. ConclusionThe global burden of dengue is projected to continue rising in the coming years, underscoring the urgent need for comprehensive control measures, including enhanced vector control, public education, vaccination, and drug development. These findings provide crucial scientific evidence for the formulation of effective public health strategies and interventions aimed at reducing the global threat posed by dengue.

Outbreaks of dengue in India since 2012

Dengue is an arthropod - borne virus which belongs to Flaviviridae family cause a range of different illness from mild symptoms to severe deadly Dengue Hemorrhagic fever/Dengue shock syndrome. Dengue is a self-limiting disease. The sharp raise in population, inadequate steps to eradicate mosquito breeds and Climatic changes – Delayed monsoon and heavy rainfall are the most important factors that leads to the drastic increase of Dengue cases in India. Approximately 94198 cases and 91 deaths were reported in India. According to World Health Organization Dengue is considered as a major global public health challenge for tropical and subtropical nations. This article summarizes the State wise dengue burden, Prevalence and Incidence. This article also includes some Global strategies for dengue prevention and control.

Editorial

From Editor’s Desk… Public health officials and government officials around the globe are sounding the alarm about the unprecedented global spread of dengue fever, a notorious and potentially fatal disease. The risk of dengue fever is high enough to affect about half of the global population. The number of confirmed cases worldwide increased from half a million in 2000 to five and a half million in 2019, according to the World Health Organization (WHO). With reports of occurrences in 129 different countries, 2019 reached an unprecedented peak. While milder infections may just produce flu-like symptoms, more severe dengue infections can cause internal bleeding and potentially death. People infected with dengue can have a wide range of symptoms. A substantial number of dengue patients do not have any symptoms at all. The changing distribution of the vectors, especially Aedes aegypti and Aedes albopictus, is one of the many factors that the dengue outbreak is more likely to spread, especially in countries where dengue has never occurred before. More than five million cases and five thousand deaths have been caused by dengue since 2023. This has occurred in more than 80 countries and territories across five WHO regions: South-East Asia, Western Pacific, Eastern Mediterranean, Africa, and the Americas. These numbers likely do not represent the actual incidence of dengue because most primary infections do not cause any symptoms and reporting is not required in many nations. Dr. Khalid Mohammed Alabdulwahhab Editor in Chief

Analysis of Dengue and SARS‐CoV‐2 Coinfection in a Tertiary Care Hospital: A Retrospective Study

Introduction. Coinfection of dengue virus and SARS‐CoV‐2 infections in dengue‐endemic areas is a significant public health concern. Coinfections can result in severe illness. Hence, this study determines the incidence of dengue and COVID‐19 coinfection for a better understanding of the clinical presentation, laboratory parameters, and outcomes including mortality. Methods. The patients admitted to two tertiary hospitals with RT PCR‐proven COVID‐19 infection and dengue positive by NS1 rapid antigen or IgM dengue ELISA for two years between January 2020 and December 2022 were considered. Clinical data were retrieved from medical records including the laboratory findings and outcomes of these patients. The categorical data were analyzed in the form of frequency and proportion. The quantitative data were analyzed in the form of mean, median, and proportion. Results. Out of 2301 confirmed dengue samples and 3718 confirmed COVID‐19 samples, there were 14 cases of coinfection with the presence of COVID‐19 and dengue infection at the same time. ICU admission of 14.2% and mean hospital stay of 7 days were noted. Mainly the symptoms reported were fever at 92.9%, myalgia at 35.7%, and headache, vomiting, and cough at 28.6%. The laboratory findings were elevated lactate dehydrogenase and C‐reactive protein in 100% of patients, elevated ferritin in 92.9%, thrombocytopenia in 71.4%, elevated AST and ALT in 71.4%, and elevated D‐dimer in 57.1% of patients. There was no effect on morbidity and mortality seen among coinfection. Conclusion. COVID‐19 and dengue share similar clinical features and laboratory findings. Diagnosis of one disease cannot rule out the presence of other infections. There might be chances of misdiagnosis or missed diagnosis. Hence, it is important to stress about early detection using specific methods and confirmation of disease with timely management, as it is a potentially new dimension for public health concern and management.

Spatial disease mapping using the Poisson-Gamma model

In disease mapping, it is preferable to estimate the risk rather than the significance in general, but the variation in estimation precision across the geographical map of the study region must also be taken into consideration. In such a situation the conventional methods would not yield the best estimates. Heterogeneity is an important aspect to be considered as significant in Disease Mapping and relative risk estimation. The simple regression models often do not capture the extent of the variation exhibited in the spatial count data. This is the case when the spatial data is over-dispersed or there is spatial correlation due to unobserved confounders. In such situations, it is appropriate to include some additional terms, which may be in the form of the prior distribution. In this paper, a Poisson model with Gamma prior is used to model and map the dengue incidences in Tamil Nadu to explain the patterns of variations.

