Dengue Risk Research Articles

Maternally derived antibody titer dynamics and risk of hospitalized infant dengue disease

Infants less than 1 y of age experience high rates of dengue disease in dengue virus (DENV) endemic countries. This burden is commonly attributed to antibody-dependent enhancement (ADE), whereby concentrations of maternally derived DENV antibodies become subneutralizing, and infection-enhancing. Understanding antibody-related mechanisms of enhanced infant dengue disease risk represents a significant challenge due to the dynamic nature of antibodies and their imperfect measurement processes. Further, key uncertainties exist regarding the impact of long-term shifts in birth rates, population-level infection risks, and maternal ages on the DENV immune landscape of newborns and their subsequent risks of severe dengue disease in infancy. Here, we analyze DENV antibody data from two infant cohorts (N = 142 infants with 605 blood draws) and 40 y of infant dengue hospitalization data from Thailand. We use mathematical models to reconstruct maternally derived antibody dynamics, accounting for discretized measurement processes and limits of assay detection. We then explore possible antibody-related mechanisms of enhanced infant dengue disease risk and their ability to reconstruct the observed age distribution of hospitalized infant dengue cases. We find that ADE mechanisms are best able to reconstruct the observed data. Finally, we describe how the shifting epidemiology of dengue in Thailand, combined with declining birth rates, have decreased the absolute risk of infant dengue disease by 88% over a 40-y period while having minimal impact on the mean age of infant hospitalized dengue disease.

Age and socio-economic status affect dengue and COVID-19 incidence: spatio-temporal analysis of the 2020 syndemic in Buenos Aires City.

In early 2020, Argentina experienced the worst dengue outbreak in its history, concomitant with first-to-date increasing COVID-19 cases. Dengue epidemics in temperate Argentina have already been described as spatially heterogeneous; in the previous 2016 outbreak, transmission occurred 7.3 times more frequently in slums compared to the rest of Buenos Aires City (CABA). These informal settlements have deficient sanitary conditions, precarious housing and high incidence of social vulnerabilities. The purpose of this work was to study the spatio-temporal patterns of the 2020 dengue epidemic in CABA in relation to socio-economic living conditions of its inhabitants and its interaction with the onset of COVID-19. The study considered the period between Jan 1st and May 30th 2020. Dengue and COVID-19 databases were obtained from the National Health Surveillance System; each record was anonymized and geo-localized. The city was divided according to census tracts and grouped in four socio-economic strata: slums, high, mid and low residential. An aligned-rank transform ANOVA was performed to test for differences in the incidence of dengue and COVID-19, and age at death due to COVID-19, among socio-economic strata, four age categories and their interaction. The incidence by cluster was calculated with a distance matrix up to 600 m from the centroid. Spatial joint dengue and COVID-19 risk was estimated by multiplying the nominal risk for each disease, defined from 1 (low) to 5 (high) according to their quantiles. During the study period, 7,175 dengue cases were registered in CABA (incidence rate 23.3 cases per 10,000 inh), 29.2% of which occurred in slums. During the same period, 8,809 cases of COVID-19 were registered (28.6 cases per 10,000 inh); over half (51.4%) occurred in slums, where the median age of cases (29 years old) was lower than in residential areas (42 years old). The mean age of the deceased was 58 years old in slums compared to 79 years old outside. The percentage of deaths in patients under 60 years old was 56% in slums compared to 8% in the rest of the city. The incidence of both diseases was higher in slums than in residential areas for most age categories. Spatial patterns were heterogeneous: dengue presented higher incidence values in the southern sector of the city and the west, and low values in highly urbanized quarters, whereas COVID-19 presented higher values in the east, south, high populated areas and slums. The lowest joint risk clusters were located mainly in high residential areas, whereas high joint risk was observed mainly in the south, some western clusters, the historical part of the city and center north. The social epidemiological perspective of dengue and COVID-19 differed, given that socio environmental heterogeneity influenced the burden of both viruses in a different manner. Despite the overwhelming effect of the COVID-19 pandemic, health care towards other diseases, especially in territories with pre-existing vulnerabilities, should not be unattended.

