Dengue disease surveillance: an updated systematic literature review

Microclimate factors related to dengue virus burden clusters in two endemic towns of Mexico

In dengue-endemic areas, transmission control is limited by the difficulty of achieving sufficient coverage and sustainability of interventions. To maximize the effectiveness of interventions, areas with higher transmission could be identified and prioritized. The aim was to identify burden clusters of Dengue virus (DENV) infection and evaluate their association with microclimatic factors in two endemic towns from southern Mexico. Information from a prospective population cohort study (2·5 years of follow-up) was used, microclimatic variables were calculated from satellite information, and a cross-sectional design was conducted to evaluate the relationship between the outcome and microclimatic variables in the five surveys. Spatial clustering was observed in specific geographic areas at different periods. Both, land surface temperature (aPR 0·945; IC95% 0·895-0·996) and soil humidity (aPR 3·018; IC95% 1·013-8·994), were independently associated with DENV burden clusters. These findings can help health authorities design focused dengue surveillance and control activities in dengue endemic areas.

Optimising the surveillance of Aedes aegypti in Brazil by selecting smaller representative areas within an endemic city

OBJECTIVES

Arboviruses, such as dengue (DENV), zika (ZIKV), and chikungunya (CHIKV), constitute a growing urban public health threat. Focusing on Aedes aegypti mosquitoes, their primary vectors, is crucial for mitigation. While traditional immature-stage mosquito surveillance has limitations, capturing adult mosquitoes through traps yields more accurate data on disease transmission. However, deploying traps presents logistical and financial challenges, demonstrating effective temporal predictions but lacking spatial accuracy. Our goal is to identify smaller representative areas within cities to enhance the early warning system for DENV outbreaks.

METHODS

We created Sentinel Geographic Units (SGUs), smaller areas of 1 km2 within each stratum, larger areas, with the aim of aligning the Trap Positivity Index (TPI) and Adult Density Index (ADI) with their respective strata. We conducted a two-step evaluation of SGUs. First, we examined the equivalence of TPI and ADI between SGUs and strata from January 2017 to July 2022. Second, we assessed the ability of SGU's TPI and ADI to predict DENV outbreaks in comparison to Foz do Iguaçu's Early-Warning System, which forecasts outbreaks up to 4 weeks ahead. Spatial and temporal analyses were carried out, including data interpolation and model selection based on Akaike information criteria (AIC).

RESULTS

Entomological indicators produced in small SGUs can effectively replace larger sentinel areas to access dengue outbreaks. Based on historical data, the best predictive capability is achieved 2 weeks after infestation verification. Implementing the SGU strategy with more frequent sampling can provide more precise space-time estimates and enhance dengue control.

CONCLUSIONS

The implementation of SGUs offers an efficient way to monitor mosquito populations, reducing the need for extensive resources. This approach has the potential to improve dengue transmission management and enhance the public health response in endemic cities.

Spatial analysis of dengue transmission in an endemic city in Brazil reveals high spatial structuring on local dengue transmission dynamics

In the last decades, dengue has become one of the most widespread mosquito-borne arboviruses in the world, with an increasing incidence in tropical and temperate regions. The mosquito Aedes aegypti is the dengue primary vector and is more abundant in highly urbanized areas. Traditional vector control methods have showing limited efficacy in sustaining mosquito population at low levels to prevent dengue virus outbreaks. Considering disease transmission is not evenly distributed in the territory, one perspective to enhance vector control efficacy relies on identifying the areas that concentrate arbovirus transmission within an endemic city, i.e., the hotspots. Herein, we used a 13-month timescale during the SARS-Cov-2 pandemic and its forced reduction in human mobility and social isolation to investigate the spatiotemporal association between dengue transmission in children and entomological indexes based on adult Ae. aegypti trapping. Dengue cases and the indexes Trap Positive Index (TPI) and Adult Density Index (ADI) varied seasonally, as expected: more than 51% of cases were notified on the first 2 months of the study, and higher infestation was observed in warmer months. The Moran's Eigenvector Maps (MEM) and Generalized Linear Models (GLM) revealed a strong large-scale spatial structuring in the positive dengue cases, with an unexpected negative correlation between dengue transmission and ADI. Overall, the global model and the purely spatial model presented a better fit to data. Our results show high spatial structure and low correlation between entomological and epidemiological data in Foz do Iguaçu dengue transmission dynamics, suggesting the role of human mobility might be overestimated and that other factors not evaluated herein could be playing a significant role in governing dengue transmission.

A global dataset of publicly available dengue case count data

OpenDengue is a global database of dengue case data collated from public sources and standardised and formatted to facilitate easy reanalysis. Dataset version 1.2 of this database contains information on over 56 million dengue cases from 102 countries between 1924 and 2023, making it the largest and most comprehensive dengue case database currently available. Over 95% of records are at the weekly or monthly temporal resolution and subnational data is available for 40 countries. To build OpenDengue we systematically searched databases, ministry of health websites, peer reviewed literature and Pro-MED mail reports and extracted denominator-based case count data. We undertake standardisation and error checking protocols to ensure consistency and resolve discrepancies. We meticulously documented the extraction process to ensure records are attributable and reproducible. The OpenDengue database remains under development with plans for further disaggregation and user contributions are encouraged. This new dataset can be used to better understand the long-term drivers of dengue transmission, improve estimates of disease burden, targeting and evaluation of interventions and improving future projections.

Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review

BACKGROUND

Creating a spatial model of dengue fever risk is challenging duet to many interrelated factors that could affect dengue. Therefore, it is crucial to understand how these critical factors interact and to create reliable predictive models that can be used to mitigate and control the spread of dengue.

METHODS

This scoping review aims to provide a comprehensive overview of the important predictors, and spatial modelling tools capable of producing Dengue Haemorrhagic Fever (DHF) risk maps. We conducted a methodical exploration utilizing diverse sources, i.e., PubMed, Scopus, Science Direct, and Google Scholar. The following data were extracted from articles published between January 2011 to August 2022: country, region, administrative level, type of scale, spatial model, dengue data use, and categories of predictors. Applying the eligibility criteria, 45 out of 1,349 articles were selected.

RESULTS

A variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and machine learning technique. We found that there was no pattern of predictor use associated with particular approaches. Instead, a wide range of predictors was used to create the DHF risk maps. These predictors may include climatology factors (e.g., temperature, rainfall, humidity), epidemiological factors (population, demographics, socio-economic, previous DHF cases), environmental factors (land-use, elevation), and relevant factors.

CONCLUSIONS

DHF risk spatial models are useful tools for detecting high-risk locations and driving proactive public health initiatives. Relying on geographical and environmental elements, these models ignored the impact of human behaviour and social dynamics. To improve the prediction accuracy, there is a need for a more comprehensive approach to understand DHF transmission dynamics.

Entomo-virological surveillance followed by serological active survey of symptomatic individuals is helpful to identify hotspots of early arbovirus transmission

Arboviruses transmitted by Aedes aegypti in urban environments have spread rapidly worldwide, causing great impacts on public health. The development of reliable and timely alert signals is among the most important steps in designing accurate surveillance systems for vector-borne diseases. In July and September 2017, we conducted a pilot study to improve an existing integrated surveillance system by using entomo-virological surveillance to prioritize areas to conduct active searches for individuals with arbovirus infection symptoms. Foz do Iguaçu City has a permanent entomo-virological surveillance system with approximately 3,500 traps to capture Aedes sp. in the adult stage. The Aedes aegypti females are captured alive and human samples are submitted to RT-qPCR (real-time qPCR) screening for DENV, ZIKV, and CHIKV diagnosis. Of the 55 Ae. aegypti mosquitoes tested in July 2017, seven (12.7%) were considered positive for DENV-2 and three (5.4%) for CHIKV. In September, we tested a sample of 54 mosquitoes, and 15 (27.7%) were considered infected by DENV-2. We created 25 circumferences with 150-m radius each to perform an active survey to identify symptomatic householders. In July, we selected one circumference, and five (35.7%) patients were positive for DENV, whereas two (14.3%) for CHIKV. In September, we selected four circumferences, and, from the 21 individuals sampled, nine (42.8%) were positive for DENV-2. A statistical model with a binomial response was used to estimate the number of cases in areas without active surveys, i.e., 20 circumferences. We estimated an additional 83 symptomatic patients (95% CI: 45-145) to be found in active searches, with 38 (95% CI: 18-72) of them confirming arbovirus infection. Arbovirus detection and serotyping in mosquitoes, but also in symptomatic individuals during active surveys, can provide an alert signal of early arbovirus transmission.

