Modeling tools for dengue risk mapping - a systematic review

Neighborhood Violence Impacts Disease Control and Surveillance: Case Study of Cali, Colombia from 2014 to 2016

Arboviruses are responsible for a large burden of disease globally and are thus subject to intense epidemiological scrutiny. However, a variable notably absent from most epidemiological analyses has been the impact of violence on arboviral transmission and surveillance. Violence impedes surveillance and delivery of health and preventative services and affects an individual’s health-related behaviors when survival takes priority. Moreover, low and middle-income countries bear a disproportionately high burden of violence and related health outcomes, including vector borne diseases. To better understand the epidemiology of arboviral outbreaks in Cali, Colombia, we georeferenced chikungunya (CHIKV), dengue (DENV), and Zika (ZIKV) viral cases from The National System of Surveillance in Public Health between October 2014 and April 2016. We extracted homicide data from the municipal monthly reports and kernel density of homicide distribution from IdeasPaz. Crucially, an overall higher risk of homicide is associated with increased risk of reported DENV, lower rates of acute testing, and higher rates of lab versus clinical discordance. In the context of high violence as a potential barrier to access to preventive health services, a community approach to improve health and peace should be considered.

Two-level resolution of relative risk of dengue disease in a hyperendemic city of Colombia

Risk maps of dengue disease offer to the public health officers a tool to model disease risk in space and time. We analyzed the geographical distribution of relative incidence risk of dengue disease in a high incidence city from Colombia, and its evolution in time during the period January 2009—December 2015, identifying regional effects at different levels of spatial aggregations. Cases of dengue disease were geocoded and spatially allocated to census sectors, and temporally aggregated by epidemiological periods. The census sectors are nested in administrative divisions defined as communes, configuring two levels of spatial aggregation for the dengue cases. Spatio-temporal models including census sector and commune-level spatially structured random effects were fitted to estimate dengue incidence relative risks using the integrated nested Laplace approximation (INLA) technique. The final selected model included two-level spatial random effects, a global structured temporal random effect, and a census sector-level interaction term. Risk maps by epidemiological period and risk profiles by census sector were generated from the modeling process, showing the transmission dynamics of the disease. All the census sectors in the city displayed high risk at some epidemiological period in the outbreak periods. Relative risk estimation of dengue disease using INLA offered a quick and powerful method for parameter estimation and inference.

Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia

The aim of this study is to estimate the parallel relative risk of Zika virus disease (ZVD) and dengue using spatio-temporal interaction effects models for one department and one city of Colombia during the 2015–2016 ZVD outbreak. We apply the integrated nested Laplace approximation (INLA) for parameter estimation, using the epidemiological week (EW) as a time measure. At the departmental level, the best model showed that the dengue or ZVD risk in one municipality was highly associated with risk in the same municipality during the preceding EWs, while at the city level, the final model selected established that the high risk of dengue or ZVD in one census sector was highly associated not only with its neighboring census sectors in the same EW, but also with its neighboring sectors in the preceding EW. The spatio-temporal models provided smoothed risk estimates, credible risk intervals, and estimation of the probability of high risk of dengue and ZVD by area and time period. We explore the intricacies of the modeling process and interpretation of the results, advocating for the use of spatio-temporal models of the relative risk of dengue and ZVD in order to generate highly valuable epidemiological information for public health decision making.

Applications of Space Technologies to Global Health: Scoping Review

Background

Space technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations’ Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health.

Objective

The aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges.

Methods

We used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs’ Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified.

Results

A total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use.

Conclusions

Although numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed.

Mapping dengue risk in Singapore using Random Forest

BACKGROUND

Singapore experiences endemic dengue, with 2013 being the largest outbreak year known to date, culminating in 22,170 cases. Given the limited resources available, and that vector control is the key approach for prevention in Singapore, it is important that public health professionals know where resources should be invested in. This study aims to stratify the spatial risk of dengue transmission in Singapore for effective deployment of resources.

METHODOLOGY/PRINCIPAL FINDINGS

Random Forest was used to predict the risk rank of dengue transmission in 1km2 grids, with dengue, population, entomological and environmental data. The predicted risk ranks are categorized and mapped to four color-coded risk groups for easy operation application. The risk maps were evaluated with dengue case and cluster data. Risk maps produced by Random Forest have high accuracy. More than 80% of the observed risk ranks fell within the 80% prediction interval. The observed and predicted risk ranks were highly correlated ([Formula: see text]≥0.86, P <0.01). Furthermore, the predicted risk levels were in excellent agreement with case density, a weighted Kappa coefficient of more than 0.80 (P <0.01). Close to 90% of the dengue clusters occur in high risk areas, and the odds of cluster forming in high risk areas were higher than in low risk areas.

CONCLUSIONS

This study demonstrates the potential of Random Forest and its strong predictive capability in stratifying the spatial risk of dengue transmission in Singapore. Dengue risk map produced using Random Forest has high accuracy, and is a good surveillance tool to guide vector control operations.

Mapping dengue risk in Singapore using Random Forest

BACKGROUND

Singapore experiences endemic dengue, with 2013 being the largest outbreak year known to date, culminating in 22,170 cases. Given the limited resources available, and that vector control is the key approach for prevention in Singapore, it is important that public health professionals know where resources should be invested in. This study aims to stratify the spatial risk of dengue transmission in Singapore for effective deployment of resources.

