Dengue Surveillance Research Articles

Potential Way to Develop Dengue Virus Detection in Aedes Larvae as an Alternative for Dengue Active Surveillance: A Literature Review.

The burden of dengue has emerged as a serious public health issue due to its impact on morbidity, mortality, and economic burden. Existing surveillance systems are inadequate to provide the necessary data for the prompt and efficient control of dengue. Passive surveillance of dengue cases may lead to underreporting and delayed mitigation responses. Improved dengue control program requires sensitive and proactive methods for early detection of dengue. We collected and reviewed existing research articles worldwide on detecting dengue virus in Aedes species larvae. Searches were conducted in PUBMED and Google Scholar, including all the studies published in English and Bahasa Indonesia. Twenty-nine studies were included in this review in terms of assay used, positivity rate, and dengue serotype detected. The presence of dengue virus in immature mosquitoes was mostly detected using reverse transcription PCR (RT-PCR) in pooled larvae. In one study, dengue virus was detected in larvae from laboratory-infected mosquitoes using enzyme-linked immunosorbent assay (ELISA). The positivity rate of dengue virus detection ranged from 0 to 50% in field-caught larvae. Although various methods can detect the dengue virus, further research encourages the use of low-cost and less laborious methods for active surveillance of dengue in larvae.

Health System Preparedness, Response, and Incidence of Dengue in District Mianwali

Dengue is a mosquito-borne viral infection caused by the dengue virus. It has 4 different serotypes and is mainly spread by Aedes mosquitoes. The disease is a significant public health problem worldwide. Objective: Assess district health system preparedness for Dengue outbreaks, identify gaps, strengthen surveillance, and determine incidence post-preventive measures. Methods: The study was a mixed-method design, using both qualitative and quantitative approaches. A cross-sectional design was used to assess the incidence of dengue. Results: A total of 118 participants were selected for questionnaire-based interviews out of which 47.9% were male and 51.3% were females. The average age of the participants was 38.58 ± 9.63 with a minimum age of 24 years and minimum age of 55 years was observed. Overall 8.4% of the participants belonged to the Supervisor cadre, 5.9% were entomologists, 32.8% were CDC supervisors and 52.1% were clinical doctors. Most healthcare professionals find it easy to diagnose and report a case of dengue fever. Only 0.85% of the participants felt it very difficult to diagnose and report a dengue case, 45.76% felt somewhat easy and 44.07% felt very easy in reporting a dengue case. Some of the participants 9.32% neither felt it easy nor difficult to report dengue cases. Conclusions: Punjab, Pakistan's dengue surveillance system has improved but needs further enhancements in case detection, reporting, communication, and stakeholder collaboration. The province has established dengue monitoring units and implemented a comprehensive reporting system.

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.

Digital Health Interventions in Dengue Surveillance to Detect and Predict Outbreak: A Scoping Review

Background: Dengue fever is a global concern, with half of the population at risk. Digital Health Interventions (DHIs) have been widely used in Dengue surveillance. Objective: The objective of this review is to identify DHIs that have been used in Dengue surveillance. Methods: A systematic literature search was performed on three primary databases: PubMed, Scopus, and Google Scholar. A total of 2637 studies, including duplicates, were found to be possibly pertinent to the study topic during the electronic search for the systematic literature review. After the screening of titles and abstracts, 51 studies remained eligible. Results: The study analyzed 13 main categories of DHIs in Dengue surveillance, with Brazil, India, Sri Lanka, China, and Indonesia being the top five countries. Geographic Information System was the most used DHIs, followed by Machine Learning, Social Media, Mobile Applications, Google Trends, and Web Applications. DHIs were integrated, as evidenced by the deployment of many DHIs simultaneously in a single Dengue surveillance program. Conclusion: Future research should concentrate on finding more efficient ways to combine all available data sources and approaches to improve data completeness and predictive model precision and identify Dengue outbreaks early.

Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand

BackgroundDengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs.MethodsTo evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord Gi∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${G}_{i}^{*}$$\\end{document}, Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility.ResultsIn the simulation study, Getis Ord Gi∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${G}_{i}^{*}$$\\end{document} and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord Gi∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${G}_{i}^{*}$$\\end{document} and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies.ConclusionsBayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings.

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.

Detection of Zika Virus Antibodies in Retrospective Serum Samples from Suspected Dengue Cases in Sarawak, Malaysian Borneo

Sarawak, a state in Malaysian Borneo, has never reported a case of Zika virus (ZIKV) infection nor conducted any seroprevalence studies on the virus. This study aimed to provide a serological insight into ZIKV in Sarawak. 212 sera samples collected from a dengue surveillance study conducted from 2007 to 2011 were retrospectively analyzed. Samples negative for both dengue and Japanese encephalitis virus by RT-PCR were screened by immunoblots assay for ZIKV prM protein. Positive samples were further subjected to 50% plaque reduction neutralization test (PRNT50) for confirmation. 20 (9.4%) sera samples were positive for ZIKV prM protein but not against prM of dengue and Japanese encephalitis virus. These 20 samples were further subjected to PRNT50, and six samples (2.8%) showed possible seropositivity for ZIKV. This preliminary investigation provides serological evidence of ZIKV infection in Sarawak and highlights the importance of conducting surveillance programs for ZIKV in this dengue-endemic state.

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.

Support Vector Machine Outperforms Other Machine Learning Models in Early Diagnosis of Dengue Using Routine Clinical Data.

Background: There is a dire need for the establishment of active dengue surveillance to continuously detect cases, circulating serotypes, and determine the disease burden of dengue fever (DF) in the country and region. Predicting dengue PCR results using machine learning (ML) models represents a significant advancement in pre-emptive healthcare measures. This study outlines the comprehensive process of data preprocessing, model selection, and the underlying mechanisms of each algorithm employed to accurately predict dengue PCR outcomes. Methods: We analyzed data from 300 suspected dengue patients in Islamabad and Rawalpindi, Pakistan, from August to October 2023. NS1 antigen ELISA, IgM and IgG antibody tests, and serotype-specific real-time polymerase chain reaction (RT-PCR) were used to detect the dengue virus (DENV). Representative PCR-positive samples were sequenced by Sanger sequencing to confirm the circulation of various dengue serotypes. Demographic information, serological test results, and hematological parameters were used as inputs to the ML models, with the dengue PCR result serving as the output to be predicted. The models used were logistic regression, XGBoost, LightGBM, random forest, support vector machine (SVM), and CatBoost. Results: Of the 300 patients, 184 (61.33%) were PCR positive. Among the total positive cases detected by PCR, 9 (4.89%), 171 (92.93%), and 4 (2.17%) were infected with serotypes 1, 2, and 3, respectively. A total of 147 (79.89%) males and 37 (20.11%) females were infected, with a mean age of 33 ± 16 years. In addition, the mean platelet and leukocyte counts and the hematocrit percentages were 75,447%, 4189.02%, and 46.05%, respectively. The SVM was the best-performing ML model for predicting RT-PCR results, with 71.4% accuracy, 97.4% recall, and 71.6% precision. Hyperparameter tuning improved the recall to 100%. Conclusion: Our study documents three circulating serotypes in the capital territory of Pakistan and highlights that the SVM outperformed other models, potentially serving as a valuable tool in clinical settings to aid in the rapid diagnosis of DF.

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.