A Privacy-Preserved Internet-of-Medical-Things Scheme for Eradication and Control of Dengue Using UAV

Dengue is a mosquito-borne viral infection, found in tropical and sub-tropical climates worldwide, mostly in urban and semi-urban areas. Countries like Pakistan receive heavy rains annually resulting in floods in urban cities due to poor drainage systems. Currently, different cities of Pakistan are at high risk of dengue outbreaks, as multiple dengue cases have been reported due to poor flood control and drainage systems. After heavy rain in urban areas, mosquitoes are provided with a favorable environment for their breeding and transmission through stagnant water due to poor maintenance of the drainage system. The history of the dengue virus in Pakistan shows that there is a closed relationship between dengue outbreaks and a rainfall. There is no specific treatment for dengue; however, the outbreak can be controlled through internet of medical things (IoMT). In this paper, we propose a novel privacy-preserved IoMT model to control dengue virus outbreaks by tracking dengue virus-infected patients based on bedding location extracted using call data record analysis (CDRA). Once the bedding location of the patient is identified, then the actual infected spot can be easily located by using geographic information system mapping. Once the targeted spots are identified, then it is very easy to eliminate the dengue by spraying the affected areas with the help of unmanned aerial vehicles (UAVs). The proposed model identifies the targeted spots up to 100%, based on the bedding location of the patient using CDRA.

Citywide Integrated Aedes aegypti Mosquito Surveillance as Early Warning System for Arbovirus Transmission, Brazil

Infestation indices based on adult trapping predicted dengue outbreaks better than larval indices did.

Arbovirus epidemiology lacks efficient and timely surveillance systems with accurate outbreak alert signals. We devised a citywide integrated surveillance system combining entomologic, epidemiologic, and entomo-virologic data gathered during 2017–2020 in Foz do Iguaçu, Brazil. We installed 3,476 adult mosquito traps across the city and inspected traps every 2 months. We compared 5 entomologic indices: traditional house and Breteau indices for larval surveys and trap positivity, adult density, and mosquitoes per inhabitant indices for adult trapping. We screened for dengue, Zika, and chikungunya viruses in live adult Aedes aegypti mosquitoes collected from traps. Indices based on adult mosquito sampling had higher outbreak predictive values than larval indices, and we were able to build choropleth maps of infestation levels <36 h after each round of trap inspection. Locating naturally infected vectors provides a timely support tool for local public health managers to prioritize areas for intervention response to prevent virus outbreaks.

Constructing and validating a transferable epidemic risk index in data scarce environments using open data: A case study for dengue in the Philippines

Epidemics are among the most costly and destructive natural hazards globally. To reduce the impacts of infectious disease outbreaks, the development of a risk index for infectious diseases can be effective, by shifting infectious disease control from emergency response to early detection and prevention.

In this study, we introduce a methodology to construct and validate an epidemic risk index using only open data, with a specific focus on scalability. The external validation of our risk index makes use of distance sampling to correct for underreporting of infections, which is often a major source of biases, based on geographical accessibility to health facilities. We apply this methodology to assess the risk of dengue in the Philippines.

The results show that the computed dengue risk correlates well with standard epidemiological metrics, i.e. dengue incidence (p = 0.002). Here, dengue risk constitutes of the two dimensions susceptibility and exposure. Susceptibility was particularly associated with dengue incidence (p = 0.048) and dengue case fatality rate (CFR) (p = 0.029). Exposure had lower correlations to dengue incidence (p = 0.193) and CFR (p = 0.162). Highest risk indices were seen in the south of the country, mainly among regions with relatively high susceptibility to dengue outbreaks.

Our findings reflect that the modelled epidemic risk index is a strong indication of sub-national dengue disease patterns and has therefore proven suitability for disease risk assessments in the absence of timely epidemiological data. The presented methodology enables the construction of a practical, evidence-based tool to support public health and humanitarian decision-making processes with simple, understandable metrics. The index overcomes the main limitations of existing indices in terms of construction and actionability.

Epidemics are among the most costly and destructive natural hazards occurring globally; currently, the response to epidemics is still focused on reaction rather than prevention or preparedness. The development of an epidemic risk index can support identifying high-risk areas and can guide prioritization of preventive action and humanitarian response. While several frameworks for epidemic risk assessment exist, they suffer from several limitations, which resulted in limited uptake by local health actors—such as governments and humanitarian relief workers—in their decision-making processes. In this study, we present a methodology to develop epidemic risk indices, which overcomes the major limitations of previous work: strict data requirements, insufficient geographical granularity, validation against epidemiological data. We take as a case study dengue in the Philippines and develop an epidemic risk index; we correct dengue incidence for underreporting based on accessibility to healthcare and show that it correlates well with the risk index (Pearson correlation coefficient 0.69, p-value 0.002). Our methodology enables the development of disease-specific epidemic risk indices at a sub-national level, even in countries with limited data availability; these indices can guide local actors in programming prevention and response activities. Our findings on the case study show that the epidemic risk index is a strong indicator of sub-national dengue disease patterns and is therefore suitable for disease risk assessments in the absence of timely and complete epidemiological data.

Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review

Background

Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes.

Methodology/Principal findings

We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-related outcome (55%), assess the validity of data sources for dengue surveillance (23%), or both (22%). Most studies (60%) used a machine learning approach. Studies on dengue prediction compared different prediction models, or identified significant predictors among several covariates in a model. The most significant predictors were rainfall (43%), temperature (41%), and humidity (25%). The two models with the highest performances were Neural Networks and Decision Trees (52%), followed by Support Vector Machine (17%). We cannot rule out a selection bias in our study because of our two main limitations: we did not include preprints and could not obtain the opinion of other international experts.

Conclusions/Significance

Combining real-world data and Big Data with machine learning methods is a promising approach to improve dengue prediction and monitoring. Future studies should focus on how to better integrate all available data sources and methods to improve the response and dengue management by stakeholders.

Dengue is one of the most important arbovirus infections in the world and its public health, societal and economic burden is increasing. Although the majority of dengue cases are asymptomatic or mild, severe disease forms can lead to death. For this reason, early diagnosis and monitoring of dengue are crucial to decrease mortality. However, most endemic regions still rely on traditional monitoring methods, despite the growing availability of novel data sources and data-driven methods based on real-world data, Big Data, and machine learning algorithms. In this systematic review, we identified and analyzed studies that used these novel approaches for dengue monitoring and/or prediction. We found that novel data streams, such as Internet search engines and social media platforms, and machine learning methods can be successfully used to improve dengue management, but are still vastly ignored in real life. These approaches should be combined with traditional methods to help stakeholders better prepare for each outbreak and improve early responsiveness.

Assessing the burden of dengue among household members in Alaminos, Laguna, the Philippines: a prospective cohort study

Background

The incidence of dengue is increasing rapidly and is a challenging health issue in the Philippines. Epidemiological data are largely based on a passive-surveillance reporting system, which leads to substantial under-reporting of cases.

Objectives

To estimate dengue infection and disease incidence prospectively at the community level in an endemic area of the Philippines using an active surveillance strategy.

Methods

We implemented active surveillance in the highly endemic community of Alaminos, Laguna. The study consisted of a 1-year follow-up with 2 visits scheduled at the start and end of the study, as well as regular active surveillance in between and unscheduled visits for suspected cases. Blood samples were collected and analyzed to detect dengue during the first scheduled visit and all unscheduled visits, and clinical examination was performed at all visits (registered at clinicaltrials.gov NCT02766088).

Results

We enrolled 500 participants, aged from 6 months to 50 years; 76.2% were found positive for immunoglobulin G (95% confidence interval [CI], 71.9-80.0), with 92.0% among those aged 9-17 years. Active (weekly) surveillance identified 4 virologically confirmed cases of dengue (incidence proportion 0.8; 95% CI 0.3-2.1); all in participants aged ≤14 years.

Conclusions

Routine surveillance programs such as sentinel sites are needed to characterize the entire clinical spectrum of symptomatic dengue, disease incidence, and transmission in the community.

Emerging epidemics: is the Zanzibar healthcare system ready to detect and respond to mosquito-borne viral diseases?

Background

Effective control of emerging mosquito-borne viral diseases such as dengue, chikungunya, and Zika requires, amongst other things, a functional healthcare system, ready and capable of timely detection and prompt response to incipient epidemics. We assessed the readiness of Zanzibar health facilities and districts for early detection and management of mosquito-borne viral disease outbreaks.

Methods

A cross-sectional study involving all 10 District Health Management Teams and 45 randomly selected public and private health facilities in Zanzibar was conducted using a mixed-methods approach including observations, document review, and structured interviews with health facility in-charges and District Health Management Team members.

Results

The readiness of the Zanzibar healthcare system for timely detection, management, and control of dengue and other mosquito-borne viral disease outbreaks was critically low. The majority of health facilities and districts lacked the necessary requirements including standard guidelines, trained staff, real-time data capture, analysis and reporting systems, as well as laboratory diagnostic capacity. In addition, health education programmes for creating public awareness and Aedes mosquito surveillance and control activities were non-existent.