Retracted: “Best of Both World”: The Amalgamation of Fuzzy Delphi Method with Nominal Group Technique for Dengue Risk Prioritisation Decision-Making

Retracted: “Best of Both World”: The Amalgamation of Fuzzy Delphi Method with Nominal Group Technique for Dengue Risk Prioritisation Decision-Making

Travel-Associated Dengue Cases — United States, 2010–2021

Dengue, the leading cause of arboviral disease worldwide, can be fatal without appropriate treatment. Among 7,528 confirmed or probable travel-associated U.S. dengue cases reported during 2010–2021, one in five (1,474, 20%) was reported in 2019. This is 168% higher than the annual average number of cases reported during 2010–2018 and 2020–2021 (approximately 550 per year) and 61% higher than the 913 cases reported in 2016, the second highest year on record. The number of cases as a fraction of air traffic volume to international destinations outside North America or Europe was also highest in 2019, with 41.9 cases per million trips, compared with 21.0 per million in other years during 2010–2021. This report compares the number and characteristics of travel-associated dengue cases reported to national surveillance in the United States in 2019 with cases reported during 2010–2018 and 2020–2021. Areas with conditions suitable for dengue transmission as well as the population at risk for dengue are expected to increase, placing U.S. travelers at higher risk for infection. Health care providers should be aware that dengue is a common cause of fever in the returning traveler and be familiar with its signs and symptoms, testing, and management. Dengue vaccines are not currently recommended for U.S. travelers; therefore, persons should review areas of dengue risk and follow guidance for preventing mosquito bites.

Impact of Comorbid Conditions on Severity of Dengue Fever: A Comparative Study

Objective: to identify the comorbid conditions associated with an increased risk of severe Dengue in clinically diagnosed patients with Dengue viral infections.
 Study Design: Comparative cross sectional study.
 Place and Duration of Study: Dengue ward, Department of Medicine, Combined Military Hospital Lahore, from Oct to Nov 2021.
 Methodology: Three hundred patients with confirmed Dengue infection were enrolled in the study. These patients were divided into two groups: 150 patients with comorbids (diabetes mellitus, hypertension, ischemic heart disease) and 150 patients without comorbids. The impact of comorbid conditions on the severity of dengue fever was studied.
 Results: Seventy-three out of the 300 patients had Dengue Fever without warning signs, while 79 out of the 300 patients had Severe Dengue Fever. Almost half of the patients (148) 300 had Dengue Fever with warning signs. The mean age of participants is 45.62±14.06 years. Patients with diabetes mellitus were more likely to develop Dengue without warning signs. Patients with hypertension were more likely to develop severe Dengue.
 Conclusion: This study showed that comorbid conditions such as Diabetes and Hypertension are important risk factors for developing severe Dengue.

Dengue in Timor-Leste during the COVID-19 phenomenon.

Dengue is a significant public health problem in mostly tropical countries, including Timor-Leste. Dengue continues to draw attention from the health sector during the COVID-19 phenomenon. Therefore, the goal of this study is to evaluate the dengue incidence rate in comparison with the COVID-19 cumulative number and associated dengue risk factors, including the fatality rate of dengue infection in each municipality during the COVID-19 phenomenon in Timor-Leste, by applying the data processing program in Geographic Information Systems (GIS). A descriptive study using GIS was performed to provide a spatial-temporal mapping of dengue cases. Secondary data, which were sourced from the Department of Health Statistics Information under the Ministry of Health Timor-Leste, were collected for the period during the COVID-19 outbreak in 2020-2021. These data were grounded at the municipal (province) level. Quantum GIS and Microsoft Excel were used to analyze the data. During the COVID-19 outbreak (2020-2021), dengue spread nationwide. It was found that there was an increase in municipalities with high dengue cases and cumulative COVID-19 numbers. The high number of dengue cases associated with the COVID-19 cumulative number found in municipalities with an urban characteristic and in terms of severity, dengue fever (DF) is most commonly reported with a total of 1,556 cases and is followed by dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). Most cases were reported in the months of the monsoon season, such as December, January, and March. Dengue GIS mapping helps understand the disease's presence and dynamic nature over time.