Effects of the Covid-19 pandemic on the actions of the dengue control program in a metropolitan area in the southeast region of Brazil

The COVID-19 pandemic represents a challenge for health systems, straining available material and personal resources, interfering with other surveillance and control programs. Dengue is epidemiologically the most relevant urban arbovirus of Latin America, being endemic in the metropolitan municipality of Contagem in Minas Gerais State, southeast region of Brazil. This is an ecological study that aimed to analyze the effects of the COVID-19 epidemic on dengue control and surveillance in Contagem in the year of 2020. In 2020, the municipality presented more dengue cases than expected for non-epidemic years, with a sharp decline from epidemiological week 11 onwards, coinciding with the increase in COVID-19 cases. There was also a reduction in vector control, with the national suspension of the second Survey of Rapid Infestation Index of Aedes aegypti and a significant decrease in the installation of oviposition traps, properties visited for monitoring and that received focal treatment. Our results and the difficulties observed in different instances of dengue control corroborate the hypothesis of weakening of the surveillance program and possible underreporting of dengue cases as a result of the COVID-19 emergency. KEY WORDS: COVID-19; dengue; epidemiology; syndemic; public health surveillance.

Health professionals' knowledge on dengue and health facility preparedness for case detection: A cross-sectional study in Dar es Salaam, Tanzania.

Dengue presents a growing public health concern in the Dar es Salaam region of Tanzania, marked by the recurring incidence of outbreaks. Unfortunately, there is little information available on the region's preparedness in terms of health care workers' knowledge on dengue as well as the availability of reagents and equipment essential for diagnosing and monitoring of dengue infections. To elucidate this, 78 health facilities were visited in Temeke district and structured questionnaires were distributed to 324 health care workers. The aim was to evaluate health care workers' knowledge on dengue and to assess the availability of reagents and equipment essential for diagnosing and monitoring of dengue infections. Content validity of the questionnaire was achieved through extensive literature review and it exhibited high reliability (Cronbach Alpha coefficient = 0.813). Cumulative scores for responses on knowledge questions by health care workers were computed. Characteristics such as level of education, place of work and gender were tested for association with these scores using chi-square tests and logistics regression. Almost all health care workers (99.7%) were aware of dengue disease. However, less than half (46.9%) had knowledge scores of or over 40%. Clinicians had approximately four times higher knowledge scores than other cadres (AOR, 3.637; p-value≤ 0.0001), and those who worked in private facilities had twice the knowledge score than those working in government institutions (AOR, 2.071; p-value = 0.007). Only 8.6%, 35.6% and 14.7% of respondents reported the availability of dengue rapid tests, medical guidelines and refresher training respectively, showing a lack of health facilities readiness for the detection of dengue infections. Based on findings from this study, we recommend government authorities to build capacity of health care workers, to improve their understanding of dengue. We also urge the government and stakeholders to work together to ensure availability of diagnostic tests and other tools needed for diagnosis and surveillance of dengue.

Utility of surveillance data for planning for dengue elimination in Yogyakarta, Indonesia: a scenario-tree modelling approach

IntroductionField trials and modelling studies suggest that elimination of dengue transmission may be possible through widespread release of Aedes aegypti mosquitoes infected with the insect bacterium Wolbachia pipientis (wMel strain),...

Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A wavelet approach.

Dengue transmission poses significant challenges for public health authorities worldwide due to its susceptibility to various factors, including environmental and climate variability, affecting its incidence and geographic spread. This study focuses on Costa Rica, a country characterized by diverse microclimates nearby, where dengue has been endemic since its introduction in 1993. Using wavelet coherence and clustering analysis, we performed a time-series analysis to uncover the intricate connections between climate, local environmental factors, and dengue occurrences. The findings indicate that multiannual dengue frequency (3 yr) is correlated with the Oceanic Niño Index and the Tropical North Atlantic Index. This association is particularly prominent in cantons located along the North and South Pacific Coast, as well as in the Central cantons of the country. Furthermore, the time series of these climate indices exhibit a leading phase of approximately nine months ahead of dengue cases. Additionally, the clustering analysis uncovers non-contiguous groups of cantons that exhibit similar correlation patterns, irrespective of their proximity or adjacency. This highlights the significance of climate factors in influencing dengue dynamics across diverse regions, regardless of spatial closeness or distance between them. On the other hand, the annual dengue frequency was correlated with local environmental indices. A persistent correlation between dengue cases and local environmental variables is observed over time in the North Pacific and the Central Region of the country's Northwest, with environmental factors leading by less than three months. These findings contribute to understanding dengue transmission's spatial and temporal dynamics in Costa Rica, highlighting the importance of climate and local environmental factors in dengue surveillance and control efforts.

