Environmental factors and incidence of dengue fever and dengue haemorrhagic fever in an urban area, Southern Thailand

A systematic review of published literature on mosquito control action thresholds across the world

BACKGROUND

Despite the use of numerous methods of control measures, mosquito populations and mosquito-borne diseases are still increasing globally. Evidence-based action thresholds to initiate or intensify control activities have been identified as essential in reducing mosquito populations to required levels at the correct/optimal time. This systematic review was conducted to identify different mosquito control action thresholds existing across the world and associated surveillance and implementation characteristics.

METHODOLOGY/PRINCIPAL FINDINGS

Searches for literature published from 2010 up to 2021 were performed using two search engines, Google Scholar and PubMed Central, according to PRISMA guidelines. A set of inclusion/exclusion criteria were identified and of the 1,485 initial selections, only 87 were included in the final review. Thirty inclusions reported originally generated thresholds. Thirteen inclusions were with statistical models that seemed intended to be continuously utilized to test the exceedance of thresholds in a specific region. There was another set of 44 inclusions that solely mentioned previously generated thresholds. The inclusions with "epidemiological thresholds" outnumbered those with "entomological thresholds". Most of the inclusions came from Asia and those thresholds were targeted toward Aedes and dengue control. Overall, mosquito counts (adult and larval) and climatic variables (temperature and rainfall) were the most used parameters in thresholds. The associated surveillance and implementation characteristics of the identified thresholds are discussed here.

CONCLUSIONS/SIGNIFICANCE

The review identified 87 publications with different mosquito control thresholds developed across the world and published during the last decade. Associated surveillance and implementation characteristics will help organize surveillance systems targeting the development and implementation of action thresholds, as well as direct awareness towards already existing thresholds for those with programs lacking available resources for comprehensive surveillance systems. The findings of the review highlight data gaps and areas of focus to fill in the action threshold compartment of the IVM toolbox.

Weather integrated multiple machine learning models for prediction of dengue prevalence in India

Dengue is a rapidly spreading viral disease transmitted to humans by Aedes mosquitoes. Due to global urbanization and climate change, the number of dengue cases are gradually increasing in recent decades. Hence, an early prediction of dengue continues to be a major concern for public health in countries with high prevalence of dengue. Creating a robust forecast model for the accurate prediction of dengue is a complex task and can be done through various data modelling approaches. In the present study, we have applied vector auto regression, generalized boosted models, support vector regression, and long short-term memory (LSTM) to predict the dengue prevalence in Kerala state of the Indian subcontinent. We consider the number of dengue cases as the target variable and weather variables viz., relative humidity, soil moisture, mean temperature, precipitation, and NINO3.4 as independent variables. Various analytical models have been applied on both datasets and predicted the dengue cases. Among all the models, the LSTM model was outperformed with superior prediction capability (RMSE: 0.345 and R²:0.86) than the other models. However, other models are able to capture the trend of dengue cases but failed in predicting the outbreak periods when compared to LSTM. The findings of this study will be helpful for public health agencies and policymakers to draw appropriate control measures before the onset of dengue. The proposed LSTM model for dengue prediction can be followed by other states of India as well.

A Review of the Surveillance Techniques for Aedes albopictus

Aedes (Stegomyia) albopictus (Skuse) (Diptera: Culicidae) transmits a variety of arboviruses (arthropod-borne viruses) and acts as one of the most dangerous mosquito species in the world. Mosquito surveillance is the main means of evaluating vector density, vector-borne disease risk, and the efficacy of vector-control operations. The larval density of Ae. albopictus can be reflected by means of Breteau index and Route index, and egg density can be monitored by ovitrap and mosq-ovitrap, whereas mosquito surveillance methods mainly include human landing catch, human-baited double net trap, BG-Sentinel trap, autocidal gravid ovitrap, gravid Aedes trap, and mosquito magnet. This article describes different methods of Ae. albopictus surveillance and offers suggestions to improve surveillance.

Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review

Dengue fever is a serious and growing public health problem in Latin America and elsewhere, intensified by climate change and human mobility. This paper reviews the approaches to the epidemiological prediction of dengue fever using the One Health perspective, including an analysis of how Machine Learning techniques have been applied to it and focuses on the risk factors for dengue in Latin America to put the broader environmental considerations into a detailed understanding of the small-scale processes as they affect disease incidence. Determining that many factors can act as predictors for dengue outbreaks, a large-scale comparison of different predictors over larger geographic areas than those currently studied is lacking to determine which predictors are the most effective. In addition, it provides insight into techniques of Machine Learning used for future predictive models, as well as general workflow for Machine Learning projects of dengue fever.

Seasonal variation and intra urban heterogeneity of the entomological risk of transmission of dengue and yellow fever in Abidjan, Côte d'Ivoire

Dengue and yellow fever are prevalent in Côte d'Ivoire and Aedes (Stegomyia) aegypti (Linnaeus), (Diptera: Culicidae), is known as the main vector. We aimed to assess seasonal variation and spatial heterogeneity in the transmission of both arbovirus diseases in Abidjan. Entomological surveys targeting larvae of A. aegypti, were carried out between November 2015 and August 2016 covering the four climatic seasons including a cohort of 100 houses randomly selected in three neighbourhoods. A. aegypti was the predominant species (96.6%) of mosquitoes resulting from the rearing of harvested larvae, and the only vector of dengue and yellow fever recorded during the study period. The highest proportion of water storage containers (45.5%) which represented the major breeding sites infested by the larvae of A. aegypti, was observed in Anoumabo. The house indices >5% and/or Breteau indices >20 recorded in each neighbourhood, during the different climatic seasons, indicated that there was, a high and permanent, heterogeneity in the transmission risk of dengue and yellow fever between the three neighbourhoods. In terms of transmission risk, Anoumabo was the neighbourhood with the highest risk compared to the two others, then, particular attention should be paid to this site in terms of surveillance by vector control programme in Abidjan.