METHODOLOGY/PRINCIPAL FINDINGS

Random Forest was used to predict the risk rank of dengue transmission in 1km2 grids, with dengue, population, entomological and environmental data. The predicted risk ranks are categorized and mapped to four color-coded risk groups for easy operation application. The risk maps were evaluated with dengue case and cluster data. Risk maps produced by Random Forest have high accuracy. More than 80% of the observed risk ranks fell within the 80% prediction interval. The observed and predicted risk ranks were highly correlated ([Formula: see text]≥0.86, P <0.01). Furthermore, the predicted risk levels were in excellent agreement with case density, a weighted Kappa coefficient of more than 0.80 (P <0.01). Close to 90% of the dengue clusters occur in high risk areas, and the odds of cluster forming in high risk areas were higher than in low risk areas.

CONCLUSIONS

This study demonstrates the potential of Random Forest and its strong predictive capability in stratifying the spatial risk of dengue transmission in Singapore. Dengue risk map produced using Random Forest has high accuracy, and is a good surveillance tool to guide vector control operations.

A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka

Background

Dengue is one of the major health problems in Sri Lanka causing an enormous social and economic burden to the country. An accurate early warning system can enhance the efficiency of preventive measures. The aim of the study was to develop and validate a simple accurate forecasting model for the District of Gampaha, Sri Lanka. Three time-series regression models were developed using monthly rainfall, rainy days, temperature, humidity, wind speed and retrospective dengue incidences over the period January 2012 to November 2015 for the District of Gampaha, Sri Lanka. Various lag times were analyzed to identify optimum forecasting periods including interactions of multiple lags. The models were validated using epidemiological data from December 2015 to November 2017. Prepared models were compared based on Akaike’s information criterion, Bayesian information criterion and residual analysis.

Results

The selected model forecasted correctly with mean absolute errors of 0.07 and 0.22, and root mean squared errors of 0.09 and 0.28, for training and validation periods, respectively. There were no dengue epidemics observed in the district during the training period and nine outbreaks occurred during the forecasting period. The proposed model captured five outbreaks and correctly rejected 14 within the testing period of 24 months. The Pierce skill score of the model was 0.49, with a receiver operating characteristic of 86% and 92% sensitivity.

Conclusions

The developed weather based forecasting model allows warnings of impending dengue outbreaks and epidemics in advance of one month with high accuracy. Depending upon climatic factors, the previous month’s dengue cases had a significant effect on the dengue incidences of the current month. The simple, precise and understandable forecasting model developed could be used to manage limited public health resources effectively for patient management, vector surveillance and intervention programmes in the district.

Electronic supplementary material

The online version of this article (10.1186/s13071-018-2828-2) contains supplementary material, which is available to authorized users.

Spatial and Temporal Characteristics of 2014 Dengue Outbreak in Guangdong, China

The record-breaking number of dengue cases reported in Guangdong, China in 2014 has been topic for many studies. However, the spatial and temporal characteristics of this unexpectedly explosive outbreak are still poorly understood. We adopt an integrated approach to ascertain the spatial-temporal progression of the outbreak in each city in Guangdong as well as in each district in Guangzhou, where the majority of cases occurred. We utilize the Richards model, which determines the waves of reported cases at each location and identifies the turning point for each wave, in combination with a spatial association analysis conducted by computing the standardized G* statistic that measures the degree of spatial autocorrelation of a set of geo-referenced data from a local perspective. We found that Yuexiu district in Guangzhou was the initial hot spot for the outbreak, subsequently spreading to its neighboring districts in Guangzhou and other cities in Guangdong province. Hospital isolation of cases during early stage of outbreak in neighboring Zhongshan (in effort to prevent disease transmission to the vectors) might have played an important role in the timely mitigation of the disease. Integration of modeling approach and spatial association analysis allows us to pinpoint waves that spread the disease to communities beyond the borders of the initially affected regions.

Novel tools for the surveillance and control of dengue: findings by the DengueTools research consortium

BACKGROUND

Dengue fever persists as a major global disease burden, and may increase as a consequence of climate change. Along with other measures, research actions to improve diagnosis, surveillance, prevention, and predictive models are highly relevant. The European Commission funded the DengueTools consortium to lead a major initiative in these areas, and this review synthesises the outputs and findings of this work conducted from 2011 to 2016. Research areas: DengueTools organised its work into three research areas, namely [1] Early warning and surveillance systems; [2] Strategies to prevent dengue in children; and [3] Predictive models for the global spread of dengue. Research area 1 focused on case-studies undertaken in Sri Lanka, including developing laboratory-based sentinel surveillance, evaluating economic impact, identifying drivers of transmission intensity, evaluating outbreak prediction capacity and developing diagnostic capacity. Research area 2 addressed preventing dengue transmission in school children, with case-studies undertaken in Thailand. Insecticide-treated school uniforms represented an intriguing potential approach, with some encouraging results, but which were overshadowed by a lack of persistence of insecticide on the uniforms with repeated washing. Research area 3 evaluated potential global spread of dengue, particularly into dengue-naïve areas such as Europe. The role of international travel, changing boundaries of vectors, developing models of vectorial capacity under different climate change scenarios and strategies for vector control in outbreaks was all evaluated.

CONCLUDING REMARKS

DengueTools was able to make significant advances in methods for understanding and controlling dengue transmission in a range of settings. These will have implications for public health agendas to counteract dengue, including vaccination programmes.

OUTLOOK

Towards the end of the DengueTools project, Zika virus emerged as an unexpected epidemic in the central and southern America. Given the similarities between the dengue and Zika viruses, with vectors in common, some of the DengueTools thinking translated readily into the Zika situation.