Dengue 1 outbreak in Rosso, northern Senegal, October 2021: entomologic investigations.

Senegal has experienced periodic epidemics of dengue in urban areas with increased incidence in recent years. However, few data are available on the local ecology of the epidemic vectors. In October 2021, a dengue outbreak was reported in northern Senegal to the Institute Pasteur de Dakar. Entomologic investigations then were undertaken to identify the areas at risk of transmission and to identify the vector(s). Adult mosquitoes were collected indoors and outdoors at selected households, while containers with water were inspected for mosquito larvae. All the Aedes aegypti (L.) collected were tested for dengue virus NS1 protein using a rapid diagnostic test (RDT), and positive samples were confirmed by real-time RT-PCR. The qRT-PCR positive samples were subjected to whole genome sequencing using Nanopore technology. The majority of the larvae-positive containers (83.1%) were used for water storage. The Breteau and Container indices exceeded the WHO-recommended thresholds for the risk of dengue virus transmission except at 2 localities. Ae. aegypti, the only reputed dengue vector, was collected resting indoors as well as outdoors and biting during the day and night. The NS1 protein was detected in 22 mosquito pools, including one pool of females emerging from field-collected larvae. All NS1-positive results were confirmed by RT-PCR. Virus serotyping showed that the outbreak was caused by DENV-1. This study demonstrates the need for continuous control of adult and aquatic stages of Ae. aegypti to prevent future dengue epidemics in Senegal. RDTs appear to be a promising tool for dengue diagnostics and surveillance.

A strategy for entomo-virological surveillance of yellow fever, dengue, zika, and chikungunya viruses in field-collected mosquitoes

Arboviruses transmitted by Culicidae insects are significant threats to human health, presenting dynamic transmission cycles and involving different vectors and hosts. The surveillance and characterization of the vectors involved in these cycles are crucial for understanding and preventing potential outbreaks. Therefore, we propose a strategy that we used for entomological surveillance of urban, rural, and sylvatic mosquitoes and to characterize natural infection by four major arboviruses.•Immature and adult mosquitoes were collected intra, peri and extradomicilie of urban and rural households, using different collection methodologies.•Mosquitoes were pooled or separated in head-thorax and abdomen, according to the species.•A multiplex nested RT-PCR (Reverse transcription polymerase chain reaction) method was used for the simultaneous detection of dengue virus (DENV), zika virus (ZIKV), chikungunya virus (CHIKV), and yellow fever virus (YFV).Overall, this strategy proved helpful for vectors surveillance at different ecosystems, as well as for implementing a low-cost molecular surveillance system that allows the early detection of potential outbreaks, and identify other potential vectors involved in viral transmission.

National dengue surveillance, Cambodia 2002-2020.

Global dengue incidence has increased dramatically over the past few decades from approximately 500 000 reported cases in 2000 to over 5 million in 2019. This trend has been attributed to population growth in endemic areas, rapid unplanned urbanization, increasing global connectivity, and climate change expanding the geographic range of the Aedes spp. mosquito, among other factors. Reporting dengue surveillance data is key to understanding the scale of the problem, identifying important changes in the landscape of disease, and developing policies for clinical management, vector control and vaccine rollout. However, surveillance practices are not standardized, and data may be difficult to interpret particularly in low- and middle-income countries with fragmented health-care systems. The latest national dengue surveillance data for Cambodia was published in 2010. Since its publication, the country experienced marked changes in health policies, population demographics, climate and urbanization. How these changes affected dengue control remains unknown. In this article, we summarize two decades of policy changes, published literature, country statistics, and dengue case data collected by the Cambodia National Dengue Control Programme to: (i)identify important changes in the disease landscape; and (ii)derive lessons to inform future surveillance and disease control strategies. We report that while dengue case morbidity and mortality rates in Cambodia fell between 2002 and 2020, dengue incidence doubled and age at infection increased. Future national surveillance, disease prevention and treatment, and vector control policies will have to account for these changes to optimize disease control.