Importance of Public Transport Networks for Reconciling the Spatial Distribution of Dengue and the Association of Socio-Economic Factors with Dengue Risk in Bangkok, Thailand

Dengue is the most widespread mosquito-borne viral disease of man and spreading at an alarming rate. Socio-economic inequality has long been thought to contribute to providing an environment for viral propagation. However, identifying socio-economic (SE) risk factors is confounded by intra-urban daily human mobility, with virus being ferried across cities. This study aimed to identify SE variables associated with dengue at a subdistrict level in Bangkok, analyse how they explain observed dengue hotspots and assess the impact of mobility networks on such associations. Using meteorological, dengue case, national statistics, and transport databases from the Bangkok authorities, we applied statistical association and spatial analyses to identify SE variables associated with dengue and spatial hotspots and the extent to which incorporating transport data impacts the observed associations. We identified three SE risk factors at the subdistrict level: lack of education, % of houses being cement/brick, and number of houses as being associated with increased risk of dengue. Spatial hotspots of dengue were found to occur consistently in the centre of the city, but which did not entirely have the socio-economic risk factor characteristics. Incorporation of the intra-urban transport network, however, much improved the overall statistical association of the socio-economic variables with dengue incidence and reconciled the incongruous difference between the spatial hotspots and the SE risk factors. Our study suggests that incorporating transport networks enables a more real-world analysis within urban areas and should enable improvements in the identification of risk factors.

Predicting dengue incidence leveraging internet-based data sources. A case study in 20 cities in Brazil

The dengue virus affects millions of people every year worldwide, causing large epidemic outbreaks that disrupt people’s lives and severely strain healthcare systems. In the absence of a reliable vaccine against dengue or an effective treatment to manage the illness in humans, most efforts to combat dengue infections have focused on preventing its vectors, mainly the Aedes aegypti mosquito, from flourishing across the world. These mosquito-control strategies need reliable disease activity surveillance systems to be deployed. Despite significant efforts to estimate dengue incidence using a variety of data sources and methods, little work has been done to understand the relative contribution of the different data sources to improved prediction. Additionally, scholarship on the topic had initially focused on prediction systems at the national- and state-levels, and much remains to be done at the finer spatial resolutions at which health policy interventions often occur. We develop a methodological framework to assess and compare dengue incidence estimates at the city level, and evaluate the performance of a collection of models on 20 different cities in Brazil. The data sources we use towards this end are weekly incidence counts from prior years (seasonal autoregressive terms), weekly-aggregated weather variables, and real-time internet search data. We find that both random forest-based models and LASSO regression-based models effectively leverage these multiple data sources to produce accurate predictions, and that while the performance between them is comparable on average, the former method produces fewer extreme outliers, and can thus be considered more robust. For real-time predictions that assume long delays (6–8 weeks) in the availability of epidemiological data, we find that real-time internet search data are the strongest predictors of dengue incidence, whereas for predictions that assume short delays (1–3 weeks), in which the error rate is halved (as measured by relative RMSE), short-term and seasonal autocorrelation are the dominant predictors. Despite the difficulties inherent to city-level prediction, our framework achieves meaningful and actionable estimates across cities with different demographic, geographic and epidemic characteristics.

As the incidence of infectious diseases like dengue continues to increase throughout the world, tracking their spread in real time poses a significant challenge to local and national health authorities. Accurate incidence data are often difficult to obtain as outbreaks emerge and unfold, both due the partial reach of serological surveillance (especially in rural areas), and due to delays in reporting, which result in post-hoc adjustments to what should have been real-time data. Thus, a range of ‘nowcasting’ tools have been developed to estimate disease trends, using different mathematical and statistical methodologies to fill the temporal data gap. Over the past several years, researchers have investigated how to best incorporate internet search data into predictive models, since these can be obtained in real-time. Still, most such models have been regression-based, and have tended to underperform in cases when epidemiological data are only available after long reporting delays. Moreover, in tropical countries, attention has increasingly turned from testing and applying models at the national level to models at higher spatial resolutions, such as states and cities. Here, we develop machine learning models based on both LASSO regression and on random forest ensembles, and proceed to apply and compare them across 20 cities in Brazil. We find that our methodology produces meaningful and actionable disease estimates at the city level with both underlying model classes, and that the two perform comparably across most metrics, although the ensemble method produces fewer outliers. We also compare model performance and the relative contribution of different data sources across diverse geographic, demographic and epidemic conditions.

Modeling spatial pattern of dengue in North Central Mexico using survey data and logistic regression

Dengue is a major public health concern mainly in tropical and subtropical environments worldwide. Despite several attempts to prevent this disease occurring in tropical regions of Mexico, it has not yet been controlled. This work focused on spatial modeling of confirmed dengue fever cases that occurred during the period 2010-2014 in the Huasteca Potosina region of Mexico. Multivariable Logistic Regression Modeling (MLRM) was used to determine the relationship between explanatory variables and the presence/absence of dengue. Model performance was evaluated using the area under curve (AUC) of the relative operating characteristic (ROC); AUC > 0.95. A high spatial resolution map was created to reveal the most probable patterns of dengue risk. Our results can be used for targeted control and prevention programs at local and regional levels. This methodology can be applied to other major diseases that are spatially distributed in accordance with environmental factors.

Dengue disease dynamics are modulated by the combined influences of precipitation and landscape: A machine learning approach

BACKGROUND

Dengue is an endemic vector-borne disease influenced by environmental factors such as landscape and climate. Previous studies separately assessed the effects of landscape and climate factors on mosquito occurrence and dengue incidence. However, both factors concurrently coexist in time and space and can interact, affecting mosquito development and dengue disease transmission. For example, eggs laid in a suitable environment can hatch after being submerged in rain water. It has been difficult for conventional statistical modeling approaches to demonstrate these combined influences due to mathematical constraints.

OBJECTIVES

To investigate the combined influences of landscape and climate factors on mosquito occurrence and dengue incidence.

METHODS

Entomological, epidemiological, and landscape data from the rainy season (July-December) were obtained from respective government agencies in Metropolitan Manila, Philippines, from 2012 to 2014. Temperature, precipitation and vegetation data were obtained through remote sensing. A random forest algorithm was used to select the landscape and climate variables. Afterward, using the identified key variables, a model-based (MOB) recursive partitioning was implemented to test the combined influences of landscape and climate factors on ovitrap index (vector mosquito occurrence) and dengue incidence.

RESULTS

The MOB recursive partitioning for ovitrap index indicated a high sensitivity of vector mosquito occurrence on environmental conditions generated by a combination of high residential density areas with low precipitation. Moreover, the MOB recursive partitioning indicated high sensitivity of dengue incidence to the effects of precipitation in areas with high proportions of residential density and commercial areas.