Estimating Effects of Temperature on Dengue Transmission in Colombian Cities

BACKGROUND

Dengue fever is a viral disease that affects tropical and subtropical regions of the world. It is well known that processes related to virus transmission by mosquitoes are highly influenced by weather. Temperature has been described as one of the climatic variables that largely governs the development and survival of mosquito eggs as well as the survival of all insect stages. Previously, we noted that high temperatures in the Colombian city of Riohacha negatively affect the establishment of dengue virus (DENV) infection in mosquitoes; in Bello and Villavicencio cities, which have lower average temperatures, DENV infection rates in mosquitoes are positively associated with a gradual increase in temperature. Here, we test the hypothesis that a similar effect of temperature can be detected in the incidence in the human population inhabiting dengue-endemic cities in Colombia.

OBJECTIVE

Our objective was to evaluate the effect of climate variables related to temperature on DENV incidence in human populations living in DENV-endemic cities in Colombia.

METHODS

Epidemiologic data from the Instituto Nacional de Salud from 2012-2015 and 7 variables related to temperature were used to perform Spearman rank sum test analyses on 20 Colombian cities. Additionally, locally estimated scatterplot smoothing analyses were performed to describe the relationship among temperatures and incidence.

FINDINGS

Results indicated that Colombian cities with average and maximum temperatures greater than 28°C and 32°C, respectively, had an inversely related relationship to DENV incidence, which is in accordance with areas where higher temperatures are recorded in Colombia.

CONCLUSION

Climatic variables related to temperature affect dengue epidemiology in different way. According to the temperature of each city, transmission might be positively or negatively affected.

Relative risk estimation of dengue disease at small spatial scale

Background

Dengue is a high incidence arboviral disease in tropical countries around the world. Colombia is an endemic country due to the favourable environmental conditions for vector survival and spread. Dengue surveillance in Colombia is based in passive notification of cases, supporting monitoring, prediction, risk factor identification and intervention measures. Even though the surveillance network works adequately, disease mapping techniques currently developed and employed for many health problems are not widely applied. We select the Colombian city of Bucaramanga to apply Bayesian areal disease mapping models, testing the challenges and difficulties of the approach.

Methods

We estimated the relative risk of dengue disease by census section (a geographical unit composed approximately by 1–20 city blocks) for the period January 2008 to December 2015. We included the covariates normalized difference vegetation index (NDVI) and land surface temperature (LST), obtained by satellite images. We fitted Bayesian areal models at the complete period and annual aggregation time scales for 2008–2015, with fixed and space-varying coefficients for the covariates, using Markov Chain Monte Carlo simulations. In addition, we used Cohen’s Kappa agreement measures to compare the risk from year to year, and from every year to the complete period aggregation.

Results

We found the NDVI providing more information than LST for estimating relative risk of dengue, although their effects were small. NDVI was directly associated to high relative risk of dengue. Risk maps of dengue were produced from the estimates obtained by the modeling process. The year to year risk agreement by census section was sligth to fair.

Conclusion

The study provides an example of implementation of relative risk estimation using Bayesian models for disease mapping at small spatial scale with covariates. We relate satellite data to dengue disease, using an areal data approach, which is not commonly found in the literature. The main difficulty of the study was to find quality data for generating expected values as input for the models. We remark the importance of creating population registry at small spatial scale, which is not only relevant for the risk estimation of dengue but also important to the surveillance of all notifiable diseases.

An economic evaluation of vector control in the age of a dengue vaccine

INTRODUCTION

Dengue is a rapidly emerging vector-borne Neglected Tropical Disease, with a 30-fold increase in the number of cases reported since 1960. The economic cost of the illness is measured in the billions of dollars annually. Environmental change and unplanned urbanization are conspiring to raise the health and economic cost even further beyond the reach of health systems and households. The health-sector response has depended in large part on control of the Aedes aegypti and Ae. albopictus (mosquito) vectors. The cost-effectiveness of the first-ever dengue vaccine remains to be evaluated in the field. In this paper, we examine how it might affect the cost-effectiveness of sustained vector control.

METHODS

We employ a dynamic Markov model of the effects of vector control on dengue in both vectors and humans over a 15-year period, in six countries: Brazil, Columbia, Malaysia, Mexico, the Philippines, and Thailand. We evaluate the cost (direct medical costs and control programme costs) and cost-effectiveness of sustained vector control, outbreak response and/or medical case management, in the presence of a (hypothetical) highly targeted and low cost immunization strategy using a (non-hypothetical) medium-efficacy vaccine.

RESULTS

Sustained vector control using existing technologies would cost little more than outbreak response, given the associated costs of medical case management. If sustained use of existing or upcoming technologies (of similar price) reduce vector populations by 70-90%, the cost per disability-adjusted life year averted is 2013 US$ 679-1331 (best estimates) relative to no intervention. Sustained vector control could be highly cost-effective even with less effective technologies (50-70% reduction in vector populations) and in the presence of a highly targeted and low cost immunization strategy using a medium-efficacy vaccine.

DISCUSSION

Economic evaluation of the first-ever dengue vaccine is ongoing. However, even under very optimistic assumptions about a highly targeted and low cost immunization strategy, our results suggest that sustained vector control will continue to play an important role in mitigating the impact of environmental change and urbanization on human health. If additional benefits for the control of other Aedes borne diseases, such as Chikungunya, yellow fever and Zika fever are taken into account, the investment case is even stronger. High-burden endemic countries should proceed to map populations to be covered by sustained vector control.