A Spatial Dengue Risk Map for the Jaffna District of Northern Sri Lanka

Dengue, a mosquito-borne disease, is a major public health concern in Sri Lanka including the northern Jaffna district. Epidemiology of dengue is associated with variations in land-use patterns and socio-economic factors. Jaffna district is one of the administrative locations with high incidence of dengue over the last two decades. The objective of the study was to identify potential dengue risk areas based on land-use patterns and socio-economic and demographic factors in Jaffna district at the Medical Officer for Health (MoH) administrative level applying spatial multi-criteria analysis using Geographic Information System Platform. The analysis also incorporated dengue cases and dengue serotypes identified in 2019 in the Jaffna district. The study reveals that the Nallur MoH division is the very high-risk area for dengue transmission. The identified risk map is expected to be useful for health authorities to prioritize control measures to identified risk areas. A detailed study considering land-use pattern, socio-economy, demography and serotypes over the years is warranted to understand the spatio-temporal patterns in dengue transmission in the Jaffna district.

Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study.

Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia. We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model. When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale. The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue. Swedish Research Council Formas, Umeå Centre for Global Health Research, Swedish Council for Working Life and Social Research, Swedish research council VINNOVA and Alexander von Humboldt Foundation (Germany).

Seasonal Patterns and Genetic Variability of Aedes Mosquitoes in Some Selected Communities of Maiduguri Metropolitan Council, Borno State, Nigeria

Aedes aegypti and Aedes albopictus mosquitoes spread dengue and yellow fever in Africa and worldwide. Rural-urban drift creates Aedes mosquito breeding sites through uncontrolled urbanization, inadequate urban infrastructure, lack of basic public health delivery, indiscriminate waste disposal, varying socioeconomic activities, and climatic changes. The above have happened in Maiduguri Metropolis since Boko Haram insurgencies began. This study will examine Aedes aegypti's seasonal distribution, abundance, composition, and genetic variability in selected Maiduguri Metropolitan Council and Jere Local Government Council communities. From October 2016 to May 2017, CDC traps and vacuum aspirators collected adult mosquitoes from study sites and stored at – 80 C. These were identified morphologically with taxonomy keys and stereomicroscopes, then molecularly with markers. Aedes mosquito population structure and genetic variability were determined using statistical software and molecular methods on samples. Hot-dry season has the highest relative abundance of this mosquito species in these study sites, while cold-dry season has the lowest. The molecular identification found Aedes aegypti in nine of twelve study locations but not Aedes albopictus. Some mosquitoes from the study locations had mutations due to carrying dengue virus or host population genes, but those without mutations showed a good phylogenetic relationship with Aedes aegypti mosquitoes from other countries, suggesting no genetic variability. The relative abundance of Aedes mosquitoes in Maiduguri Metropolitan Council increases the risk of dengue, zika, yellow fever, and chikungunya virus infections, and this mosquito species' seasonal distributions vary within and across seasons, but no genetic variability was found in the mosquitoes from the different locations used in this study.

Identification of prior dengue-naïve Dengvaxia recipients with an increased risk for symptomatic dengue during fever surveillance in the Philippines.