Frequent outbreaks of dengue in Nepal – causes and solutions: a narrative review

Dengue is a viral illness caused by dengue virus, a flavivirus transmitted by the bite of female Aedes aegypti and Aedes albopictus. In the year 2022, Nepal witnessed the largest ever dengue outbreak in the country. Dengue cases were reported from all seven provinces, affecting all 77 districts of the country. As of 31st December 2022, altogether 54 784 dengue cases have been identified, with Bagmati province reporting the highest. The country’s capital, Kathmandu, reported the highest burden of dengue cases (26%). The high degree of pre-monsoon rainfall because of climate change and mismanaged waste disposal system were the main cause of the 2022 dengue outbreak in Nepal. Furthermore, unplanned urbanization, Aedes vector expansion, open border policy with India, inefficient dengue surveillance system, and insufficient preventive efforts by the government are the causes of frequent dengue outbreaks. Awareness among the general public regarding vector control measures, improved surveillance of acute febrile illness, utilization of vector risk mapping techniques, and establishment of temperature-based early warning systems are utmost to halt future dengue outbreaks in Nepal.

Chemical Ecology and Management of Dengue Vectors.

Dengue, caused by the dengue virus, is the most widespread arboviral infectious disease of public health significance globally. This review explores the communicative function of olfactory cues that mediate host-seeking, egg-laying, plant-feeding, and mating behaviors in Aedes aegypti and Aedes albopictus, two mosquito vectors that drive dengue virus transmission. Aedes aegypti has adapted to live in close association with humans, preferentially feeding on them and laying eggs in human-fabricated water containers and natural habitats. In contrast, Ae. albopictus is considered opportunistic in its feeding habits and tends to inhabit more vegetative areas. Additionally, the ability of both mosquito species to locate suitable host plants for sugars and find mates for reproduction contributes to their survival. Advances in chemical ecology, functional genomics, and behavioral analyses have improved our understanding of the underlying neural mechanisms and reveal novel and specific olfactory semiochemicals that these species use to locate and discriminate among resources in their environment. Physiological status; learning; and host- and habitat-associated factors, including microbial infection and abundance, shape olfactory responses of these vectors. Some of these semiochemicals can be integrated into the toolbox for dengue surveillance and control.

Previous Dengue Infection among Children in Puerto Rico and Implications for Dengue Vaccine Implementation.

Limited dengue virus (DENV) seroprevalence estimates are available for Puerto Rico, which are needed to inform the potential use and cost-effectiveness of DENV vaccines. The Communities Organized to Prevent Arboviruses (COPA) is a cohort study initiated in 2018 in Ponce, Puerto Rico, to assess arboviral disease risk and provide a platform to evaluate interventions. We recruited participants from households in 38 study clusters, who were interviewed and provided a serum specimen. Specimens from 713 children aged 1 to 16 years during the first year of COPA were tested for the four DENV serotypes and ZIKV using a focus reduction neutralization assay. We assessed the seroprevalence of DENV and ZIKV by age and developed a catalytic model from seroprevalence and dengue surveillance data to estimate the force of infection for DENV during 2003-2018. Overall, 37% (n = 267) were seropositive for DENV; seroprevalence was 9% (11/128) among children aged 1 to 8 years and 44% (256/585) among children aged 9 to 16 years, exceeding the threshold over which DENV vaccination is deemed cost-effective. A total of 33% were seropositive for ZIKV, including 15% among children aged 0 to 8 years and 37% among children aged 9 to 16 years. The highest force of infection occurred in 2007, 2010, and 2012-2013, with low levels of transmission from 2016 to 2018. A higher proportion of children had evidence of multitypic DENV infection than expected, suggesting high heterogeneity in DENV risk in this setting.