CONCLUSIONS

Dengue dynamics are not solely influenced by individual effects of either climate or landscape, but rather by their synergistic or combined effects. The presented findings have the potential to target vector surveillance in areas identified as suitable for mosquito occurrence under specific climatic conditions and may be relevant as part of urban planning strategies to control dengue.

A review exploring the overarching burden of Zika virus with emphasis on epidemiological case studies from Brazil

This paper explores the main factors for mosquito-borne transmission of the Zika virus by focusing on environmental, anthropogenic, and social risks. A literature review was conducted bringing together related information from this genre of research from peer-reviewed publications. It was observed that environmental conditions, especially precipitation, humidity, and temperature, played a role in the transmission. Furthermore, anthropogenic factors including sanitation, urbanization, and environmental pollution promote the transmission by affecting the mosquito density. In addition, socioeconomic factors such as poverty as well as social inequality and low-quality housing have also an impact since these are social factors that limit access to certain facilities or infrastructure which, in turn, promote transmission when absent (e.g., piped water and screened windows). Finally, the paper presents short-, mid-, and long-term preventative solutions together with future perspectives. This is the first review exploring the effects of anthropogenic aspects on Zika transmission with a special emphasis in Brazil.

Dengue Transmission Mapping with Weather-Based Predictive Model in Three Southernmost Provinces of Thailand

This study aimed to show maps and analyses that display dengue cases and weather-related factors on dengue transmission in the three southernmost provinces of Thailand, namely Pattani, Yala, and Narathiwat provinces. Data on the number of dengue cases and weather variables including rainfall, rainy day, mean temperature, min temperature, max temperature, relative humidity, and air pressure for the period from January 2015 to December 2019 were obtained from the Bureau of Epidemiology, Ministry of Public Health and the Meteorological Department of Southern Thailand, respectively. Spearman rank correlation test was performed at lags from zero to two months and the predictive modeling used time series Poisson regression analysis. The distribution of dengue cases showed that in Pattani and Yala provinces the most dengue cases occurred in June. Narathiwat province had the most dengue cases occurring in August. The air pressure, relative humidity, rainfall, rainy day, and min temperature are the main predictors in Pattani province, while air pressure, rainy day, and max/mean temperature seem to play important roles in the number of dengue cases in Yala and Narathiwat provinces. The goodness-of-fit analyses reveal that the model fits the data reasonably well. The results provide scientific information for creating effective dengue control programs in the community, and the predictive model can support decision making in public health organizations and for management of the environmental risk area.

Forest Coverage and Socioeconomic Factors Associated with Dengue in El Salvador, 2011-2013

Dengue virus serotypes 1, 2, 3, and 4 are transmitted by Aedes aegypti and Aedes albopictus mosquitoes, which cause illness in an estimated 100 million annually. Although dengue viruses are endemic throughout El Salvador, very little is known about their ecology and epidemiology. The principal methods to prevent and reduce dengue cases are through vector control and by adoption of a vaccine. In addition, understanding the environmental and socioeconomic factors associated with dengue could contribute to case reduction by targeting prevention efforts in dengue hotspots. This study investigated environmental and socioeconomic factors associated with dengue cases in El Salvador. Dengue cases were obtained from 2011 to 2013 for 262 municipalities. The mean incidence was determined for each municipality for the 3 year period. Negative binomial regression models evaluated the relationship between dengue cases and the environmental factors elevation, forest coverage, mean annual temperature, and cumulative precipitation. Twelve socioeconomic and infrastructure variables and their relationship with dengue were also investigated by using negative binomial regression. A total of 29,764 confirmed dengue cases were reported. The mean dengue incidence for 2011-2013 was 135/100,000. The highest number of dengue cases occurred in San Salvador and surrounding municipalities, as well as in two additional cities, Santa Ana and San Miguel; the highest incidence of dengue cases (per 100,000) occurred in cities in the west and at the center of the country. Significant environmental variables associated with dengue included temperature, precipitation, and non-forested area. The socioeconomic variables poverty rate, illiteracy rate, and school attendance, and the infrastructure variables percent of homes with sanitary service, municipal trash service, electricity, and cement brick flooring, as well as population density, were also significant predictors of dengue. Understanding these environmental and socioeconomic factors and their relationship with dengue will help design and implement timely prevention strategies and vector control to reduce dengue in El Salvador.

A spatio-temporal analysis of dengue spread in a Brazilian dry climate region

We investigated the relation between the spread, time scale, and spatial arrangement of dengue in Bahia, a Brazilian dry climate region, for the period 2000 to 2009. The degree of cross-correlation is calculated for 15 economic regions. We propose a multiscale statistical analysis to datasets of dengue cases in order to verify the effect of infection dispersal on the economic regions from the metropolitan region of Salvador. Our empirical results support a significant and persistent cross-correlation between most regions, reinforcing the idea that economic regions or climatic conditions are non-statistically significant in the spread of dengue in the State of Bahia. Our main contribution lies in the cross-correlation results revealing multiple aspects related to the propagation of dengue in dry climate regions.

Added-value of mosquito vector breeding sites from street view images in the risk mapping of dengue incidence in Thailand

Dengue is an emerging vector-borne viral disease across the world. The primary dengue mosquito vectors breed in containers with sufficient water and nutrition. Outdoor containers can be detected from geotagged images using state-of-the-art deep learning methods. In this study, we utilize such container information from street view images in developing a risk mapping model and determine the added value of including container information in predicting dengue risk. We developed seasonal-spatial models in which the target variable dengue incidence was explained using weather and container variable predictors. Linear mixed models with fixed and random effects are employed in our models to account for different characteristics of containers and weather variables. Using data from three provinces of Thailand between 2015 and 2018, the models are developed at the sub-district level resolution to facilitate the development of effective targeted intervention strategies. The performance of the models is evaluated with two baseline models: a classic linear model and a linear mixed model without container information. The performance evaluated with the correlation coefficients, R-squared, and AIC shows the proposed model with the container information outperforms both baseline models in all three provinces. Through sensitivity analysis, we investigate the containers that have a high impact on dengue risk. Our findings indicate that outdoor containers identified from street view images can be a useful data source in building effective dengue risk models and that the resulting models have potential in helping to target container elimination interventions.