Space and space-time distributions of dengue in a hyper-endemic urban space: the case of Girardot, Colombia

Background

Dengue is a widely spread vector-borne disease. Dengue cases in the Americas have increased over the last few decades, affecting various urban spaces throughout these continents, including the tourism-oriented city of Girardot, Colombia. Interactions among mosquitoes, pathogens and humans have recently been examined using different temporal and spatial scales in attempts to determine the roles that social and ecological systems play in dengue transmission. The current work characterizes the spatial and temporal behaviours of dengue in Girardot and discusses the potential territorial dynamics related to the distribution of this disease.

Methods

Based on officially reported dengue cases (2012–2015) corresponding to epidemic (2013) and inter-epidemic years (2012, 2014, 2015), space (Getis-Ord index) and space-time (Kulldorff’s scan statistics) analyses were performed.

Results

Geocoded dengue cases (n = 2027) were slightly overrepresented by men (52.1%). As expected, the cases were concentrated in the 0- to 15-year-old age group according to the actual trends of Colombia. The incidence rates of dengue during the rainy and dry seasons as well as those for individual years (2012, 2013 and 2014) were significant using the global Getis-Ord index. Local clusters shifted across seasons and years; nevertheless, the incidence rates clustered towards the southwest region of the city under different residential conditions. Space-time clusters shifted from the northeast to the southwest of the city (2012–2014). These clusters represented only 4.25% of the total cases over the same period (n = 1623). A general trend was observed, in which dengue cases increased during the dry seasons, especially between December and February.

Conclusions

Despite study limitations related to official dengue records and available fine-scale demographic information, the spatial analysis results were promising from a geography of health perspective. Dengue did not show linear association with poverty or with vulnerable peripheral spaces in intra-urban settings, supporting the idea that the pathogenic complex of dengue is driven by different factors. A coordinated collaboration of epidemiological, public health and social science expertise is needed to assess the effect of “place” from a relational perspective in which geography has an important role to play.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-017-2610-7) contains supplementary material, which is available to authorized users.

Spatial patterns of dengue cases in Brazil

Dengue infection plays a central role in our society, since it is the most prevalent vector-borne viral disease affecting humans. We statistically investigated patterns concerning the spatial spreading of dengue epidemics in Brazil, as well as their temporal evolution in all Brazilian municipalities for a period of 12 years. We showed that the distributions of cases in municipalities follow power laws persistent in time and that the infection scales linearly with the population of the municipalities. We also found that the average number of dengue cases does not have a clear dependence on the longitudinal position of municipalities. On the other hand, we found that the average distribution of cases varies with the latitudinal position of municipalities, displaying an almost constant growth from high latitudes until reaching the Tropic of Capricorn leveling to a plateau closer to the Equator. We also characterized the spatial correlation of the number of dengue cases between pairs of municipalities, where our results showed that the spatial correlation function decays with the increase of distance between municipalities, following a power-law with an exponential cut-off. This regime leads to a typical dengue traveling distance. Finally, we considered modeling this last behaviour within the framework of a Edwards-Wilkinson equation with a fractional derivative on space.

Early warning signal for dengue outbreaks and identification of high risk areas for dengue fever in Colombia using climate and non-climate datasets

Background

Dengue has been prevalent in Colombia with high risk of outbreaks in various locations. While the prediction of dengue epidemics will bring significant benefits to the society, accurate forecasts have been a challenge. Given competing health demands in Colombia, it is critical to consider the effective use of the limited healthcare resources by identifying high risk areas for dengue fever.

Methods

The Climate Risk Factor (CRF) index was constructed based upon temperature, precipitation, and humidity. Considering the conditions necessary for vector survival and transmission behavior, elevation and population density were taken into account. An Early Warning Signal (EWS) model was developed by estimating the elasticity of the climate risk factor function to detect dengue epidemics. The climate risk factor index was further estimated at the smaller geographical unit (5 km by 5 km resolution) to identify populations at high risk.

Results

From January 2007 to December 2015, the Early Warning Signal model successfully detected 75% of the total number of outbreaks 1 ~ 5 months ahead of time, 12.5% in the same month, and missed 12.5% of all outbreaks. The climate risk factors showed that populations at high risk are concentrated in the Western part of Colombia where more suitable climate conditions for vector mosquitoes and the high population level were observed compared to the East.

Conclusions

This study concludes that it is possible to detect dengue outbreaks ahead of time and identify populations at high risk for various disease prevention activities based upon observed climate and non-climate information. The study outcomes can be used to minimize potential societal losses by prioritizing limited healthcare services and resources, as well as by conducting vector control activities prior to experiencing epidemics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-017-2577-4) contains supplementary material, which is available to authorized users.

Effect of Temperature Thresholds on Modeled Aedes aegypti (Diptera: Culicidae) Population Dynamics

Dynamic simulation models provide vector abundance estimates using only meteorological data. However, model outcomes may heavily depend on the assumptions used to parameterize them. We conducted a sensitivity analysis for a model of Aedes aegypti (L.) abundance using weather data from two locations where this vector is established, La Margarita, Puerto Rico and Tucson, Arizona. We tested the effect of simplifying temperature-dependent development and mortality rates and of changing development and mortality thresholds as compared with baselines estimated using biophysical models. The simplified development and mortality rates had limited effect on abundance estimates in either location. However, in Tucson, where the vector is established but has not transmitted viruses, a difference of 5 °C resulted in populations either surviving or collapsing in the hot Arizona mid-summer, depending on the temperature thresholds. We find three important implications of the observed sensitivity to temperature thresholds. First, this analysis indicates the need for better estimates of the temperature tolerance thresholds to refine entomologic risk mapping for disease vectors. Second, our results highlight the importance of extreme temperatures on vector survival at the marginal areas of this vector's distribution. Finally, the model suggests that adaptation to warmer temperatures may shift regions of pathogen transmission.