Conclusions

The Zanzibar healthcare system has limited readiness for management, and control of mosquito-borne viral diseases. In light of impending epidemics, the critical shortage of skilled human resource, lack of guidelines, lack of effective disease and vector surveillance and control measures as well as lack of laboratory capacity at all levels of health facilities require urgent attention across the Zanzibar archipelago.

Case-area targeted interventions (CATI) for reactive dengue control: Modelling effectiveness of vector control and prophylactic drugs in Singapore

BACKGROUND

Targeting interventions to areas that have recently experienced cases of disease is one strategy to contain outbreaks of infectious disease. Such case-area targeted interventions (CATI) have become an increasingly popular approach for dengue control but there is little evidence to suggest how precisely targeted or how recent cases need to be, to mount an effective response. The growing interest in the development of prophylactic and therapeutic drugs for dengue has also given new relevance for CATI strategies to interrupt transmission or deliver early treatment.

METHODS/PRINCIPAL FINDINGS

Here we develop a patch-based mathematical model of spatial dengue spread and fit it to spatiotemporal datasets from Singapore. Simulations from this model suggest CATI strategies could be effective, particularly if used in lower density areas. To maximise effectiveness, increasing the size of the radius around an index case should be prioritised even if it results in delays in the intervention being applied. This is partially because large intervention radii ensure individuals receive multiple and regular rounds of drug dosing or vector control, and thus boost overall coverage. Given equivalent efficacy, CATIs using prophylactic drugs are predicted to be more effective than adult mosquito-killing vector control methods and may even offer the possibility of interrupting individual chains of transmission if rapidly deployed. CATI strategies quickly lose their effectiveness if baseline transmission increases or case detection rates fall.

CONCLUSIONS/SIGNIFICANCE

These results suggest CATI strategies can play an important role in dengue control but are likely to be most relevant for low transmission areas where high coverage of other non-reactive interventions already exists. Controlled field trials are needed to assess the field efficacy and practical constraints of large operational CATI strategies.

Temperature, traveling, slums, and housing drive dengue transmission in a non-endemic metropolis

Dengue is steadily increasing worldwide and expanding into higher latitudes. Current non-endemic areas are prone to become endemic soon. To improve understanding of dengue transmission in these settings, we assessed the spatiotemporal dynamics of the hitherto largest outbreak in the non-endemic metropolis of Buenos Aires, Argentina, based on detailed information on the 5,104 georeferenced cases registered during summer-autumn of 2016. The highly seasonal dengue transmission in Buenos Aires was modulated by temperature and triggered by imported cases coming from regions with ongoing outbreaks. However, local transmission was made possible and consolidated heterogeneously in the city due to housing and socioeconomic characteristics of the population, with 32.8% of autochthonous cases occurring in slums, which held only 6.4% of the city population. A hierarchical spatiotemporal model accounting for imperfect detection of cases showed that, outside slums, less-affluent neighborhoods of houses (vs. apartments) favored transmission. Global and local spatiotemporal point-pattern analyses demonstrated that most transmission occurred at or close to home. Additionally, based on these results, a point-pattern analysis was assessed for early identification of transmission foci during the outbreak while accounting for population spatial distribution. Altogether, our results reveal how social, physical, and biological processes shape dengue transmission in Buenos Aires and, likely, other non-endemic cities, and suggest multiple opportunities for control interventions.

Dengue fever is mainly transmitted by a mosquito species that is highly urbanized, and lays eggs and develops mostly in artificial water containers. Dengue transmission is sustained year-round in most tropical regions of the world, but in many subtropical/temperate regions it occurs only in the warmest months. To improve understanding of dengue transmission in these regions, we analyzed one of the largest outbreaks in Buenos Aires city, a subtropical metropolis. Based on information on 5,104 georeferenced cases during summer-autumn 2016, we found that most transmission occurred in or near home, that slums had the highest risk of transmission, and that, outside slums, less-affluent neighborhoods of houses (vs. apartments) favored transmission. We showed that the cumulative effects of temperature over the previous few weeks set the temporal limits for transmission to occur, and that the outbreak was sparked by infected people arriving from regions with ongoing outbreaks. Additionally, we implemented a statistical method to identify transmission foci in real-time that improves targeting control interventions. Our results deepen the understanding of dengue transmission as a result of social, physical, and biological processes, and pose multiple opportunities for improving control of dengue and other mosquito-borne viruses such as Zika, chikungunya, and yellow fever.

Rational use of ultrasonography with triaging of patients to detect dengue plasma leakage in resource limited settings: a prospective cohort study

OBJECTIVES

To compare the traditional haematocrit-based criteria (>20% rise above baseline) with ultrasonography for diagnosing plasma leakage in dengue fever and to identify clinical indicators for triaging patients in resource-limited settings when the demand for ultrasonography is high.

METHODS

The Colombo Dengue Study is a prospective observational cohort study recruiting dengue patients in the first three days of dengue fever, before plasma leakage. Serial haematocrit assessments and ultrasonography were performed in patients recruited from October 2017 to February 2020. Clinical signs/symptoms and laboratory investigation results independently associated with ultrasound detected plasma leakage were identified with a derivation cohort and confirmed in a validation cohort.

RESULTS

129 of 426 patients had ultrasonography-confirmed plasma leakage while 146 had a haematocrit rise >20%. Those positive on ultrasonography were also likely to fulfil the haematocrit-based criteria (OR: 4.42, 95% CI: 2.85-6.86), but the two groups did not overlap fully. In the derivation cohort (n = 317), platelet count <97 000/µl, AST/ALT > 51 IU/l and having abdominal pain in the first three days of fever were independent predictors of ultrasound-detected plasma leakage. In the validation cohort (n = 109), the combination of low platelet count and high aminotransferase level had better predictive capacity in terms of sensitivity and specificity.

CONCLUSION

Dengue patients should be monitored with both serial haematocrit and ultrasonography whenever possible and plasma leakage should be diagnosed by either one of these criteria. If accessibility to scans is limited, platelet count, serum transaminase levels and presence of abdominal pain are useful to triage patients.

Twenty-Two years of dengue fever (1996-2017): an epidemiological study in a Brazilian city

This ecological study analyzed the temporal pattern of clinically diagnosed and laboratory confirmed dengue cases in Belo Horizonte, Minas Gerais, Brazil from 1996 to 2017. The study was divided into two analytical parts, the first of which evaluated the association between dengue incidence and host and climatic factors. The second part encompassed data from 2002 to 2017 and examined dengue incidence in relation to virus serotype and an intra-urban socioeconomic index. Over 22 years there were 469,171 cases and four epidemic peaks. There was an increase in the number, severity, and lethality of cases over the last 10 years of the study period. Biological and environmental factors appear to modulate the behavior of dengue in a large urban center.

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

BACKGROUND

With 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 FINDINGS

We 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.

CONCLUSIONS

This 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.

Comparing machine learning with case-control models to identify confirmed dengue cases

In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like illness (DLI) who received laboratory tests. Among them, 60.11% (2942 cases) were confirmed to have dengue. Using just four input variables [age, body temperature, white blood cells counts (WBCs) and platelets], not only the state-of-the-art deep neural network (DNN) prediction models but also the conventional decision tree (DT) and logistic regression (LR) models delivered performances with receiver operating characteristic (ROC) curves areas under curves (AUCs) of the ranging from 83.75% to 85.87% [for DT, DNN and LR: 84.60% ± 0.03%, 85.87% ± 0.54%, 83.75% ± 0.17%, respectively]. Subgroup analyses found all the models were very sensitive particularly in the pre-epidemic period. Pre-peak sensitivities (<35 weeks) were 92.6%, 92.9%, and 93.1% in DT, DNN, and LR respectively. Adjusted odds ratios examined with LR for low WBCs [≤ 3.2 (x103/μL)], fever (≥38°C), low platelet counts [< 100 (x103/μL)], and elderly (≥ 65 years) were 5.17 [95% confidence interval (CI): 3.96-6.76], 3.17 [95%CI: 2.74-3.66], 3.10 [95%CI: 2.44-3.94], and 1.77 [95%CI: 1.50-2.10], respectively. Our prediction models can readily be used in resource-poor countries where viral/serologic tests are inconvenient and can also be applied for real-time syndromic surveillance to monitor trends of dengue cases and even be integrated with mosquito/environment surveillance for early warning and immediate prevention/control measures. In other words, a local community hospital/clinic with an instrument of complete blood counts (including platelets) can provide a sentinel screening during outbreaks. In conclusion, the machine learning approach can facilitate medical and public health efforts to minimize the health threat of dengue epidemics. However, laboratory confirmation remains the primary goal of surveillance and outbreak investigation.