Larval Indices of Vector Mosquitoes as Predictors of Dengue Epidemics: An Approach to Manage Dengue Outbreaks Based on Entomological Parameters in the Districts of Colombo and Kandy, Sri Lanka

Background

Early detection of dengue epidemics is a vital aspect in control programmes. Predictions based on larval indices of disease vectors are widely used in dengue control, with defined threshold values. However, there is no set threshold in Sri Lanka at the national or regional levels for Aedes larval indices. Therefore, the current study aimed at developing threshold values for vector indices in two dengue high-risk districts in Sri Lanka.

Methods

Monthly vector indices (House Index [HI], Container Index [CI], Breteau Index for Aedes aegypti [BI_agp], and Ae. albopictus [BI_alb]), of ten selected dengue high-risk Medical Officer of Health (MOH) areas located in Colombo and Kandy districts, were collected from January 2010 to June 2019, along with monthly reported dengue cases. Receiver Operating Characteristic (ROC) curve analysis in SPSS (version 23) was used to assess the discriminative power of the larval indices in identifying dengue epidemics and to develop thresholds for the dengue epidemic management.

Results

Only HI and BI_agp denoted significant associations with dengue epidemics at lag periods of one and two months. Based on Ae. aegypti, average threshold values were defined for Colombo as Low Risk (2.4 ≤ BI_agp < 3.8), Moderate Risk (3.8 ≤ BI_agp < 5), High Risk (BI_agp ≥ 5), along with BI_agp 2.9 ≤ BI_agp < 4.2 (Low Risk), 4.2 ≤ BI_agp < 5.3 (Moderate Risk), and BI_agp ≥ 5.3 (High Risk) for Kandy. Further, 5.5 ≤ HI < 8.9, 8.9 ≤ HI < 11.9, and HI ≥ 11.9 were defined as Low Risk, Moderate Risk, and High Risk average thresholds for HI in Colombo, while 6.9 ≤ HI < 9.1 (Low Risk), 8.9 ≥ HI < 11.8 (Moderate Risk), and HI ≥ 11.8 (High Risk) were defined for Kandy.

Conclusions

The defined threshold values for Ae. aegypti and HI could be recommended as indicators for early detection of dengue epidemics and to drive vector management activities, with the objective of managing dengue epidemics with optimal usage of financial, technical, and human resources in Sri Lanka.

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

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

Bionomics and insecticide resistance of Aedes albopictus in Shandong, a high latitude and high-risk dengue transmission area in China

BACKGROUND

Dengue fever outbreaks tend to spread northward in China, and Jining is the northernmost region where local dengue fever cases have been detected. Therefore, it is important to investigate the density of Aedes albopictus and its resistance to deltamethrin.

METHODS

The Breteau index (BI) and container index (CI) were calculated to assess the larval density of Ae. albopictus and human-baited double net trap (HDN) surveillance was performed in six subordinate counties (Rencheng, Yanzhou, Sishui, Liangshan, Zoucheng and Jiaxiang) of Jining City in 2017 and 2018. The resistance of Ae. albopictus adults to deltamethrin was evaluated using the World Health Organization (WHO) standard resistance bioassay. The mutations at Vgsc codons 1532 and 1534 were also analysed to determine the association between kdr mutations and phenotypic resistance in adult mosquitoes.

RESULTS

The average BI, CI and biting rate at Jining were 45.30, 16.02 and 1.97 (female /man/hour) in 2017 and 15.95, 7.86 and 0.59 f/m/h in 2018, respectively. In August 26, 2017, when the first dengue fever case was diagnosed, the BI at Qianli village in Jiaxiang County was 107.27. The application of prevention and control measures by the government sharply decreased the BI to a value of 4.95 in September 3, 2017. The mortality of field-collected Ae. albopictus females from Jiaxiang was 41.98%. I1532T, F1534L and F1534S mutations were found in domain III of the Vgsc gene. This study provides the first demonstration that both I1532T and F1534S mutations are positively correlated with the deltamethrin-resistant phenotype.

CONCLUSIONS

Mosquito density surveillance, resistance monitoring and risk assessment should be strengthened in areas at risk for dengue to ensure the sustainable control of Ae. albopictus and thus the prevention and control of dengue transmission.

Neglected Urban Villages in Current Vector Surveillance System: Evidences in Guangzhou, China

Numerous urban villages (UVs) with substandard living conditions that cause people to live there with vulnerability to health impacts, including vector-borne diseases such as dengue fever (DF), are major environmental and public health concerns in highly urbanized regions, especially in developing countries. It is necessary to explore the relationship between UVs and vector for effectively dealing with these problems. In this study, land-use types, including UVs, normal construction land (NCL), unused land (UL), vegetation, and water, were retrieved from the high-resolution remotely sensed imagery in the central area of Guangzhou in 2017. The vector density from May to October in 2017, including Aedes. albopictus (Ae. albopictus)’s Breteau index (BI), standard space index (SSI), and adult density index (ADI) were obtained from the vector surveillance system implemented by the Guangzhou Center for Disease Control and Prevention (CDC). Furthermore, the spatial and temporal patterns of vector monitoring sites and vector density were analyzed on a fine scale, and then the Geodetector tool was further employed to explore the relationships between vector density and land-use types. The monitoring sites were mainly located in NCL (55.70%–56.44%) and UV (13.14%–13.92%). Among the total monitoring sites of BI (79), SSI (312), and ADI (326), the random sites accounted for about 88.61%, 97.12%, and 98.47%, respectively. The density of Ae. albopictus was temporally related to rainfall and temperature and was obviously differentiated among different land-use types. Meanwhile, the grids with higher density, which were mostly concentrated in the Pearl River fork zone that collects a large number of UVs, showed that the density of Ae. albopictus was spatially associated with the UVs. Next, the results of the Geodetector illustrated that UVs posed great impact on the density of Ae. albopictus across the central region of Guangzhou. We suggest that the number of monitoring sites in the UVs should be appropriately increased to strengthen the current vector surveillance system in Guangzhou. This study will provide targeted guidance for local authorities, making more effective control and prevention measures on the DF epidemics.