Joint effects of climate variability and socioecological factors on dengue transmission: epidemiological evidence

OBJECTIVE

To assess the epidemiological evidence on the joint effects of climate variability and socioecological factors on dengue transmission.

METHODS

Following PRISMA guidelines, a detailed literature search was conducted in PubMed, Web of Science and Scopus. Peer-reviewed, freely available and full-text articles, considering both climate and socioecological factors in relation to dengue, published in English from January 1993 to October 2015 were included in this review.

RESULTS

Twenty studies have met the inclusion criteria and assessed the impact of both climatic and socioecological factors on dengue dynamics. Among those, four studies have further investigated the relative importance of climate variability and socioecological factors on dengue transmission. A few studies also developed predictive models including both climatic and socioecological factors.

CONCLUSIONS

Due to insufficient data, methodological issues and contextual variability of the studies, it is hard to draw conclusion on the joint effects of climate variability and socioecological factors on dengue transmission. Future research should take into account socioecological factors in combination with climate variables for a better understanding of the complex nature of dengue transmission as well as for improving the predictive capability of dengue forecasting models, to develop effective and reliable early warning systems.

Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China

BACKGROUND

Dengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an early warning model that integrates internet-based query data into traditional surveillance data.

METHODOLOGY AND PRINCIPAL FINDINGS

A Dengue Baidu Search Index (DBSI) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors. Generalized additive models (GAM) with or without DBSI were established. The generalized cross validation (GCV) score and deviance explained indexes, intraclass correlation coefficient (ICC) and root mean squared error (RMSE), were respectively applied to measure the fitness and the prediction capability of the models. Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence, and the model with DBSI (ICC:0.94 and RMSE:59.86) has a better prediction capability than the model without DBSI (ICC:0.72 and RMSE:203.29).

CONCLUSIONS

Our study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou.

HELZ2 Is an IFN Effector Mediating Suppression of Dengue Virus

Flaviviral infections including dengue virus are an increasing clinical problem worldwide. Dengue infection triggers host production of the type 1 IFN, IFN alpha, one of the strongest and broadest acting antivirals known. However, dengue virus subverts host IFN signaling at early steps of IFN signal transduction. This subversion allows unbridled viral replication which subsequently triggers ongoing production of IFN which, again, is subverted. Identification of downstream IFN antiviral effectors will provide targets which could be activated to restore broad acting antiviral activity, stopping the signal to produce endogenous IFN at toxic levels. To this end, we performed a targeted functional genomic screen for IFN antiviral effector genes (IEGs), identifying 56 IEGs required for antiviral effects of IFN against fully infectious dengue virus. Dengue IEGs were enriched for genes encoding nuclear receptor interacting proteins, including HELZ2, MAP2K4, SLC27A2, HSP90AA1, and HSP90AB1. We focused on HELZ2 (Helicase With Zinc Finger 2), an IFN stimulated gene and IEG which encodes a promiscuous nuclear factor coactivator that exists in two isoforms. The two unique HELZ2 isoforms are both IFN responsive, contain ISRE elements, and gene products increase in the nucleus upon IFN stimulation. Chromatin immunoprecipitation-sequencing revealed that the HELZ2 complex interacts with triglyceride-regulator LMF1. Mass spectrometry revealed that HELZ2 knockdown cells are depleted of triglyceride subsets. We thus sought to determine whether HELZ2 interacts with a nuclear receptor known to regulate immune response and lipid metabolism, AHR, and identified HELZ2:AHR interactions via co-immunoprecipitation, found that AHR is a dengue IEG, and that an AHR ligand, FICZ, exhibits anti-dengue activity. Primary bone marrow derived macrophages from HELZ2 knockout mice, compared to wild type controls, exhibit enhanced dengue infectivity. Overall, these findings reveal that IFN antiviral response is mediated by HELZ2 transcriptional upregulation, enrichment of HELZ2 protein levels in the nucleus, and activation of a transcriptional program that appears to modulate intracellular lipid state. IEGs identified in this study may serve as both (1) potential targets for host directed antiviral design, downstream of the common flaviviral subversion point, as well as (2) possible biomarkers, whose variation, natural, or iatrogenic, could affect host response to viral infections.

Evaluation of the importance of fever with respect to dengue prognosis according to the 2009 WHO classification: a retrospective study

Background

The 2009 revised World Health Organization (WHO) guidelines for dengue describe fever as the core symptom. Accordingly, the diagnosis of non-febrile patients is complicated. The aim of this study was to evaluate the importance of fever in patients with dengue according to the 2009 revised WHO classification.

Methods

In this study, we assessed 30,670 dengue cases using enzyme-linked immunosorbent assay, detection of the non-structural protein 1, or polymerase chain reaction for diagnostic confirmation. Fisher’s exact test was used to evaluate associations between fever and related clinical manifestations. The Mann–Whitney U test was used to assess the association of dengue classification with fever and time to treatment. The effects of fever and time to treatment on the risk of progression were analyzed using an ordinal logistic regression to stereotype the model.