Dengue virus (DENV) is the leading cause of mosquito-borne viral diseases in humans. Dengvaxia, the first licensed dengue vaccine, is recommended for DENV-seropositive individuals aged 9-45 years. In the Philippines, Dengvaxia was administered to more than 830,000 children without prior serological testing in 2016-2017. Subsequently, it was revealed that DENV-seronegative children who received Dengvaxia developed severe disease following breakthrough DENV infection. As a result, thousands of children participating in the mass vaccination campaign were at higher risk of severe dengue disease. It is vital that an assay that identifies baseline DENV-naïve Dengvaxia recipients be developed and validated. This would permit more frequent and extensive assessments and timely treatment of breakthrough DENV infections. We evaluated the performance of a candidate assay, the DENV1-4 nonstructural protein 1 (NS1) IgG enzyme-linked immunosorbent assay (ELISA), developed by the University of Hawaii (UH), using well-documented serum/plasma samples including those >20 years post-DENV infection, and tested samples from 199 study participants including 100 Dengvaxia recipients from the fever surveillance programs in the Philippines. The sensitivity and specificity of the assay were 96.6% and 99.4%, respectively, which are higher than those reported for pre-vaccination screening. A significantly higher rate of symptomatic breakthrough DENV infection was found among children that were DENV-naïve (10/23) than among those that were DENV-immune (7/53) when vaccinated with Dengvaxia (p=0.004, Fisher's exact test), demonstrating the feasibility of the assay and algorithms in clinical practice. The UH DENV1-4 NS1 IgG ELISA can determine baseline DENV serostatus among Dengvaxia recipients not only during non-acute dengue but also during breakthrough DENV infection, and has implications for assessing the long-term safety and effectiveness of Dengvaxia in the post-licensure period.

Risk of Dengue and tendency map based on geographic localization of cases and vectorial infestation in the North of Brazil

Risk of Dengue and tendency map based on geographic localization of cases and vectorial infestation in the North of Brazil

Spatio-temporal dengue risk modelling in the south of Thailand: a Bayesian approach to dengue vulnerability.

More than half of the global population is predicted to be living in areas susceptible to dengue transmission with the vast majority in Asia. Dengue fever is of public health concern, particularly in the southern region of Thailand due to favourable environmental factors for its spread. The risk of dengue infection at the population level varies in time and space among sub-populations thus, it is important to study the risk of infection considering spatio-temporal variation. This study presents a joint spatio-temporal epidemiological model in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation with the CARBayesST package of R software. For this purpose, monthly dengue records by district from 2002 to 2018 from the southern region of Thailand provided by the Ministry of Public Health of Thailand and eight environmental variables were used. Results show that an increasing level of temperature, number of rainy days and sea level pressure are associated with a higher occurrence of dengue fever and consequently higher incidence risk, while an increasing level of wind speed seems to suggest a protective factor. Likewise, we found that the elevated risks of dengue in the immediate future are in the districts of Phipun, Phrom Kili, Lan Saka, Phra Phrom and Chaloem Phakiat. The resulting estimates provide insights into the effects of covariate risk factors, spatio-temporal trends and dengue-related health inequalities at the district level in southern Thailand. Possible implications are discussed considering some anthropogenic factors that could inhibit or enhance dengue occurrence. Risk maps indicated which districts are above and below baseline risk, allowing for the identification of local anomalies and high-risk boundaries. In the event of near future, the threat of elevated disease risk needs to be prevented and controlled considering the factors underlying the spread of mosquitoes in the Southeast Asian region.

The Importance of Including Non-Household Environments in Dengue Vector Control Activities.

Most vector control activities in urban areas are focused on household environments; however, information relating to infection risks in spaces other than households is poor, and the relative risk that these spaces represent has not yet been fully understood. We used data-driven simulations to investigate the importance of household and non-household environments for dengue entomological risk in two Kenyan cities where dengue circulation has been reported. Fieldwork was performed using four strategies that targeted different stages of mosquitoes: ovitraps, larval collections, Prokopack aspiration, and BG-sentinel traps. Data were analyzed separately between household and non-household environments to assess mosquito presence, the number of vectors collected, and the risk factors for vector presence. With these data, we simulated vector and human populations to estimate the parameter m and mosquito-to-human density in both household and non-household environments. Among the analyzed variables, the main difference was found in mosquito abundance, which was consistently higher in non-household environments in Kisumu but was similar in Ukunda. Risk factor analysis suggests that small, clean water-related containers serve as mosquito breeding places in households as opposed to the trash- and rainfall-related containers found in non-household structures. We found that the density of vectors (m) was higher in non-household than household environments in Kisumu and was also similar or slightly lower between both environments in Ukunda. These results suggest that because vectors are abundant, there is a potential risk of transmission in non-household environments; hence, vector control activities should take these spaces into account.