Epidemiology of dengue fever in Guatemala

Dengue fever occurs worldwide and about 1% of cases progress to severe haemorrhage and shock. Dengue is endemic in Guatemala and its surveillance system could document long term trends. We analysed 17 years of country-wide dengue surveillance data in Guatemala to describe epidemiological trends from 2000 to 2016.Data from the national dengue surveillance database were analysed to describe dengue serotype frequency, seasonality, and outbreaks. We used Poisson regression models to compare the number of cases each year with subsequent years and to estimate incidence ratios within serotype adjusted by age and gender. 91,554 samples were tested. Dengue was confirmed by RT-qPCR, culture or NS1-ELISA in 7097 (7.8%) cases and was IgM ELISA-positive in 19,290 (21.1%) cases. DENV1, DENV2, DENV3, and DENV4 were detected in 2218 (39.5%), 2580 (45.9%), 591 (10.5%), and 230 (4.1%) cases. DENV1 and DENV2 were the predominant serotypes, but all serotypes caused epidemics. The largest outbreak occurred in 2010 with 1080 DENV2 cases reported. The incidence was higher among adults during epidemic years, with significant increases in 2005, 2007, and 2013 DENV1 outbreaks, the 2010 DENV2 and 2003 DENV3 outbreaks. Adults had a lower incidence immediately after epidemics, which is likely linked to increased immunity.

Dengue is the most common mosquito-borne virus, and a major cause of fever, with an estimated 390 million infections annually. Guatemala, in Central America, has had ongoing dengue transmission since the 1990s. Its national surveillance system monitors outbreaks and seasonal trends of infections to inform public health responses. We have analysed 17 years of surveillance data collected from 2000 to 2016, to describe seasonal trends, outbreak years, and the fluctuating prevalence of the four dengue serotypes. Laboratory data from 91,554 individual serum samples were included, of which 7.8% were positive for dengue. All four dengue serotypes circulate in the country, with dengue 1 and 2 being the predominant serotypes. This is important, as it increases the likelihood of dengue infections being followed by a new infection with a different serotype, which can lead to severe dengue. We also report that adults in Guatemala have a lower likelihood of infection the year after an epidemic, which might be linked to an increased immunity in the population.

A comparison of passive surveillance and active cluster-based surveillance for dengue fever in southern coastal Ecuador

Background

Dengue is a major emerging infectious disease, endemic throughout the tropics and subtropics, with approximately 2.5 billion people at risk globally. Active (AS) and passive surveillance (PS), when combined, can improve our understanding of dengue’s complex disease dynamics to guide effective, targeted public health interventions. The objective of this study was to compare findings from the Ministry of Health (MoH) PS to a prospective AS arbovirus research study in Machala, Ecuador in 2014 and 2015.

Methods

Dengue cases in the PS system were compared to laboratory confirmed acute dengue illness cases that entered the AS study during the study period. Variables of interest included age class and sex. Outbreak detection curves by epidemiologic week, overall cumulative incidence and age-specific incidence proportions were calculated. Descriptive statistics were tabulated for all variables of interest. Chi-square tests were performed to compare demographic characteristics between the AS and PS data sets in 2014 and 2015.

Results

177 and 245 cases were identified from 1/1/2014 to 12/31/2015 by PS and AS, respectively; nine cases appeared in both systems. AS identified a greater number of laboratory-confirmed cases in 2014, accounting for more than 60% of dengue cases in the study area. In 2015, the opposite trend was observed with PS identifying 60% of the dengue cases in the study area. Peak transmission time in laboratory confirmed dengue illness, as noted by AS and PS was similar in 2014, whereas earlier detection (7 weeks) was observed by AS in 2015. Younger patients were more frequently identified by PS, while older patients were identified more frequently by AS. The cumulative incidence proportion for laboratory confirmed dengue illness reported via PS to the MoH was 4.12 cases per 10,000 residents in 2014, and 2.21 cases per 10,000 residents in 2015.

Conclusions

Each surveillance system captured distinct demographic subgroups within the Machala population, possibly due to differences in healthcare seeking behaviors, access to care, emerging threats of other viruses transmitted by the same mosquito vector and/or differences in clinical presentation. Integrating AS with pre-existing PS can aid in identifying additional cases in previously underdiagnosed subpopulations, improving our understanding of disease dynamics, and facilitating the implementation of timely public health interventions.

Distance sampling for epidemiology: an interactive tool for estimating under-reporting of cases from clinic data

BACKGROUND

Distance sampling methods are widely used in ecology to estimate and map the abundance of animal and plant populations from spatial survey data. The key underlying concept in distance sampling is the detection function, the probability of detecting the occurrence of an event as a function of its distance from the observer, as well as other covariates that may influence detection. In epidemiology, the burden and distribution of infectious disease is often inferred from cases that are reported at clinics and hospitals. In areas with few public health facilities and low accessibility, the probability of detecting a case is also a function of the distance between an infected person and the "observer" (e.g. a health centre). While the problem of distance-related under-reporting is acknowledged in public health; there are few quantitative methods for assessing and correcting for this bias when mapping disease incidence. Here, we develop a modified version of distance sampling for prediction of infectious disease incidence by relaxing some of the framework's fundamental assumptions. We illustrate the utility of this approach using as our example malaria distribution in rural Burkina Faso, where there is a large population at risk but relatively low accessibility of health facilities.

RESULTS

The modified distance-sampling framework was used to predict the probability of reporting malaria infection at 8 rural clinics, based on road-travel distances from villages. The rate at which reporting probability dropped with distance varied between clinics, depending on road and clinic positions. The probability of case detection was estimated as 0.3-1 in the immediate vicinity of the clinic, dropping to 0.1-0.6 at a travel distance of 10 km, and effectively zero at distances > 30-40 km.

CONCLUSIONS

To enhance the method's strategic impact, we provide an interactive mapping tool (as a self-contained R Shiny app) that can be used by non-specialists to interrogate model outputs and visualize how the overall probability of under-reporting and the catchment area of each clinic is influenced by changing the number and spatial allocation of health centres.

Recent apprise on coronavirus and its terrible insinuations

Epidemically increased evidence reveals that the link between the 2019-nCoV and other similar strain of coronaviruses circulating in bats and specifically the Rhinopodous bat sub-species. These sub-species are ample and widely present in Southern China, Middle East Africa and Europe. Recent studies show that more than 500 CoV have been identified in bats in China. The Center for Diseases Control and Prevention and the World Health Organization maintains a website that is updated frequently with new cases of MERS-CoV infection. As per WHO Situation report 16th, 24,554 number of cases confirmed globally out of which 99.22% cases from china. A new coronavirus (2019-nCoV) is causing respiratory syndrome mostly in Hubei Province, China. Corona Virus spread over 24 countries including Japan, India, Korea, and other countries 2019-CoV infection vary from mild, moderate or severe illness; the later includes severe pneumonia, ARDS, sepsis and septic shock. There are two diagnostic tests for coronavirus infection i.e. molecular test and serology test. In this review article there are the various recent cases of the patients that are suffering from the corona virus, the outcome of these studies is that corona virus infection is an epidemic disease which affects Central Nervous System (CNS).

Dengue Surveillance System in Brazil: A Qualitative Study in the Federal District

Dengue's increasing trends raise concerns over global health and pose a challenge to the Brazilian health system, highlighting the necessity of a strong surveillance system to reduce morbidity, mortality, and the economic burden of this disease. Although the Brazilian surveillance system reports more dengue cases than any other country, recent studies suggest that non-reported cases are the majority. The aim of the study is to explore the strengths and weaknesses of the Brazilian surveillance system, particularly looking at the functioning of data collection and reporting. This was done through qualitative semi-structured interviews with 17 experts in dengue surveillance, supported by quantitative data from the official notification system. To select the interviewees, purposive and theoretical sampling were used. Data were analyzed through thematic analysis. The research highlighted that a lack of human and technological resources in healthcare units and surveillance departments slows down the notification process and data analysis. Due to a lack of integration in the private sector, the surveillance system fails to detect the socioeconomic profile of the patients. Investments in public healthcare, human and technological resources for surveillance and better integration in the private healthcare system, and vector surveillance may improve dengue surveillance.

Epidemiological and Evolutionary Analysis of Dengue-1 Virus Detected in Guangdong during 2014: Recycling of Old and Formation of New Lineages

The incidence of dengue is increasing in Guangdong, China, with the largest outbreak to date in 2014. Widespread awareness of epidemiological and molecular characteristics of the dengue virus (DENV) is required. In 2014, we isolated the virus from patients and sequenced its genome. The sequences of DENV isolated from Guangdong and other countries screened since 2005 were studied to establish molecular evolutionary databases along with epidemiological data to explore its epidemiological, phylogenetic, and molecular characteristics. Causes underlying the occurrence of the dengue epidemic included importation and localization of the virus. The number of indigenous cases significantly exceeded that of imported cases. Dengue virus 1 is the most important serotype and caused the long-term epidemic locally. Based on the data available since 2005, DENV1 was divided into three genotypes (I, IV, and V). Only genotypes I and V were detected in 2014. In 2014, an epidemic involving old lineages of DENV1 genotype V occurred after 2 years of silence. The genotype was previously detected from 2009 to 2011. Genotype I, which caused recent epidemics, demonstrated a continuation of new lineages, and a predictive pattern of molecular evolution since 2005 among the four lineages was present. The DENV isolated from Guangdong was closely related to those causing large-scale epidemics in neighboring countries, suggesting the possibility of its import from these countries. The lack of sufficient epidemiological data and evidence on the local mosquito-borne DENV emphasizes the importance of studying the molecular evolutionary features and establishing a well-established phylogenetic tree for dengue prevention and control in Guangdong.