Results

Disease classification was found to associate significantly with both fever and time to treatment (both P < 0.001). Non-febrile patients were nearly four-fold more likely to exhibit “dengue without warning signs” than “severe dengue” (odds ratio [OR] = 3.74; 95% confidence interval [CI]: 3.20–4.36). Patients who received treatment within 7 days were twice as likely to have “dengue without warning signs” as opposed to “severe dengue” when compared to those who waited >7 days (OR = 2.23; 95% CI: 1.78–2.80). However, this difference was negligible in the multivariate analysis (OR = 1.02; 95% CI: 0.98–1.07).

Conclusions

Fever is a risk factor for disease progression in patients with dengue. However, non-febrile patients should not be neglected because this may delay treatment and could lead to more severe disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-2128-4) contains supplementary material, which is available to authorized users.

Aedes aegypti (Diptera: Culicidae) Longevity and Differential Emergence of Dengue Fever in Two Cities in Sonora, Mexico

Dengue virus, primarily transmitted by the Aedes aegypti (L.) mosquito, has rapidly expanded in geographic extent over the past several decades. In some areas, however, dengue fever has not emerged despite established Ae. aegypti populations. The reasons for this are unclear and have sometimes been attributed to socio-economic differences. In 2013 we compared Ae. aegypti adult density and population age structure between two cities in Sonora, Mexico: Hermosillo, which has regular seasonal dengue virus transmission, and Nogales, which has minimal transmission. Larval and pupal abundance was greater in Nogales, and adult density was only higher in Hermosillo during September. Population age structure, however, was consistently older in Hermosillo. This difference in longevity may have been one factor that limited dengue virus transmission in Nogales in 2013, as a smaller proportion of Ae. aegypti females survived past the extrinsic incubation period.

The Importance of Socio-Economic Versus Environmental Risk Factors for Reported Dengue Cases in Java, Indonesia

BACKGROUND

Dengue is a major mosquito-borne viral disease and an important public health problem. Identifying which factors are important determinants in the risk of dengue infection is critical in supporting and guiding preventive measures. In South-East Asia, half of all reported fatal infections are recorded in Indonesia, yet little is known about the epidemiology of dengue in this country.

METHODOLOGY/PRINCIPAL FINDINGS

Hospital-reported dengue cases in Banyumas regency, Central Java were examined to build Bayesian spatial and spatio-temporal models assessing the influence of climatic, demographic and socio-economic factors on the risk of dengue infection. A socio-economic factor linking employment type and economic status was the most influential on the risk of dengue infection in the Regency. Other factors such as access to healthcare facilities and night-time temperature were also found to be associated with higher risk of reported dengue infection but had limited explanatory power.

CONCLUSIONS/SIGNIFICANCE

Our data suggest that dengue infections are triggered by indoor transmission events linked to socio-economic factors (employment type, economic status). Preventive measures in this area should therefore target also specific environments such as schools and work areas to attempt and reduce dengue burden in this community. Although our analysis did not account for factors such as variations in immunity which need further investigation, this study can advise preventive measures in areas with similar patterns of reported dengue cases and environment.

Alarm Variables for Dengue Outbreaks: A Multi-Centre Study in Asia and Latin America

BACKGROUND

Worldwide, dengue is an unrelenting economic and health burden. Dengue outbreaks have become increasingly common, which place great strain on health infrastructure and services. Early warning models could allow health systems and vector control programmes to respond more cost-effectively and efficiently.

METHODOLOGY/PRINCIPAL FINDINGS

The Shewhart method and Endemic Channel were used to identify alarm variables that may predict dengue outbreaks. Five country datasets were compiled by epidemiological week over the years 2007-2013. These data were split between the years 2007-2011 (historic period) and 2012-2013 (evaluation period). Associations between alarm/ outbreak variables were analysed using logistic regression during the historic period while alarm and outbreak signals were captured during the evaluation period. These signals were combined to form alarm/ outbreak periods, where 2 signals were equal to 1 period. Alarm periods were quantified and used to predict subsequent outbreak periods. Across Mexico and Dominican Republic, an increase in probable cases predicted outbreaks of hospitalised cases with sensitivities and positive predictive values (PPV) of 93%/ 83% and 97%/ 86% respectively, at a lag of 1-12 weeks. An increase in mean temperature ably predicted outbreaks of hospitalised cases in Mexico and Brazil, with sensitivities and PPVs of 79%/ 73% and 81%/ 46% respectively, also at a lag of 1-12 weeks. Mean age was predictive of hospitalised cases at sensitivities and PPVs of 72%/ 74% and 96%/ 45% in Mexico and Malaysia respectively, at a lag of 4-16 weeks.

CONCLUSIONS/SIGNIFICANCE

An increase in probable cases was predictive of outbreaks, while meteorological variables, particularly mean temperature, demonstrated predictive potential in some countries, but not all. While it is difficult to define uniform variables applicable in every country context, the use of probable cases and meteorological variables in tailored early warning systems could be used to highlight the occurrence of dengue outbreaks or indicate increased risk of dengue transmission.