Human exposure risk assessment for infectious diseases due to temperature and air pollution: an overview of reviews.

Air pollution and global temperature change are expected to affect infectious diseases. Air pollution usually causes inflammatory response and disrupts immune defense system, while temperature mainly exacerbates the effect of vectors on humans. Yet to date overview of systematic reviews assessing the exposure risk of air pollutants and temperature on infectious diseases is unavailable. This article aims to fill this research gap. PubMed, Embase, the Cochrane Library, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature were searched. Systematic reviews and meta-analyses investigated the exposure risk of pollutants or temperature on infectious diseases were included. Two investigators screened literature, extracted data and performed the risk of bias assessments independently. A total of 23 articles met the inclusion criteria, which 3 (13%) were "low" quality and 20 (87%) were "critically low" quality. COVID-19 morbidity was associated with long-term exposure PM2.5 (RR = 1.056 per 1 [Formula: see text], 95% CI: 1.039-1.072) and NO2 (RR = 1.042 per 1 [Formula: see text], 95% CI: 1.017-1.068). In addition, for each 1°C increase in temperature, the morbidity risk of dengue increased 13% (RR = 1.130 per 1°C, 95% CI: 1.120-1.150), infectious diarrhea increased 8% (RR = 1.080 per 1°C, 95% CI: 1.050-1.200), and hand, foot and mouth disease (HFMD) increased 5% (RR = 1.050 per 1°C, 95% CI: 1.020-1.080). In conclusion, PM2.5 and NO2 increased the risk of COVID-19 and temperatures were associated with dengue, infectious diarrhoea and HFMD morbidity. Moreover, the exposure risk of temperature on COVID-19 was recommended to be further explored.

Development of machine learning modelling and dengue risk mapping: a concept framework

Dengue fever (DF) has emerged as the world most common mosquito-borne diseases. DF has more than tripled in the last five decades. The disease is primarily present in tropical and subtropical areas, putting around one-third of the world’s population at risk of infection. As a result of rising urbanization, broad global travel, a lack of sufficient mosquito control measures, and globalization, dengue viruses have spread rapidly over the world. Climate variables influence DF incidence and fatality rates, but so do sociodemographic factors, rendering certain demographic subpopulations more vulnerable to infection. However, due to the methodological difficulties associated with integrating different data sources, only a few studies addressing the causes of dengue incidence incorporate both meteorological and sociodemographic components. The application of the Geographical Information System (GIS) and Machine Learning (ML) Algorithm will be introduced in this study to act as a crucial tool in the epidemiological and spatial investigation, and the machine learning algorithm will act as a tool to analyze and make predictions based on data, without being explicitly programmed to do so. The study’s findings are supposed to aid Malaysian dengue management efforts. Although the study framework was established for DF, it can be adapted to incorporate other mosquito-borne diseases such as Malaria and Chikungunya, as well as non-mosquito-borne problems.

Singapore's 5 decades of dengue prevention and control-Implications for global dengue control.