Disease surveillance using online news: an extended study of dengue fever in India

BACKGROUND

The study demonstrates the potential in using newspaper information as a proxy for monitoring dengue fever outbreaks in India. Online newspapers are being considered as sources of information on disease surveillance, early outbreak detection, and epidemiology research. Our objective is to understand the complex dengue epidemiology and discover inter-relationships between dengue fever and local social-environmental factors by mining information from local Indian news articles.

RESULTS

We search and extract articles from the newspaper database, LexisNexis. News articles related to dengue fever in India are analyzed together with local environmental, climate, and population data in both temporally and spatially to study disease epidemiology. We also examine the influence of newsworthiness for constructing a disease surveillance system. In terms of temporal aspects, dengue outbreaks follow consistent patterns every year. However, for many areas, this application is frustrated by the relatively small numbers of news articles.

CONCLUSIONS

The study has advanced capabilities in producing approaches that provide for richer interpretations of textual information provided in newspaper articles. Such approaches appear particularly well suited for developing countries with relatively poor medical infrastructures and records.

Breteau index as a promising early warning signal for dengue fever outbreaks in the Colombo District, Sri Lanka

Despite the efforts in reducing vector densities, outbreaks of dengue fever have become a frequent event in Sri Lanka. As explained by dengue transmission dynamics, vector control activities intensified at peak or near peak outbreak situations would not be successful in controlling the outbreaks. A reliable method of outbreak prediction is always warranted for early preparedness. Relationships between the monthly Breteau indices of the two vector species (Aedes aegypti L. and Ae. albopictus Skuse) and the monthly dengue incidence in a selected high-risk health division (Kaduwela) in the Colombo District, Sri Lanka were determined for three consecutive years, 2009 to 2011. The same procedure was extended for the whole Colombo District from 2013 to 2016. Cross correlation functions were used to determine the relationships with the corresponding lag-periods. Receiver Operating Characteristic Curves (ROC) were constructed to determine the performance of the Breteau indices as predictors of impending dengue outbreaks and to establish the threshold values. The pronounced performance with >80% areas under ROC curves and >75% sensitivity and >70% specificity of threshold values with defined lag-periods in correlations emphasize the importance of the Breteau index as a promising early warning signal for dengue outbreaks. The results indicate the importance of the carefully planned routine vector larval surveillance in dengue control programs which would make reliable outbreak predictions with a sufficient window period for early preparedness.

[Cases of dengue and urban waste collection: a study in the City of Recife]

The scope of this study was to identify which categories of urban waste are associated with cases of dengue and to evaluate the impact of garbage collection on dengue infection in the City of Recife (Brazil). Data from categorized waste weighing and the confirmed cases of dengue in the city were used. The data were analyzed using Pearson's correlation coefficient for the 13 categories of urban garbage, followed by Multivariate Linear Regression, selecting the variables by the stepwise method. A negative correlation between dengue infections in seven categories was identified: household garbage (r = -0.835), differentiated residues (r = -0.835), special operations residues (r = -0.711), building rubble (r = -0.687), selective waste collection (r = -0.425) and tires (r = -0.423). The regression model was able to explain 75% of the variation, indicating that an increase of 1,000 tons in household garbage collection provides a decrease of 0.032 in cases of dengue, while the same increase in tire collection esults in a decrease of 0.465. The results show that garbage collection has a strong negative impact on dengue cases and can be adopted as a prevention strategy by municipal governments.

Dengue seroprevalence: data from the clinical development of a tetravalent dengue vaccine in 14 countries (2005-2014)

Dengue seroprevalence data in the literature is limited and the available information is difficult to compare between studies because of the varying survey designs and methods used. We assessed dengue seropositivity across 14 countries using data from 15 trials conducted during the development of a tetravalent dengue vaccine between October 2005 and February 2014. Participants’ dengue seropositivity (n=8592) was determined from baseline (before vaccination) serum samples at two centralized laboratories with the plaque reduction neutralization test (PRNT₅₀). Seropositivity rates generally increased with age in endemic settings. Although seropositivity rates varied across geographical areas, between countries, and within countries by region, no major differences were observed for given age groups between the two endemic regions, Latin America and Asia-Pacific. Seropositivity rates were generally stable over time. The proportion of participants who had only experienced primary infection tended to be higher in younger children than adolescents/adults. These results will help inform and guide dengue control strategies in the participating countries.

Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data

Background

The goal of this research is to create a system that can use the available relevant information about the factors responsible for the spread of dengue and; use it to predict the occurrence of dengue within a geographical region, so that public health experts can prepare for, manage and control the epidemic. Our study presents new geospatial insights into our understanding and management of health, disease and health-care systems.

Methods

We present a machine learning-based methodology capable of providing forecast estimates of dengue prediction in each of the fifty districts of Thailand by leveraging data from multiple data sources. Using a set of prediction variables, we show an increase in prediction accuracy of the model with an optimal combination of predictors which include: meteorological data, clinical data, lag variables of disease surveillance, socioeconomic data and the data encoding spatial dependence on dengue transmission. We use Generalized Additive Models (GAMs) to fit the relationships between the predictors (with a lag of one month) and the clinical data of Dengue hemorrhagic fever (DHF) using the data from 2008 to 2012. Using the data from 2013 to 2015 and a comparative set of prediction models, we evaluate the predictive ability of the fitted models according to RMSE and SRMSE as well as using adjusted R-squared value, deviance explained and change in AIC.

Results

The model allows for combining different predictors to make forecasts with a lead time of one month and also describe the statistical significance of the variables used to characterize the forecast. The discriminating ability of the final model was evaluated against Bangkok specific constant threshold and WHO moving threshold of the epidemic in terms of specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).

Conclusions

The out-of-sample validation showed poorer results than the in-sample validation, however it demonstrated ability in detecting outbreaks up-to one month ahead. We also determine that for the predicting dengue outbreaks within a district, the influence of dengue incidences and socioeconomic data from the surrounding districts is statistically significant. This validates the influence of movement patterns of people and spatial heterogeneity of human activities on the spread of the epidemic.