City-Specific Spatiotemporal Infant and Neonatal Mortality Clusters: Links with Socioeconomic and Air Pollution Spatial Patterns in France

Infant and neonatal mortality indicators are known to vary geographically, possibly as a result of socioeconomic and environmental inequalities. To better understand how these factors contribute to spatial and temporal patterns, we conducted a French ecological study comparing two time periods between 2002 and 2009 for three (purposefully distinct) Metropolitan Areas (MAs) and the city of Paris, using the French census block of parental residence as the geographic unit of analysis. We identified areas of excess risk and assessed the role of neighborhood deprivation and average nitrogen dioxide concentrations using generalized additive models to generate maps smoothed on longitude and latitude. Comparison of the two time periods indicated that statistically significant areas of elevated infant and neonatal mortality shifted northwards for the city of Paris, are present only in the earlier time period for Lille MA, only in the later time period for Lyon MA, and decrease over time for Marseille MA. These city-specific geographic patterns in neonatal and infant mortality are largely explained by socioeconomic and environmental inequalities. Spatial analysis can be a useful tool for understanding how risk factors contribute to disparities in health outcomes ranging from infant mortality to infectious disease-a leading cause of infant mortality.

Projected Impact of Dengue Vaccination in Yucatán, Mexico

Dengue vaccines will soon provide a new tool for reducing dengue disease, but the effectiveness of widespread vaccination campaigns has not yet been determined. We developed an agent-based dengue model representing movement of and transmission dynamics among people and mosquitoes in Yucatán, Mexico, and simulated various vaccine scenarios to evaluate effectiveness under those conditions. This model includes detailed spatial representation of the Yucatán population, including the location and movement of 1.8 million people between 375,000 households and 100,000 workplaces and schools. Where possible, we designed the model to use data sources with international coverage, to simplify re-parameterization for other regions. The simulation and analysis integrate 35 years of mild and severe case data (including dengue serotype when available), results of a seroprevalence survey, satellite imagery, and climatological, census, and economic data. To fit model parameters that are not directly informed by available data, such as disease reporting rates and dengue transmission parameters, we developed a parameter estimation toolkit called AbcSmc, which we have made publicly available. After fitting the simulation model to dengue case data, we forecasted transmission and assessed the relative effectiveness of several vaccination strategies over a 20 year period. Vaccine efficacy is based on phase III trial results for the Sanofi-Pasteur vaccine, Dengvaxia. We consider routine vaccination of 2, 9, or 16 year-olds, with and without a one-time catch-up campaign to age 30. Because the durability of Dengvaxia is not yet established, we consider hypothetical vaccines that confer either durable or waning immunity, and we evaluate the use of booster doses to counter waning. We find that plausible vaccination scenarios with a durable vaccine reduce annual dengue incidence by as much as 80% within five years. However, if vaccine efficacy wanes after administration, we find that there can be years with larger epidemics than would occur without any vaccination, and that vaccine booster doses are necessary to prevent this outcome.

Dengue is a mosquito-transmitted viral disease that is common throughout the tropics. Despite a long history in humans and extensive efforts to control dengue transmission in many countries, the number, severity, and geographic range of reported cases is increasing. Most control efforts have focused on controlling mosquito populations, but the main vector, Aedes aegypti, flourishes in human-disturbed and indoor environments. Because the mosquitoes prefer to bite during the day when people are active and potentially moving around high-risk locations, fixed barriers like bed nets are not effective. Several dengue vaccines are being actively developed and may become valuable tools in dengue control. Using historical dengue data from Yucatán, Mexico, we fit a detailed simulation of human and mosquito populations to project future transmission, then used efficacy data from vaccine trials to evaluate the benefit of potential vaccination deployment strategies in the region. For a durable vaccine, we find that population-level, annual vaccine effectiveness approaches 65% by the end of the 20-year forecast period. For waning vaccines, however, effectiveness is greatly reduced–and sometimes negative–unless booster vaccinations are used.

Temporal Dynamics and Spatial Patterns of Aedes aegypti Breeding Sites, in the Context of a Dengue Control Program in Tartagal (Salta Province, Argentina)

Background

Since 2009, Fundación Mundo Sano has implemented an Aedes aegypti Surveillance and Control Program in Tartagal city (Salta Province, Argentina). The purpose of this study was to analyze temporal dynamics of Ae. aegypti breeding sites spatial distribution, during five years of samplings, and the effect of control actions over vector population dynamics.

Methodology/Principal Findings

Seasonal entomological (larval) samplings were conducted in 17,815 fixed sites in Tartagal urban area between 2009 and 2014. Based on information of breeding sites abundance, from satellite remote sensing data (RS), and by the use of Geographic Information Systems (GIS), spatial analysis (hotspots and cluster analysis) and predictive model (MaxEnt) were performed. Spatial analysis showed a distribution pattern with the highest breeding densities registered in city outskirts. The model indicated that 75% of Ae. aegypti distribution is explained by 3 variables: bare soil coverage percentage (44.9%), urbanization coverage percentage(13.5%) and water distribution (11.6%).

Conclusions/Significance

This results have called attention to the way entomological field data and information from geospatial origin (RS/GIS) are used to infer scenarios which could then be applied in epidemiological surveillance programs and in the determination of dengue control strategies. Predictive maps development constructed with Ae. aegypti systematic spatiotemporal data, in Tartagal city, would allow public health workers to identify and target high-risk areas with appropriate and timely control measures. These tools could help decision-makers to improve health system responses and preventive measures related to vector control.