This paper summarises the lessons learnt in dengue epidemiology, risk factors, and prevention in Singapore over the last half a century, during which Singapore evolved from a city of 1.9 million people to a highly urban globalised city-state with a population of 5.6 million. Set in a tropical climate, urbanisation among green foliage has created ideal conditions for the proliferation of Aedes aegypti and Aedes albopictus, the mosquito vectors that transmit dengue. A vector control programme, largely for malaria, was initiated as early as 1921, but it was only in 1966 that the Vector Control Unit (VCU) was established to additionally tackle dengue haemorrhagic fever (DHF) that was first documented in the 1960s. Centred on source reduction and public education, and based on research into the bionomics and ecology of the vectors, the programme successfully reduced the Aedes House Index (HI) from 48% in 1966 to <5% in the 1970s. Further enhancement of the programme, including through legislation, suppressed the Aedes HI to around 1% from the 1990s. The current programme is characterised by 4 key features: (i) proactive inter-epidemic surveillance and control that is stepped up during outbreaks; (ii) risk-based prevention and intervention strategies based on advanced data analytics; (iii) coordinated inter-sectoral cooperation between the public, private, and people sectors; and (iv) evidence-based adoption of new tools and strategies. Dengue seroprevalence and force of infection (FOI) among residents have substantially and continuously declined over the 5 decades. This is consistent with the observation that dengue incidence has been delayed to adulthood, with severity highest among the elderly. Paradoxically, the number of reported dengue cases and outbreaks has increased since the 1990s with record-breaking epidemics. We propose that Singapore's increased vulnerability to outbreaks is due to low levels of immunity in the population, constant introduction of new viral variants, expanding urban centres, and increasing human density. The growing magnitude of reported outbreaks could also be attributed to improved diagnostics and surveillance, which at least partially explains the discord between rising trend in cases and the continuous reduction in dengue seroprevalence. Changing global and local landscapes, including climate change, increasing urbanisation and global physical connectivity are expected to make dengue control even more challenging. The adoption of new vector surveillance and control tools, such as the Gravitrap and Wolbachia technology, is important to impede the growing threat of dengue and other Aedes-borne diseases.

Dengue clinical features and harbingers of severity in the diabetic patient: A retrospective cohort study on Reunion island, 2019

AimDiabetes mellitus is associated with both the risks of severe dengue and dengue-related deaths, however the factors characterizing dengue in the diabetic patient are ill-recognized. The objective of this hospital-based cohort study was to identify the factors characterizing dengue and those able to early identify dengue severity in the diabetic patient. MethodsWe retrospectively analysed demographic, clinical and biological parameters at admission in the cohort of patients who consulted at the university hospital between January and June 2019 with confirmed dengue. Bivariate and multivariate analyses were conducted. ResultsOf 936 patients, 184 patients (20%) were diabetic. One hundred and eighty-eight patients (20%) developed severe dengue according to the WHO 2009 definition. Diabetic patients were older and had more comorbidities than non-diabetics. In an age-adjusted logistic regression model, loss of appetite, altered mental status, high neutrophil to platelet ratios (>14.7), low haematocrit (≤ 38%), upper-range serum creatinine (>100 µmol/l) and high urea to creatinine ratio (>50) were indicative of dengue in the diabetic patient. A modified Poisson regression model identified four key independent harbingers of severe dengue in the diabetic patient: presence of diabetes complications, non-severe bleeding, altered mental status and cough. Among diabetes complications, diabetic retinopathy and neuropathy, but not diabetic nephropathy nor diabetic foot, were associated with severe dengue. ConclusionAt hospital first presentation, dengue in the diabetic patient is characterized by deteriorations in appetite, mental and renal functioning, while severe dengue can be early identified by presence of diabetes complications, dengue-related non-severe haemorrhages, cough, and dengue-related encephalopathy.

Risk of dengue, Zika, and chikungunya transmission in the metropolitan area of Cucuta, Colombia: cross-sectional analysis, baseline for a cluster-randomised controlled trial of a novel vector tool for water containers