A Mapping Review on Urban Landscape Factors of Dengue Retrieved from Earth Observation Data, GIS Techniques, and Survey Questionnaires

To date, there is no effective treatment to cure dengue fever, a mosquito-borne disease which has a major impact on human populations in tropical and sub-tropical regions. Although the characteristics of dengue infection are well known, factors associated with landscape are highly scale dependent in time and space, and therefore difficult to monitor. We propose here a mapping review based on 78 articles that study the relationships between landscape factors and urban dengue cases considering household, neighborhood and administrative levels. Landscape factors were retrieved from survey questionnaires, Geographic Information Systems (GIS), and remote sensing (RS) techniques. We structured these into groups composed of land cover, land use, and housing type and characteristics, as well as subgroups referring to construction material, urban typology, and infrastructure level. We mapped the co-occurrence networks associated with these factors, and analyzed their relevance according to a three-valued interpretation (positive, negative, non significant). From a methodological perspective, coupling RS and GIS techniques with field surveys including entomological observations should be systematically considered, as none digital land use or land cover variables appears to be an univocal determinant of dengue occurrences. Remote sensing urban mapping is however of interest to provide a geographical frame to distribute human population and movement in relation to their activities in the city, and as spatialized input variables for epidemiological and entomological models.

The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review

Background

Public health surveillance is based on the continuous and systematic collection, analysis, and interpretation of data. This informs the development of early warning systems to monitor epidemics and documents the impact of intervention measures. The introduction of digital data sources, and specifically sources available on the internet, has impacted the field of public health surveillance. New opportunities enabled by the underlying availability and scale of internet-based sources (IBSs) have paved the way for novel approaches for disease surveillance, exploration of health communities, and the study of epidemic dynamics. This field and approach is also known as infodemiology or infoveillance.

Objective

This review aimed to assess research findings regarding the application of IBSs for public health surveillance (infodemiology or infoveillance). To achieve this, we have presented a comprehensive systematic literature review with a focus on these sources and their limitations, the diseases targeted, and commonly applied methods.

Methods

A systematic literature review was conducted targeting publications between 2012 and 2018 that leveraged IBSs for public health surveillance, outbreak forecasting, disease characterization, diagnosis prediction, content analysis, and health-topic identification. The search results were filtered according to previously defined inclusion and exclusion criteria.

Results

Spanning a total of 162 publications, we determined infectious diseases to be the preferred case study (108/162, 66.7%). Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1% (154/162) of the reviewed publications. We also identified limitations in representativeness and biased user age groups, as well as high susceptibility to media events by search queries, social media, and web encyclopedias.

Conclusions

IBSs are a valuable proxy to study illnesses affecting the general population; however, it is important to characterize which diseases are best suited for the available sources; the literature shows that the level of engagement among online platforms can be a potential indicator. There is a necessity to understand the population’s online behavior; in addition, the exploration of health information dissemination and its content is significantly unexplored. With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health.

Towards harmonisation of entomological surveillance in the Mediterranean area

BACKGROUND

The Mediterranean Basin is historically a hotspot for trade, transport, and migration. As a result, countries surrounding the Mediterranean Sea share common public health threats. Among them are vector-borne diseases, and in particular, mosquito-borne viral diseases are prime candidates as (re)emerging diseases and are likely to spread across the area. Improving preparedness and response capacities to these threats at the regional level is therefore a major issue. The implementation of entomological surveillance is, in particular, of utmost importance. Guidance in designing entomological surveillance systems is critical, and these systems may pursue different specific objectives depending on the disease. The purpose of the proposed review is to draw up guidelines for designing effective and sustainable entomological surveillance systems in order to improve preparedness and response. However, we make it clear that there is no universal surveillance system, so the thinking behind harmonisation is to define evidence-based standards in order to promote best practises, identify the most appropriate surveillance activities, and optimise the use of resources. Such guidance is aimed at policymakers and diverse stakeholders and is intended to be used as a framework for the implementation of entomological surveillance programmes. It will also be useful to collaborate and share information with health professionals involved in other areas of disease surveillance. Medical entomologists and vector control professionals will be able to refer to this report to advocate for tailored entomological surveillance strategies. The main threats targeted in this review are the vectors of dengue virus, chikungunya virus, Zika virus, West Nile virus, and Rift Valley fever virus. The vectors of all these arboviruses are mosquitoes.

METHODS

Current knowledge on vector surveillance in the Mediterranean area is reviewed. The analysis was carried out by a collaboration of the medical entomology experts in the region, all of whom belong to the MediLabSecure network, which is currently funded by the European Union and represents an international effort encompassing 19 countries in the Mediterranean and Black Sea region.

FINDINGS

Robust surveillance systems are required to address the globalisation of emerging arboviruses. The prevention and management of mosquito-borne viral diseases must be addressed in the prism of a One Health strategy that includes entomological surveillance as an integral part of the policy. Entomological surveillance systems should be designed according to the entomological and epidemiological context and must have well-defined objectives in order to effect a tailored and graduated response. We therefore rely on different scenarios according to different entomological and epidemiological contexts and set out detailed objectives of surveillance. The development of multidisciplinary networks involving both academics and public authorities will provide resources to address these health challenges by promoting good practises in surveillance (identification of surveillance aims, design of surveillance systems, data collection, dissemination of surveillance results, evaluation of surveillance activities) and through the sharing of effective knowledge and information. These networks will also contribute to capacity building and stronger collaborations between sectors at both the local and regional levels. Finally, concrete guidance is offered on the vector of the main arbovirus based on the current situation in the area.

Epitope Presentation of Dengue Viral Envelope Glycoprotein Domain III on Hepatitis B Core Protein Virus-Like Particles Produced in Nicotiana benthamiana

Dengue fever is currently ranked as the top emerging tropical disease, driven by increased global travel, urbanization, and poor hygiene conditions as well as global warming effects which facilitate the spread of Aedes mosquitoes beyond their current distribution. Today, more than 100 countries are affected most of which are tropical Asian and Latin American nations with limited access to medical care. Hence, the development of a dengue vaccine that is dually cost-effective and able to confer a comprehensive protection is ultimately needed. In this study, a consensus sequence of the antigenic dengue viral glycoprotein domain III (cEDIII) was used aiming to provide comprehensive coverage against all four circulating dengue viral serotypes and potential clade replacement event. Utilizing hepatitis B tandem core technology, the cEDIII sequence was inserted into the immunodominant c/e1 loop region so that it could be displayed on the spike structures of assembled particles. The tandem core particles displaying cEDIII epitopes (tHBcAg-cEDIII) were successfully produced in Nicotiana benthamiana via Agrobacterium-mediated transient expression strategy to give a protein of ∼54 kDa, detected in both soluble and insoluble fractions of plant extracts. The assembled tHBcAg-cEDIII virus-like particles (VLPs) were also visualized from transmission electron microscopy. These VLPs had diameters that range from 32 to 35 nm, presenting an apparent size increment as compared to tHBcAg control particles without cEDIII display (namely tEL). Mice immunized with tHBcAg-cEDIII VLPs showed a positive seroconversion to cEDIII antigen, thereby signifying that the assembled tHBcAg-cEDIII VLPs have successfully displayed cEDIII antigen to the immune system. If it is proven to be successful, tHBcAg-cEDIII has the potential to be developed as a cost-effective vaccine candidate that confers a simultaneous protection against all four infecting dengue viral serotypes.

Systematic review of health economic evaluation studies of dengue vaccines

OBJECTIVES

To review the literature on economic evaluation of dengue vaccination to produce evidence to support a local cost-effectiveness study and to subsidize the decision to introduce a dengue vaccine in the Brazilian National Immunization Program.

METHODS

We systematically searched multiple databases (MEDLINE (via PubMed), EMBASE, SCOPUS, NHS Economic Evaluation Database (NHS EED), HTA Database (via Centre for Reviews and Dissemination - CRD) and LILACS), selecting full HEEs of dengue vaccine. Two independent reviewers screened articles for relevance and extracted the data. The methodology for the quality reporting was assessed using CHEERS checklist. We performed a qualitative narrative synthesis.

RESULTS

Thirteen studies conducted in Asian and Latin America countries were reviewed. All studies were favorable to the incorporation of the vaccine. However, the assumptions and values assumed for vaccine efficacy, safety and duration of protection, as well as the choice of the study population and the type of model used in the analyses, associated to an insufficient reporting of the methodological steps, affect the validity of the studies' results. The quality reporting appraisal showed that the majority (8/13) of the studies reported less than 55% of the CHEERS checklists' items.