As reported in Porcasi et al., in Argentina we are working on an integrated risk stratification system based in geospatial technologies that have moderately consolidated national scale, but need more understanding of its urban scale mechanisms. In this work, relevant results are shown on how Ae. aegypti breeding sites are distributed in dynamic spatial patterns in a small city on northern Argentina. 5 years of entomologic data were obtained by Mundo Sano Foundation, which is implementing an Aedes aegypti Surveillance and Control Program in Tartagal City (Salta Province, Argentina). The focus of this contribution is based on the difference that can found between one year data typical analysis and long term temporal evolution of spatial patterns. Although environmental sanitation activities and chemical control of breeding sites with larvicide were performed after each entomological surveillance all around Tartagal, sectors with higher densities of breeding sites remained present throughout study period. Nonetheless, the distribution of breeding sites showed a spatial dynamic with high density clusters in city outskirts.

Dengue and chikungunya: modelling the expansion of mosquito-borne viruses into naïve populations

With the recent global spread of a number of mosquito-borne viruses, there is an urgent need to understand the factors that contribute to the ability of viruses to expand into naïve populations. Using dengue and chikungunya viruses as case studies, we detail the necessary components of the expansion process: presence of the mosquito vector; introduction of the virus; and suitable conditions for local transmission. For each component we review the existing modelling approaches that have been used to understand recent emergence events or to assess the risk of future expansions. We identify gaps in our knowledge that are related to each of the distinct aspects of the human-mosquito transmission cycle: mosquito ecology; human-mosquito contact; mosquito-virus interactions; and human-virus interactions. Bridging these gaps poses challenges to both modellers and empiricists, but only through further integration of models and data will we improve our ability to better understand, and ultimately control, several infectious diseases that exert a significant burden on human health.

Climate Change and Aedes Vectors: 21st Century Projections for Dengue Transmission in Europe

Warming temperatures may increase the geographic spread of vector-borne diseases into temperate areas. Although a tropical mosquito-borne viral disease, a dengue outbreak occurred in Madeira, Portugal, in 2012; the first in Europe since 1920s. This outbreak emphasizes the potential for dengue re-emergence in Europe given changing climates. We present estimates of dengue epidemic potential using vectorial capacity (VC) based on historic and projected temperature (1901–2099). VC indicates the vectors' ability to spread disease among humans. We calculated temperature-dependent VC for Europe, highlighting 10 European cities and three non-European reference cities. Compared with the tropics, Europe shows pronounced seasonality and geographical heterogeneity. Although low, VC during summer is currently sufficient for dengue outbreaks in Southern Europe to commence–if sufficient vector populations (either Ae. aegypti and Ae. albopictus) were active and virus were introduced. Under various climate change scenarios, the seasonal peak and time window for dengue epidemic potential increases during the 21st century. Our study maps dengue epidemic potential in Europe and identifies seasonal time windows when major cities are most conducive for dengue transmission from 1901 to 2099. Our findings illustrate, that besides vector control, mitigating greenhouse gas emissions crucially reduces the future epidemic potential of dengue in Europe.

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Past and present assessments of VC indicate strong seasonal patterns in temperate climates' dengue epidemic potential.

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Current VC intensity could permit summer dengue epidemics in Southern Europe driven by either Aedes vector, where present.

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Extent of spatial and temporal VC changes depend on vector characteristics and projected greenhouse gas emissions scenarios.

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With climate change, future VC intensifies: shifting northward and prolonging the season suitable for dengue epidemics.

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By the 21st century's end, seasonal dengue outbreaks could emerge in much more of Europe if Aedes vectors were established.

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Achieving the Paris Agreement's emission reduction commitments could decelerate the increasing threat of dengue to Europe.

Globalization and climate change can increase the geographic spread of vector-borne diseases. Among those, dengue, a mosquito-transmitted viral disease, causes up to 390 million human infections annually. This study evaluates potential for dengue outbreaks in Europe based on climate conditions. Estimated suitability (1901–2099) for dengue outbreaks is expanding presently from Southern Europe northward and lengthening seasonally up to eight months around the summer; however, the projected extent, intensity and duration depend partially on greenhouse gas emissions. We conclude that limiting emissions thereby mitigating climate change could substantially reduce the likelihood of dengue transmission events in Europe during the 21st century.

Predicting unprecedented dengue outbreak using imported cases and climatic factors in Guangzhou, 2014

INTRODUCTION

Dengue is endemic in more than 100 countries, mainly in tropical and subtropical regions, and the incidence has increased 30-fold in the past 50 years. The situation of dengue in China has become more and more severe, with an unprecedented dengue outbreak hitting south China in 2014. Building a dengue early warning system is therefore urgent and necessary for timely and effective response.

METHODOLOGY AND PRINCIPAL FINDINGS

In the study we developed a time series Poisson multivariate regression model using imported dengue cases, local minimum temperature and accumulative precipitation to predict the dengue occurrence in four districts of Guangzhou, China. The time series data were decomposed into seasonal, trend and remainder components using a seasonal-trend decomposition procedure based on loess (STL). The time lag of climatic factors included in the model was chosen based on Spearman correlation analysis. Autocorrelation, seasonality and long-term trend were controlled in the model. A best model was selected and validated using Generalized Cross Validation (GCV) score and residual test. The data from March 2006 to December 2012 were used to develop the model while the data from January 2013 to September 2014 were employed to validate the model. Time series Poisson model showed that imported cases in the previous month, minimum temperature in the previous month and accumulative precipitation with three month lags could project the dengue outbreaks occurred in 2013 and 2014 after controlling the autocorrelation, seasonality and long-term trend.

CONCLUSIONS

Together with the sole transmission vector Aedes albopictus, imported cases, monthly minimum temperature and monthly accumulative precipitation may be used to develop a low-cost effective early warning system.