Empirical optimization of risk thresholds for dengue: an approach towards entomological management of Aedes mosquitoes based on larval indices in the Kandy District of Sri Lanka

Background

Larval indices such as Premise Index (PI), Breteau Index (BI) and Container Index (CI) are widely used to interpret the density of dengue vectors in surveillance programmes. These indices may be useful for forecasting disease outbreaks in an area. However, use of the values of these indices as alarm signals is rarely considered in control programmes. Therefore, the current study aims to propose threshold values for vector indices based on an empirical modeling approach for the Kandy District of Sri Lanka.

Methods

Monthly vector indices, viz PI, BI and CI, for Aedes aegypti and Aedes albopictus, of four selected dengue high risk Medical Officer of Health (MOH) areas in the Kandy District from January 2010 to August 2017, were used in the study. Gumbel frequency analysis was used to calculate the exceedance probability of quantitative values for each individual larval index within the relevant MOH area, individually and to set up the threshold values for the entomological management of dengue vectors.

Results

Among the study MOH areas, Akurana indicated a relatively high density of both Ae. aegypti and Ae. albopictus, while Gangawata Korale MOH area had the lowest. Based on Ae. aegypti, threshold values were defined for Kandy as low risk (BI_agp > 1.77), risk (BI_agp > 3.23), moderate risk (BI_agp > 4.47) and high risk (BI_agp > 6.23). In addition, PI > 6.75 was defined as low risk, while PI > 9.43 and PI>12.82 were defined as moderate and high risk, respectively as an average.

Conclusions

Threshold values recommended for Ae. aegypti (primary vector for dengue) along with cut-off values for PI (for Ae. aegypti and Ae. albopictus), could be suggested as indicators for decision making in vector control efforts. This may also facilitate the rational use of financial allocations, technical and human resources for vector control approaches in Sri Lanka in a fruitful manner.

Electronic supplementary material

The online version of this article (10.1186/s13071-018-2961-y) contains supplementary material, which is available to authorized users.

Low socioeconomic condition and the risk of dengue fever: A direct relationship

This study aimed to characterize the first dengue fever epidemic in Várzea Paulista, São Paulo, Brazil, and its spatial and spatio-temporal distribution in order to assess the association of socioeconomic factors with dengue occurrence. We used autochthonous dengue cases confirmed in a 2007 epidemic, the first reported in the city, available in the Information System on Diseases of Compulsory Declaration database. These cases where geocoded by address. We identified spatial and spatio-temporal clusters of high- and low-risk dengue areas using scan statistics. To access the risk of dengue occurrence and to evaluate its relationship with socioeconomic level we used a population-based case-control design. Firstly, we fitted a generalized additive model (GAM) to dengue cases and controls without considering the non-spatial covariates to estimate the odds ratios of the occurrence of the disease. The controls were drawn considering the spatial distribution of the household of the study area and represented the source population of the dengue cases. After that, we assessed the relationship between socioeconomic variables and dengue using the GAM and obtained the effect of these covariates in the occurrence of dengue adjusted by the spatial localization of the cases and controls. Cluster analysis and GAM indicated that northeastern area of Várzea Paulista was the most affected area during the epidemic. The study showed a positive relationship between low socioeconomic condition and increased risk of dengue. We studied the first dengue epidemic in a highly susceptible population at the beginning of the outbreak and therefore it may have allowed to identify an association between low socioeconomic conditions and increased risk of dengue. These results may be useful to predict the occurrence and to identify priority areas to develop control measures for dengue, and also for Zika and Chikungunya; diseases that recently reached Latin America, especially Brazil.

Dengue burden in India: recent trends and importance of climatic parameters

For the past ten years, the number of dengue cases has gradually increased in India. Dengue is driven by complex interactions among host, vector and virus that are influenced by climatic factors. In the present study, we focused on the extrinsic incubation period (EIP) and its variability in different climatic zones of India. The EIP was calculated by using daily and monthly mean temperatures for the states of Punjab, Haryana, Gujarat, Rajasthan and Kerala. Among the studied states, a faster/low EIP in Kerala (8–15 days at 30.8 and 23.4 °C) and a generally slower/high EIP in Punjab (5.6–96.5 days at 35 and 0 °C) were simulated with daily temperatures. EIPs were calculated for different seasons, and Kerala showed the lowest EIP during the monsoon period. In addition, a significant association between dengue cases and precipitation was also observed. The results suggest that temperature is important in virus development in different climatic regions and may be useful in understanding spatio-temporal variations in dengue risk. Climate-based disease forecasting models in India should be refined and tailored for different climatic zones, instead of use of a standard model.

Advances in using Internet searches to track dengue

Dengue is a mosquito-borne disease that threatens over half of the world’s population. Despite being endemic to more than 100 countries, government-led efforts and tools for timely identification and tracking of new infections are still lacking in many affected areas. Multiple methodologies that leverage the use of Internet-based data sources have been proposed as a way to complement dengue surveillance efforts. Among these, dengue-related Google search trends have been shown to correlate with dengue activity. We extend a methodological framework, initially proposed and validated for flu surveillance, to produce near real-time estimates of dengue cases in five countries/states: Mexico, Brazil, Thailand, Singapore and Taiwan. Our result shows that our modeling framework can be used to improve the tracking of dengue activity in multiple locations around the world.

As communicable diseases spread in our societies, people frequently turn to the Internet to search for medical information. In recent years, multiple research teams have investigated how to utilize Internet users’ search activity to track infectious diseases around our planet. In this article, we show that a methodology, originally developed to track flu in the US, can be extended to improve dengue surveillance in multiple countries/states where dengue has been observed in the last several years. Our result suggests that our methodology performs best in dengue-endemic areas with high number of yearly cases and with sustained seasonal incidence.

Individual and Interactive Effects of Socio-Ecological Factors on Dengue Fever at Fine Spatial Scale: A Geographical Detector-Based Analysis

Background: Large spatial heterogeneity was observed in the dengue fever outbreak in Guangzhou in 2014, however, the underlying reasons remain unknown. We examined whether socio-ecological factors affected the spatial distribution and their interactive effects. Methods: Moran’s I was applied to first examine the spatial cluster of dengue fever in Guangzhou. Nine socio-ecological factors were chosen to represent the urbanization level, economy, accessibility, environment, and the weather of the 167 townships/streets in Guangzhou, and then the geographical detector was applied to analyze the individual and interactive effects of these factors on the dengue outbreak. Results: Four clusters of dengue fever were identified in Guangzhou in 2014, including one hot spot in the central area of Guangzhou and three cold spots in the suburban districts. For individual effects, the temperature (q = 0.33) was the dominant factor of dengue fever, followed by precipitation (q = 0.24), road density (q = 0.24), and water body area (q = 0.23). For the interactive effects, the combination of high precipitation, high temperature, and high road density might result in increased dengue fever incidence. Moreover, urban villages might be the dengue fever hot spots. Conclusions: Our study suggests that some socio-ecological factors might either separately or jointly influence the spatial distribution of dengue fever in Guangzhou.