CONCLUSIONS

This systematic review shows that the economic evaluation of dengue vaccination did not adhere to key recommended general methods for economic evaluation. The presented cost-effectiveness results should not be transferred to other countries. It is recommended to conduct studies with local epidemiological and cost data, as well as assumptions about vaccination that reflect the results observed in clinical trials.

Economic burden of dengue in Indonesia

Background

Dengue is associated with significant economic expenditure and it is estimated that the Asia Pacific region accounts for >50% of the global cost. Indonesia has one of the world’s highest dengue burdens; Aedes aegypti and Aedes albopictus are the primary and secondary vectors. In the absence of local data on disease cost, this study estimated the annual economic burden during 2015 of both hospitalized and ambulatory dengue cases in Indonesia.

Methods

Total 2015 dengue costs were calculated using both prospective and retrospective methods using data from public and private hospitals and health centres in three provinces: Yogyakarta, Bali and Jakarta. Direct costs were extracted from billing systems and claims; a patient survey captured indirect and out-of-pocket costs at discharge and 2 weeks later. Adjustments across sites based on similar clinical practices and healthcare landscapes were performed to fill gaps in cost estimates. The national burden of dengue was extrapolated from provincial data using data from the three sites and applying an empirically-derived epidemiological expansion factor.

Results

Total direct and indirect costs per dengue case assessed at Yogyakarta, Bali and Jakarta were US$791, US$1,241 and US$1,250, respectively. Total 2015 economic burden of dengue in Indonesia was estimated at US$381.15 million which comprised US$355.2 million for hospitalized and US$26.2 million for ambulatory care cases.

Conclusion

Dengue imposes a substantial economic burden for Indonesian public payers and society. Complemented with an appropriate weighting method and by accounting for local specificities and practices, these data may support national level public health decision making for prevention/control of dengue in public health priority lists.

Dengue, an infection transmitted by mosquitos, is a public health concern particularly in tropical/subtropical areas and the Asia Pacific region where it is associated with a significant cost to society. Indonesia has one of the world’s highest dengue burdens but Indonesia-specific data on cost are lacking. To estimate the annual economic burden of dengue in Indonesia, this study collected data from public/private hospitals and health centres in three provinces (Yogyakarta, Bali and Jakarta) during 2015. We estimated cost of illness using the societal perspective: calculations of costs included those that were directly paid by the healthcare system, as well as costs incurred by the patients (or their family/care givers) and their lost productivity. The costs from the three provinces were then used as the basis for extrapolating cost of illness in Indonesia. The authors confirmed that dengue imposed a substantial economic burden for Indonesian public payers and society. Based on 2015 data, the authors estimated total economic burden of dengue in Indonesia at US$381.15 million. Of this, US$355.2 million related to patients treated in hospitals and US$26.2 million was for patients treated in health centres. Establishing a better understanding of the burden of dengue in Indonesia will help to guide public health decision-making at a national level and support prevention and control initiatives for this disease.

Correlation between Google Trends on dengue fever and national surveillance report in Indonesia

Background: Digital traces are rapidly used for health monitoring purposes in recent years. This approach is growing as the consequence of increased use of mobile phone, Internet, and machine learning. Many studies reported the use of Google Trends data as a potential data source to assist traditional surveillance systems. The rise of Internet penetration (54.7%) and the huge utilization of Google (98%) indicate the potential use of Google Trends in Indonesia. No study was performed to measure the correlation between country wide official dengue reports and Google Trends data in Indonesia. Objective: This study aims to measure the correlation between Google Trends data on dengue fever and the Indonesian national surveillance report. Methods: This research was a quantitative study using time series data (2012-2016). Two sets of data were analyzed using Moving Average analysis in Microsoft Excel. Pearson and Time lag correlations were also used to measure the correlation between those data. Results: Moving Average analysis showed that Google Trends data have a linear time series pattern with official dengue report. Pearson correlation indicated high correlation for three defined search terms with R-value range from 0.921 to 0.937 (p ≤ 0.05, overall period) which showed increasing trend in epidemic periods (2015-2016). Time lag correlation also indicated that Google Trends data can potentially be used for an early warning system and novel tool to monitor public reaction before the increase of dengue cases and during the outbreak. Conclusions: Google Trends data have a linear time series pattern and statistically correlated with annual official dengue reports. Identification of information-seeking behavior is needed to support the use of Google Trends for disease surveillance in Indonesia.

Developing the active larval indices surveillance system for dengue solution in low and high dengue risk primary care units, Southern Thailand

Purpose

The purpose of this paper is to develop an active larval indices surveillance system and compare the outcomes of the implementation in primary care units (PCUs) at low and high risk of dengue.

Design/methodology/approach

The study design was conducted by implementing a community participation action research system in low and high dengue risk PCUs in Lansaka district, Nakhon Si Thammarat province, in the Southern Region of Thailand. There were five phases to the process including preparation of all stakeholders, situation assessment, development of the surveillance system, program implementation and evaluation. The system was developed in ten villages that were categorized as either low dengue risk PCUs (comprising six villages) or high dengue risk PCUs (four villages). A village was assigned as being at high or low dengue risk according to pre-determined criteria. The low dengue risk PCU assessments were conducted on a seven-step active larval indices surveillance system where PCU officials were additionally involved in coordinating, teaching, coaching and supporting the village health volunteers (VHVs) for dengue prevention activities. The high dengue risk PCUs, on the other hand, only followed a basic larval indices surveillance system with no follow-up support.

Findings

The outcomes of using intervention systems showed that the VHVs’ dengue knowledge and larval indices understanding in both PCUs increased significantly (p<0.01). Furthermore, the low dengue risk PCUs had a higher larval indices level than the high dengue risk PCU (p<0.01).

Originality/value

This study showed that the low dengue risk PCU followed an active larval indices surveillance system at the sub-district level which is appropriate for villages. This study also revealed that VHVs are needed to strengthen the capacity in terms of knowledge and skills of developing such a system to ensure reduced levels of dengue in the community.

Integrated Aedes management for the control of Aedes-borne diseases

BACKGROUND

Diseases caused by Aedes-borne viruses, such as dengue, Zika, chikungunya, and yellow fever, are emerging and reemerging globally. The causes are multifactorial and include global trade, international travel, urbanisation, water storage practices, lack of resources for intervention, and an inadequate evidence base for the public health impact of Aedes control tools. National authorities need comprehensive evidence-based guidance on how and when to implement Aedes control measures tailored to local entomological and epidemiological conditions.

METHODS AND FINDINGS

This review is one of a series being conducted by the Worldwide Insecticide resistance Network (WIN). It describes a framework for implementing Integrated Aedes Management (IAM) to improve control of diseases caused by Aedes-borne viruses based on available evidence. IAM consists of a portfolio of operational actions and priorities for the control of Aedes-borne viruses that are tailored to different epidemiological and entomological risk scenarios. The framework has 4 activity pillars: (i) integrated vector and disease surveillance, (ii) vector control, (iii) community mobilisation, and (iv) intra- and intersectoral collaboration as well as 4 supporting activities: (i) capacity building, (ii) research, (iii) advocacy, and (iv) policies and laws.

CONCLUSIONS

IAM supports implementation of the World Health Organisation Global Vector Control Response (WHO GVCR) and provides a comprehensive framework for health authorities to devise and deliver sustainable, effective, integrated, community-based, locally adapted vector control strategies in order to reduce the burden of Aedes-transmitted arboviruses. The success of IAM requires strong commitment and leadership from governments to maintain proactive disease prevention programs and preparedness for rapid responses to outbreaks.

The Surveillance of Chikungunya Virus in a Temperate Climate: Challenges and Possible Solutions from the Experience of Lazio Region, Italy

CHIKV has become an emerging public health concern in the temperate regions of the Northern Hemisphere as a consequenceof the expansion of the endemic areas of its vectors (mainly Aedes aegypti and Aedesalbopictus). In 2017, a new outbreak of CHIKV was detected in Italy with three clusters of autochthonous transmission in the Lazio Region (central Italy), in the cities of Anzio, Rome, and Latina and a secondary cluster in the Calabria Region (south Italy). Given the climate characteristics of Italy, sporadic outbreaks mostly driven by imported cases followed by autochthonous transmission could occur during the summer season. This highlights the importance of a well-designed surveillance system, which should promptly identify autochthonous transmission. The use of a surveillance system integrating different surveillance tools, including entomological surveillance in a one health approach, together with education of the health care professionals should facilitate the detection, response, and control of arboviruses spreading.