BackgroundArbovirus diseases such as dengue, Zika, and chikungunya are a public health threat in tropical and subtropical areas. In the absence of a vaccine or specific treatment, vector management (in this case the control of the primary vector Aedes aegypti) is the best practice to prevent the three diseases. A good understanding of vector behaviour, ecology, human mobility and water use can help design effective vector control programmes. This study collected baseline information on these factors for identifying the arbovirus transmission risk and assessed the requirements for a large intervention trial in Colombia.MethodsBaseline surveys were conducted in 5,997 households, randomly selected from 24 clusters (neighbourhoods with on average 2000 houses and 250 households inspected) in the metropolitan area of Cucuta, Colombia. The study established population characteristics including water management and mobility as well as larval-pupal indices which were estimated and compared in all clusters. Additionally, the study estimated disease incidence from two sources: self-reported dengue cases in the household survey and cases notified by the national surveillance system.ResultsIn all 24 study clusters similar social and demographic characteristics were found but the entomological indicators and estimated disease incidence rates varied. The entomological indicators showed a high vector infestation: House Index = 25.1%, Container Index = 12.3% and Breteau Index = 29.6. Pupae per person Index (PPI) as an indicator of the transmission risk showed a large range from 0.22 to 2.04 indicating a high transmission risk in most clusters. The concrete ground tanks for laundry –mostly outdoors and uncovered- were the containers with the highest production of Ae. aegypti as 86.3% of all 17,613 pupae were identified in these containers. Also, the annual incidence of dengue was high: 841.6 self-reported cases per 100,000 inhabitants and the dengue incidence notified by the National surveillance system was 1,013.4 cases per 100,000 in 2019. Only 2.2% of the households used container water for drinking. 40.3% of the study population travelled during the day (when Aedes mosquitoes bite) outside their clusters.ConclusionsThe production of Ae. aegypti mosquitoes occurred almost exclusively in concrete ground tanks for laundry (lavadero), the primary intervention target. The baseline study provides necessary evidence for the design and implementation of a cluster randomized intervention trial in Colombia.

PICTUREE-Aedes: A Web Application for Dengue Data Visualization and Case Prediction.

Dengue fever remains a significant public health concern in many tropical and subtropical countries, and there is still a need for a system that can effectively combine global risk assessment with timely incidence forecasting. This research describes an integrated application called PICTUREE-Aedes, which can collect and analyze dengue-related data, display simulation results, and forecast outbreak incidence. PICTUREE-Aedes automatically updates global temperature and precipitation data and contains historical records of dengue incidence (1960-2012) and Aedes mosquito occurrences (1960-2014) in its database. The application utilizes a mosquito population model to estimate mosquito abundance, dengue reproduction number, and dengue risk. To predict future dengue outbreak incidence, PICTUREE-Aedes applies various forecasting techniques, including the ensemble Kalman filter, recurrent neural network, particle filter, and super ensemble forecast, which are all based on user-entered case data. The PICTUREE-Aedes' risk estimation identifies favorable conditions for potential dengue outbreaks, and its forecasting accuracy is validated by available outbreak data from Cambodia.

Spatial pattern assessment of dengue fever risk in subtropical urban environments: The case of Hong Kong

Dengue fever, a mosquito-borne fatal disease, brings a huge health burden in tropical regions. With global warming, rapid urbanization and the expansion of mosquitoes, dengue fever is expected to spread to many subtropical regions, leading to increased potential health risks on local populations. So far, limited studies assessed the dengue fever risk spatially for subtropical non-endemic regions hindering the development of related public health management. Therefore, we proposed a spatial hazard-exposure-vulnerability assessment framework for mapping the dengue fever risk in Hong Kong. Firstly, the spatial distribution of the habitat suitability for Aedes albopictus, the mosquito proxy for the dengue fever hazard, was predicted using a species distribution model (e.g., MaxEnt) relying on a list of variables related to local climate, urban morphology, and landscape metrics. Secondly, the spatial autocorrelation between high dengue hazard and high human population exposure in urban areas was measured. Finally, the dengue fever risk was assessed at community scale by integrating the results of vulnerability analysis basing on census data. This approach allowed the identification of 17 high-risk spots within Hong Kong. The landscape metrics about land utilities and vegetations, and urban morphological characteristics are the influential factors on the spatial distribution of dengue vector. In addition, the underlying factors behind each hot spot were investigated, and specific suggestions for dengue prevention were proposed accordingly. The findings provide a useful reference for developing local dengue fever risk prevention measures, with the proposed method easily exportable to other high-density cities within subtropical Asia and elsewhere.