Using Spatial Video to Analyze and Map the Water-Fetching Path in Challenging Environments: A Case Study of Dar es Salaam, Tanzania

Access to clean drinking water remains a significant health problem in the developing world. Traditional definitions of water access oversimplify the geographic context of water availability, the burden of water collection, and challenges faced along the path, mainly due to a lack of fine scale spatial data. This paper demonstrates how spatial video collected in three informal areas of Dar es Salaam, Tanzania, can be used to quantify aspects of the walk to water. These include impediments encountered along the path such as changes in elevation and proximity to traffic. All are mapped along with classic health-related environmental and social information, such as standing water, drains, and trash. The issue of GPS error was encountered due to the built environment that is typical of informal settlements. The spatial video allowed for the correction of the path to gain a more accurate estimate of time and distance for each walk. The resulting mapped health risks at this fine scale of detail reveal micro-geographies of concern. Spatial video is a useful tool for visualizing and analyzing the challenges of water collection. It also allows for data generated along the walk to become part of both a household and local area risk assessment.

[Monitoring the Paraguayan epidemiological dengue surveillance system (2009-2011) using Benford's law]

INTRODUCTION

Dengue is the most widespread arbovirus worldwide. In Paraguay, it reappeared in 1988-1989, with one of the largest epidemic outbreaks occurring in 2011.

OBJECTIVE

To evaluate the performance of the dengue epidemiological surveillance system in Paraguay between 2009 and 2011.

MATERIALS AND METHODS

We conducted an ecological study with secondary epidemiological surveillance data. We analyzed notified cases of the disease based on the distribution expected by Benford's law. To this end, we used the first and second digits from the global records stratified by region, season, population density, indicators of housing conditions and heads of cattle.

RESULTS

The epidemiological surveillance system performed better during non-epidemic periods and in the states with better housing conditions and fewer heads of cattle.

CONCLUSION

Given that a difference in the performance existed, we recommended that the system remains operating at the same high alert level even during periods when fewer cases are expected. The technology used by the method proposed to monitor the notification of cases is easy to transfer to operational staff.

Seasonal and Geographical Variation of Dengue Vectors in Narathiwat, South Thailand

Using GIS-based land use map for the urban-rural division (the relative ratio of population density adjusted to relatively Aedes-infested land area), we demonstrated significant independent observations of seasonal and geographical variation of Aedes aegypti and Aedes albopictus vectors between Muang Narathiwat district (urban setting) and neighbor districts (rural setting) of Narathiwat, Southern Thailand, based on binomial distribution of Aedes vectors in water-holding containers (water storage containers, discarded receptacles, miscellaneous containers, and natural containers). The distribution of Aedes vectors was influenced seasonally by breeding outdoors rather than indoors in all 4 containers. Accordingly, both urban and rural settings elicited significantly seasonal (wet versus dry) distributions of Ae. aegypti larvae observed in water storage containers (P = 0.001 and P = 0.002) and natural containers (P = 0.016 and P = 0.015), whereas, in rural setting, the significant difference was observed in discarded receptacles (P = 0.028) and miscellaneous containers (P < 0.001). Seasonal distribution of Ae. albopictus larvae in any containers in urban setting was not remarkably noticed, whereas, in rural setting, the significant difference was observed in water storage containers (P = 0.007) and discarded receptacles (P < 0.001). Moreover, the distributions of percentages of container index for Aedes-infested households in dry season were significantly lower than that in other wet seasons, P = 0.034 for urban setting and P = 0.001 for rural setting. Findings suggest that seasonal and geographical variation of Aedes vectors affect the infestation in those containers in human inhabitations and surroundings.

Peridomestic Infection as a Determining Factor of Dengue Transmission

Background

The study of endemic dengue transmission is essential for proposing alternatives to impact its burden. The traditional paradigm establishes that transmission starts around cases, but there are few studies that determine the risk.

Methods

To assess the association between the peridomestic dengue infection and the exposure to a dengue index case (IC), a cohort was carried out in two Mexican endemic communities. People cohabitating with IC or living within a 50-meter radius (exposed cohort) and subjects of areas with no ICs in a 200-meter radius (unexposed cohort) were included.

Results

Exposure was associated with DENV infection in cohabitants (PRa 3.55; 95%CI 2.37–5.31) or neighbors (PRa 1.82; 95%CI 1.29–2.58). Age, location, toilets with no direct water discharge, families with children younger than 5 and the House Index, were associated with infection. Families with older than 13 were associated with a decreased frequency. After a month since the IC fever onset, the infection incidence was not influenced by exposure to an IC or vector density; it was influenced by the local seasonal behavior of dengue and the age. Additionally, we found asymptomatic infections accounted for 60% and a greater age was a protective factor for the presence of symptoms (RR 0.98; 95%CI 0.97–0.99).

Conclusion

The evidence suggests that dengue endemic transmission in these locations is initially peridomestic, around an infected subject who may be asymptomatic due to demographic structure and endemicity, and it is influenced by other characteristics of the individual, the neighborhood and the location. Once the transmission chain has been established, dengue spreads in the community probably by the adults who, despite being the group with lower infection frequency, mostly suffer asymptomatic infections and have higher mobility. This scenario complicates the opportunity and the effectiveness of control programs and highlights the need to apply multiple measures for dengue control.