Effective surveillance systems for vector-borne diseases in urban settings and translation of the data into action: a scoping review

Background

Vector-borne diseases (VBDs) continue to represent a global threat, with “old” diseases like malaria, and “emergent” or “re-emergent” ones like Zika, because of an increase in international trade, demographic growth, and rapid urbanization. In this era of globalization, surveillance is a key element in controlling VBDs in urban settings, but surveillance alone cannot solve the problem. A review of experiences is of interest to examine other solution elements. The objectives were to assess the different means of VBD surveillance in urban environments, to evaluate their potential for supporting public health actions, and to describe the tools used for public health actions, the constraints they face, and the research and health action gaps to be filled.

Main body

For this scoping review we searched peer-reviewed articles and grey literature published between 2000 and 2016. Various tools were used for data coding and extraction. A quality assessment was done for each study reviewed, and descriptive characteristics and data on implementation process and transferability were analyzed in all studies.

After screening 414 full-text articles, we retained a total of 79 articles for review. The main targets of the articles were arboviral diseases (65.8%) and malaria (16.5%). The positive aspects of many studies fit within the framework of integrated vector management. Public awareness is considered a key to successful vector control programs. Advocacy and legislation can reinforce both empowerment and capacity building. These can be achieved by collaboration within the health sector and with other sectors. Research is needed to develop well designed studies and new tools for surveillance and control.

Conclusions

The need for surveillance systems in urban settings in both developing and developed countries was highlighted. Countries face the same challenges relating to human, financial, and structural resources. These findings also constitute a wake-up call for governments, academia, funders, and World Health Organization to strengthen control programs and enhance VBD research in urban environments.

Electronic supplementary material

The online version of this article (10.1186/s40249-018-0473-9) contains supplementary material, which is available to authorized users.

Enhanced surveillance during a public health emergency in a resource-limited setting: Experience from a large dengue outbreak in Solomon Islands, 2016-17

Between August-2016 and April-2017, Solomon Islands experienced the largest and longest-running dengue outbreak on record in the country, with 12,329 suspected cases, 877 hospitalisations and 16 deaths. We conducted a retrospective review of related data and documents, and conducted key informant interviews to characterise the event and investigate the adaptability of syndromic surveillance for enhanced and expanded data collection during a public health emergency in a low resource country setting. While the outbreak quickly consumed available public and clinical resources, we found that authorities were able to scale up the conventional national syndrome-based early warning surveillance system to support the increased information demands during the event demonstrating the flexibility of the system and syndromic surveillance more broadly. Challenges in scaling up included upskilling and assisting staff with no previous experience of the tasks required; managing large volumes of data; maintaining data quality for the duration of the outbreak; harmonising routine and enhanced surveillance data and maintaining surveillance for other diseases; producing information optimally useful for response planning; and managing staff fatigue. Solomon Islands, along with other countries of the region remains vulnerable to outbreaks of dengue and other communicable diseases. Ensuring surveillance systems are robust and able to adapt to changing demands during emergencies should be a health protection priority.

Early warning and response system (EWARS) for dengue outbreaks: Recent advancements towards widespread applications in critical settings

Background

Dengue outbreaks are increasing in frequency over space and time, affecting people’s health and burdening resource-constrained health systems. The ability to detect early emerging outbreaks is key to mounting an effective response. The early warning and response system (EWARS) is a toolkit that provides countries with early-warning systems for efficient and cost-effective local responses. EWARS uses outbreak and alarm indicators to derive prediction models that can be used prospectively to predict a forthcoming dengue outbreak at district level.

Methods

We report on the development of the EWARS tool, based on users’ recommendations into a convenient, user-friendly and reliable software aided by a user’s workbook and its field testing in 30 health districts in Brazil, Malaysia and Mexico.

Findings

34 Health officers from the 30 study districts who had used the original EWARS for 7 to 10 months responded to a questionnaire with mainly open-ended questions. Qualitative content analysis showed that participants were generally satisfied with the tool but preferred open-access vs. commercial software. EWARS users also stated that the geographical unit should be the district, while access to meteorological information should be improved. These recommendations were incorporated into the second-generation EWARS-R, using the free R software, combined with recent surveillance data and resulted in higher sensitivities and positive predictive values of alarm signals compared to the first-generation EWARS. Currently the use of satellite data for meteorological information is being tested and a dashboard is being developed to increase user-friendliness of the tool. The inclusion of other Aedes borne viral diseases is under discussion.

Conclusion

EWARS is a pragmatic and useful tool for detecting imminent dengue outbreaks to trigger early response activities.

Asymptomatic Prenatal Zika Virus Infection and Congenital Zika Syndrome

To investigate to what extent asymptomatic vs symptomatic prenatal Zika virus infections contribute to birth defects, we identified 3 prospective and 8 retrospective studies. The ratio varied greatly in the retrospective studies, most likely due to recruitment and recall bias. The prospective studies revealed a ratio of 1:1 for asymptomatic vs symptomatic maternal Zika infections resulting in adverse fetal outcomes.

The use of longitudinal cohorts for studies of dengue viral pathogenesis and protection

In this review, we describe how longitudinal prospective community-based, school-based, and household-based cohort studies contribute to improving our knowledge of viral disease, focusing specifically on contributions to understanding and preventing dengue. We describe how longitudinal cohorts enable measurement of essential disease parameters and risk factors; provide insights into biological correlates of protection and disease risk; enable rapid application of novel biological and statistical technologies; lead to development of new interventions and inform vaccine trial design; serve as sentinels in outbreak conditions and facilitate development of critical diagnostic assays; enable holistic studies on disease in the context of other infections, comorbidities, and environmental risk factors; and build research capacity that strengthens national and global public health response and disease surveillance.

Live-attenuated tetravalent dengue vaccines: The needs and challenges of post-licensure evaluation of vaccine safety and effectiveness

Since December 2015, the first dengue vaccine has been licensed in several Asian and Latin American countries for protection against disease from all four dengue virus serotypes. While the vaccine demonstrated an overall good safety and efficacy profile in clinical trials, some key research questions remain which make risk-benefit-assessment for some populations difficult. As for any new vaccine, several questions, such as very rare adverse events following immunization, duration of vaccine-induced protection and effectiveness when used in public health programs, will be addressed by post-licensure studies and by data from national surveillance systems after the vaccine has been introduced. However, the complexity of dengue epidemiology, pathogenesis and population immunity, as well as some characteristics of the currently licensed vaccine, and potentially also future, live-attenuated dengue vaccines, poses a challenge for evaluation through existing monitoring systems, especially in low and middle-income countries. Most notable are the different efficacies of the currently licensed vaccine by dengue serostatus at time of first vaccination and by dengue virus serotype, as well as the increased risk of dengue hospitalization among young vaccinated children observed three years after the start of vaccination in one of the trials. Currently, it is unknown if the last phenomenon is restricted to younger ages or could affect also seronegative individuals aged 9years and older, who are included in the group for whom the vaccine has been licensed. In this paper, we summarize scientific and methodological considerations for public health surveillance and targeted post-licensure studies to address some key research questions related to live-attenuated dengue vaccines. Countries intending to introduce a dengue vaccine should assess their capacities to monitor and evaluate the vaccine's effectiveness and safety and, where appropriate and possible, enhance their surveillance systems accordingly. Targeted studies are needed, especially to better understand the effects of vaccinating seronegative individuals.

Assessing dengue transmission risk and a vector control intervention using entomological and immunological indices in Thailand: study protocol for a cluster-randomized controlled trial

Background

Dengue fever is the most common and widespread mosquito-borne arboviral disease in the world. There is a compelling need for cost-effective approaches and practical tools that can reliably measure real-time dengue transmission dynamics that enable more accurate and useful predictions of incidence and outbreaks. Sensitive surveillance tools do not exist today, and only a small handful of new control strategies are available. Vector control remains at the forefront for combating dengue transmission. However, the effectiveness of many current vector control interventions is fraught with inherent weaknesses. No single vector control method is effective enough to control both vector populations and disease transmission. Evaluations of novel larval and adult control interventions are needed.

Methods/design

A cluster-randomized controlled trial will be carried out between 2017 and 2019 in urban community clusters in Khon Kaen and Roi Et cities, northeastern Thailand. The effectiveness of a pyriproxyfen/spinosad combination treatment of permanent water storage containers will be evaluated on epidemiological and entomological outcomes, including dengue incidence, number of female adult dengue vectors infected or not infected with dengue virus (DENV), human exposure to Aedes mosquito bites, and several other indices. These indices will also be used to develop predictive models for dengue transmission and impending outbreaks. Epidemiological and entomological data will be collected continuously for 2 years, with the intervention implemented after 1 year.

Discussion

The aims of the trial are to simultaneously evaluate the efficacy of an innovative dengue vector control intervention and developing predictive dengue models. Assessment of human exposure to mosquito bites by detecting antibodies generated against Aedes saliva proteins in human blood samples has, so far, not been applied in dengue epidemiological risk assessment and disease surveillance methodologies. Likewise, DENV detection in mosquitoes (adult and immature stages) has not been used in any practical way for routine disease surveillance strategies. The integration of multiple outcome measures will assist health authorities to better predict outbreaks for planning and applying focal and timely interventions. The trial outcomes will not only be important for Thailand, but also for the entire Southeast Asian region and further afield.