Robust spatial temporal analysis with improved geographically and temporally weighted regression model of dengue incidence rate

Robust spatial temporal analysis with improved geographically and temporally weighted regression model of dengue incidence rate

Dengue incidence forecasting model in Magalang, Pampanga using time series analysis

This study aimed to provide an Auto-Regressive Integrated Moving Average (ARIMA) model that can predict dengue incidence in Magalang, Pampanga based on the past dengue incidences & climate variables from 2016 to 2019 and identify significant predictor/predictors among the climate variables. Weekly dengue incidence and climatological data from January 2016 to December 2019 were gathered and considered as the dependent and independent variables, respectively. Then, the Expert Modeler in IBM SPSS 22 automatically selected and estimated the best fitting ARIMA model for one dependent variable series. The created model was then used to forecast dengue incidence for the 52-week period of 2020, employing quarterly, semi-annual, and annual forecasting. A one-way ANOVA was conducted to evaluate differences among the four groups, followed by a post hoc Scheffe test to determine which groups differed from each other. Additionally, the Diebold-Mariano (DM) test was utilized to validate the forecasting performance of the ARIMA model. The non-seasonal ARIMA (0,1,6) model, with the sum of rainfall as the sole predictor from seven candidate predictors, was identified as the best fit. Model fit statistics indicated a stationary R-squared value of 0.739. The Ljung-Box statistics test value of 0.344, deemed not significant, confirmed correct model specification. One-way ANOVA analysis revealed a significant difference among the four groups, with an F value of 34.37 and a p-value approaching zero. Post hoc Scheffe tests further indicated a significant difference between the observed dataset and both semi-annual and annual forecasting, with p-values of 0.014 and approaching zero, respectively. However, no significant difference was observed between the observed dataset and quarterly forecasting, with a p-value of 0.281. Moreover, DM test statistics revealed that quarterly forecasting outperformed both annual and semi-annual forecasting. This suggests that the non-seasonal ARIMA (0,1,6) model, with the sum of rainfall as a predictor, effectively predicts future dengue incidences using quarterly forecasting.

Silent spread of DENV-3 in Brazil: autochthonous outbreak in São Paulo after 15years.

The high incidence of dengue in Brazil and re-emergence of DENV-3 in São Paulo after a 15-year absence, coupled with the potential risk for DENV-4, may be the results from changing climate patterns favouring Aedes mosquito proliferation, allied by the resuming of domestic and international travels.

School and community driven dengue vector control and monitoring in Myanmar: Study protocol for a cluster randomized controlled trial.

Dengue is the most common and widespread mosquito-borne arboviral disease globally estimated to cause >390 million infections and >20,000 deaths annually. There are no effective preventive drugs and the newly introduced vaccines are not yet available. Control of dengue transmission still relies primarily on mosquito vector control. Although most vector control methods currently used by national dengue control programs may temporarily reduce mosquito populations, there is little evidence that they affect transmission. There is an urgent need for innovative, participatory, effective, and locally adapted approaches for sustainable vector control and monitoring in which students can be particularly relevant contributors and to demonstrate a clear link between vector reduction and dengue transmission reduction, using tools that are inexpensive and easy to use by local communities in a sustainable manner. Here we describe a cluster randomized controlled trial to be conducted in 46 school catchment areas in two townships in Yangon, Myanmar. The outcome measures are dengue cases confirmed by rapid diagnostic test in the townships, dengue incidence in schools, entomological indices, knowledge, attitudes and practice, behavior, and engagement. The trial involves middle school students that positions them to become actors in dengue knowledge transfer to their communities and take a leadership role in the delivery of vector control interventions and monitoring methods. Following this rationale, we believe that students can become change agents of decentralized vector surveillance and sustainable disease control in line with recent new paradigms in integrated and participatory vector surveillance and control. This provides an opportunity to operationalize transdisciplinary research towards sustainable health development. Due to the COVID-19 pandemic and political instability in Myanmar the project has been terminated by the donor, but the protocol will be helpful for potential future implementation of the project in Myanmar and/or elsewhere.Registration: This trial was registered in the ISRCTN Registry on 31 May 2022 ( https://doi.org/10.1186/ISRCTN78254298).

Arbovirus Detection of Adult Female <i>Aedes aegypti</i> for Dengue Surveillance: a Cohort Study in Bandung City, Indonesia

Dengue surveillance is an important activity to prevent dengue outbreaks. This activity becomes a significant challenge for the region with limited logistic capabilities. Developing a simple mathematical model to predict the possibility of dengue incidence provides a reliable early warning system. This study compared the correlation between vector (adult female Aedes aegypti) and arbovirus detection on a vector to dengue incidence, which generalized linear mixed models tested. The incidence of adult female Aedes aegypti and dengue fever cases were interpolated through third-power inverse distance weighting (IDW). A spatial correlation between female Aedes aegypti incidence and dengue incidence was obtained from polynomial regression. Collection sites were 16 villages in Bandung city, one of the significant dengue endemic areas in January–December 2017. A total of 8,402 mosquitoes of Aedes aegypti, Aedes albopictus, and Culex sp., with 17% belonging to Aedes aegypti as the subject of the dengue virus (DENV) infection test. Data analysis only showed a weak correlation between the numbers of adult female Aedes aegypti and dengue incidence. On the other hand, there is no correlation between positive dengue infection of vector and dengue incidence. This study highlights the importance of constant arbovirus surveillance and integrated surveillance methods on all possible dengue vectors to develop an early warning system for dengue incidence.