Trial registration

ISRCTN, ISRCTN73606171. Registered on 23 June 2017.

Electronic supplementary material

The online version of this article (10.1186/s13063-018-2490-1) contains supplementary material, which is available to authorized users.

Improved tools and strategies for the prevention and control of arboviral diseases: A research-to-policy forum

BACKGROUND

Research has been conducted on interventions to control dengue transmission and respond to outbreaks. A summary of the available evidence will help inform disease control policy decisions and research directions, both for dengue and, more broadly, for all Aedes-borne arboviral diseases.

METHOD

A research-to-policy forum was convened by TDR, the Special Programme for Research and Training in Tropical Diseases, with researchers and representatives from ministries of health, in order to review research findings and discuss their implications for policy and research.

RESULTS

The participants reviewed findings of research supported by TDR and others. Surveillance and early outbreak warning. Systematic reviews and country studies identify the critical characteristics that an alert system should have to document trends reliably and trigger timely responses (i.e., early enough to prevent the epidemic spread of the virus) to dengue outbreaks. A range of variables that, according to the literature, either indicate risk of forthcoming dengue transmission or predict dengue outbreaks were tested and some of them could be successfully applied in an Early Warning and Response System (EWARS). Entomological surveillance and vector management. A summary of the published literature shows that controlling Aedes vectors requires complex interventions and points to the need for more rigorous, standardised study designs, with disease reduction as the primary outcome to be measured. House screening and targeted vector interventions are promising vector management approaches. Sampling vector populations, both for surveillance purposes and evaluation of control activities, is usually conducted in an unsystematic way, limiting the potentials of entomological surveillance for outbreak prediction. Combining outbreak alert and improved approaches of vector management will help to overcome the present uncertainties about major risk groups or areas where outbreak response should be initiated and where resources for vector management should be allocated during the interepidemic period.

CONCLUSIONS

The Forum concluded that the evidence collected can inform policy decisions, but also that important research gaps have yet to be filled.

Aedes Mosquitoes and Aedes-Borne Arboviruses in Africa: Current and Future Threats

The Zika crisis drew attention to the long-overlooked problem of arboviruses transmitted by Aedes mosquitoes in Africa. Yellow fever, dengue, chikungunya and Zika are poorly controlled in Africa and often go unrecognized. However, to combat these diseases, both in Africa and worldwide, it is crucial that this situation changes. Here, we review available data on the distribution of each disease in Africa, their Aedes vectors, transmission potential, and challenges and opportunities for Aedes control. Data on disease and vector ranges are sparse, and consequently maps of risk are uncertain. Issues such as genetic and ecological diversity, and opportunities for integration with malaria control, are primarily African; others such as ever-increasing urbanization, insecticide resistance and lack of evidence for most control-interventions reflect problems throughout the tropics. We identify key knowledge gaps and future research areas, and in particular, highlight the need to improve knowledge of the distributions of disease and major vectors, insecticide resistance, and to develop specific plans and capacity for arboviral disease surveillance, prevention and outbreak responses.

Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico

Modelling dengue fever in endemic areas is important to mitigate and improve vector-borne disease control to reduce outbreaks. This study applied artificial neural networks (ANNs) to predict dengue fever outbreak occurrences in San Juan, Puerto Rico (USA), and in several coastal municipalities of the state of Yucatan, Mexico, based on specific thresholds. The models were trained with 19 years of dengue fever data for Puerto Rico and six years for Mexico. Environmental and demographic data included in the predictive models were sea surface temperature (SST), precipitation, air temperature (i.e., minimum, maximum, and average), humidity, previous dengue cases, and population size. Two models were applied for each study area. One predicted dengue incidence rates based on population at risk (i.e., numbers of people younger than 24 years), and the other on the size of the vulnerable population (i.e., number of people younger than five years and older than 65 years). The predictive power was above 70% for all four model runs. The ANNs were able to successfully model dengue fever outbreak occurrences in both study areas. The variables with the most influence on predicting dengue fever outbreak occurrences for San Juan, Puerto Rico, included population size, previous dengue cases, maximum air temperature, and date. In Yucatan, Mexico, the most important variables were population size, previous dengue cases, minimum air temperature, and date. These models have predictive skills and should help dengue fever mitigation and management to aid specific population segments in the Caribbean region and around the Gulf of Mexico.

Bayesian dynamic modeling of time series of dengue disease case counts

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.

Phylogenetic and evolutionary analyses of dengue viruses isolated in Jakarta, Indonesia

Dengue has affected Indonesia for the last five decades and become a major health problem in many cities in the country. Jakarta, the capital of Indonesia, reports dengue cases annually, with several outbreaks documented. To gain information on the dynamic and evolutionary history of dengue virus (DENV) in Jakarta, we conducted phylogenetic and evolutionary analyses of DENV isolated in 2009. Three hundred thirty-three dengue-suspected patients were recruited. Our data revealed that dengue predominantly affected young adults, and the majority of cases were due to secondary infection. A total of 171 virus isolates were successfully serotyped. All four DENV serotypes were circulating in the city, and DENV-1 was the predominant serotype. The DENV genotyping of 17 isolates revealed the presence of Genotypes I and IV in DENV-1, while DENV-2 isolates were grouped into the Cosmopolitan genotype. The grouping of isolates into Genotype I and II was seen for DENV-3 and DENV-4, respectively. Evolutionary analysis revealed the relatedness of Jakarta isolates with other isolates from other cities in Indonesia and isolates from imported cases in other countries. We revealed the endemicity of DENV and the role of Jakarta as the potential source of imported dengue cases in other countries. Our study provides genetic information regarding DENV from Jakarta, which will be useful for upstream applications, such as the study of DENV epidemiology and evolution and transmission dynamics.

Dengue Symptoms Significance in Anti-Dengue Drug Development: Road Less Travelled

Dengue outbreak has affected rural areas of Delhi-NCR, Haryana widely but it lacks in surveillance. High cases of dengue symptoms were reported in these regions whereas dengue symptoms have been a neglected issue in the anti-dengue drug development. Therefore, this study aims to analyze the status of the dengue infection, a rural issue of Delhi-NCR, Haryana and to identify the significance of dengue symptoms in anti-dengue drug development. The study was conducted when there is high chance of dengue infection i.e. from August 2015 to October 2015 at OPD Unit of PR Institute of Medical Science & Research, Delhi-NCR, Sonepat. It includes 158 patients from 24 rural areas of Haryana comprising both males and females from different age groups. Out of 20% cases, 6% were IgG-Positive, 9% were IgMPositive and 88% were NS1-Positive and rest 80% was normal. It includes 44% cases of thrombocytopenia. Badkhalsa village (28%), age group 18-24 (34%) and males (63%) reported cases of high infection. It was found that people with fewer platelet counts (Rai village) were not suffering from dengue whereas people with more platelet count reported dengue infection (Badkhalsa village).

INTERPRETATION & CONCLUSION

This study focuses on new research directions by highlighting the dengue symptoms importance in anti-dengue drug development also it is a first attempt to investigate the status of dengue, a rural issue of Delhi-NCR, Haryana and suggests that health authorities and people living in these regions should take initiatives for better health.

miR-21 promotes dengue virus serotype 2 replication in HepG2 cells

Infection with the mosquito transmitted dengue virus (DENV) remains a significant worldwide public health problem. While the majority of infections are asymptomatic, infection can result in a range of symptoms. MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression through repression or degradation of mRNAs. To understand the contribution of miRNAs to DENV 2 replication, we screened a number of candidate miRNAs for variations in expression levels during DENV 2 infection of HepG2 (liver) cells. Seven miRNAs were identified as differentially expressed, and one, miR-21, was differentially expressed at all time points examined. Interestingly, miR-21 was also differentially regulated in DENV 2 infection under conditions of antibody dependent enhancement of infection, and in direct Zika virus infection, but not in DENV 4 infection. The role of miR-21 during DENV infection was further examined by treating HepG2 cells with an anti-miR-21 (AMO-21) before DENV infection. The results showed a significant reduction in DENV 2 production, clearly suggesting that miR-21 plays a key role in DENV 2 replication. To further confirm the role of miR-21 in DENV infection, a peptide nucleic acid-21 (PNA-21) construct with a nucleotide sequence complementary to AMO-21, was co-administered with AMO-21 as an AMO-21/PNA-21 complex followed by DENV 2 infection. The results showed that AMO-21 significantly reduced DENV 2 titer, PNA-21 significantly increased DENV 2 titer and the combined AMO-21/PNA-21 showed no difference from non-treated infection controls. Taken together, the results show that miR-21 promotes DENV 2 replication, and this mechanism could serve as a possible therapeutic intervention point.