The study of dengue transmission is essential for proposing alternatives to diminish the cases and the cost of dengue treatment and control. The traditional paradigm establishes that transmission chain starts around a case, but there are few studies that determine the risk, therefore, we studied if to live around a dengue case increases the risk to get infected by Dengue virus. We interviewed and took blood samples from people cohabitating with dengue cases and neighbors in two Mexican communities, to compare we interviewed and took blood samples from subjects of areas without dengue cases in these communities. We found that people cohabitating and neighbors had more risk to get infected. Younger and older person, the workers, families with children younger than 5, houses with toilets with no direct water discharge, and areas with more mosquitoes, also had increased infection risk until one month after the fever onset of dengue case. After this month the frequency of dengue infections was only influenced by the seasonal behavior of dengue and the age of the subjects. Also, we found that 60% of infections are asymptomatic and older people have less risk to develop symptoms. This study suggests that dengue transmission in these locations is initially peridomestic, around the houses of infected subject who may be asymptomatic (without symptoms), and it is influenced by other characteristics of the individual, the neighborhood and the community. After this peridomestic transmission, dengue spreads in the community probably by adults who mostly suffer asymptomatic infections and have higher mobility, which complicates the application and affects the results of vector control programs.

Geospatial analysis applied to epidemiological studies of dengue: a systematic review

A systematic review of the geospatial analysis methods used in the dengue fever studies published between January 2001 and March 2011 was undertaken. In accordance with specific selection criteria thirty-five studies were selected for inclusion in the review. The aim was to assess the types of spatial methods that have been used to analyze dengue transmission. We found twenty-one different methods that had been used in dengue fever epidemiological studies in that period, three of which were most frequently used. The results show that few articles had applied spatial analysis methods in dengue fever studies; however, whenever they were applied they contributed to a better understanding of dengue fever geospatial diffusion.

Spatial Distribution of Dengue in a Brazilian Urban Slum Setting: Role of Socioeconomic Gradient in Disease Risk

Background

Few studies of dengue have shown group-level associations between demographic, socioeconomic, or geographic characteristics and the spatial distribution of dengue within small urban areas. This study aimed to examine whether specific characteristics of an urban slum community were associated with the risk of dengue disease.

Methodology/Principal Findings

From 01/2009 to 12/2010, we conducted enhanced, community-based surveillance in the only public emergency unit in a slum in Salvador, Brazil to identify acute febrile illness (AFI) patients with laboratory evidence of dengue infection. Patient households were geocoded within census tracts (CTs). Demographic, socioeconomic, and geographical data were obtained from the 2010 national census. Associations between CTs characteristics and the spatial risk of both dengue and non-dengue AFI were assessed by Poisson log-normal and conditional auto-regressive models (CAR). We identified 651 (22.0%) dengue cases among 2,962 AFI patients. Estimated risk of symptomatic dengue was 21.3 and 70.2 cases per 10,000 inhabitants in 2009 and 2010, respectively. All the four dengue serotypes were identified, but DENV2 predominated (DENV1: 8.1%; DENV2: 90.7%; DENV3: 0.4%; DENV4: 0.8%). Multivariable CAR regression analysis showed increased dengue risk in CTs with poorer inhabitants (RR: 1.02 for each percent increase in the frequency of families earning ≤1 times the minimum wage; 95% CI: 1.01-1.04), and decreased risk in CTs located farther from the health unit (RR: 0.87 for each 100 meter increase; 95% CI: 0.80-0.94). The same CTs characteristics were also associated with non-dengue AFI risk.

Conclusions/Significance

This study highlights the large burden of symptomatic dengue on individuals living in urban slums in Brazil. Lower neighborhood socioeconomic status was independently associated with increased risk of dengue, indicating that within slum communities with high levels of absolute poverty, factors associated with the social gradient influence dengue transmission. In addition, poor geographic access to health services may be a barrier to identifying both dengue and non-dengue AFI cases. Therefore, further spatial studies should account for this potential source of bias.

Dengue is influenced by the environment; however, few studies have investigated the relationship between neighborhood characteristics and the spatial distribution of dengue within small urban areas. We examined whether specific characteristics of an urban slum community were associated with dengue risk. From January 2009 to December 2010, we conducted community-based surveillance in a slum in Salvador, Brazil to identify patients with acute febrile illness (AFI) and to test them for dengue. We identified 651 (22.0%) patients with laboratory evidence of dengue infection among 2,962 AFI patients. All the four dengue serotypes were detected, but DENV2 predominated (DENV1 8.1%; DENV2 90.7%; DENV3 0.4%; DENV4 0.8%). Estimated risk of symptomatic dengue was 21.3 and 70.2 cases per 10,000 inhabitants in 2009 and 2010, respectively. We found that neighborhood poverty level and proximity to the health center were associated with higher risk of detection of dengue and other AFI. This study highlights the large burden of dengue in poor urban slums of Brazil and indicates that socioeconomic development could potentially mitigate risk factors for both dengue and non-dengue AFI cases. In addition, we found that residential proximity to a health care facility was associated with improved case detection. Therefore, further studies on disease distribution should consider household proximity to health care facilities when assessing risk.

Dengue on islands: a Bayesian approach to understanding the global ecology of dengue viruses

Background

Transmission of dengue viruses (DENV), the most common arboviral pathogens globally, is influenced by many climatic and socioeconomic factors. However, the relative contributions of these factors on a global scale are unclear.

Methods

We randomly selected 94 islands stratified by socioeconomic and geographic characteristics. With a Bayesian model, we assessed factors contributing to the probability of islands having a history of any dengue outbreaks and of having frequent outbreaks.

Results

Minimum temperature was strongly associated with suitability for DENV transmission. Islands with a minimum monthly temperature of greater than 14.8°C (95% CI: 12.4–16.6°C) were predicted to be suitable for DENV transmission. Increased population size and precipitation were associated with increased outbreak frequency, but did not capture all of the variability. Predictions for 48 testing islands verified these findings.

Conclusions

This analysis clarified two key components of DENV ecology: minimum temperature was the most important determinant of suitability; and endemicity was more likely in areas with high precipitation and large, but not necessarily dense, populations. Wealth and connectivity, in contrast, had no discernable effects. This model adds to our knowledge of global determinants of dengue risk and provides a basis for understanding the ecology of dengue endemicity.

Using Geographic Information for Disease Surveillance at Mass Gatherings

Mass gatherings present the medical community with an excellent window of opportunity to study infectious diseases that can be transmitted over long distances. This is because the venue of a mass gathering usually does not change year-to-year. As a result, special attention can be given to the public health risks that are introduced by travelers from around the world into these mass gatherings. Travelers can also be infected with diseases that are endemic in the host country and transport the locally acquired infectious diseases to their home environments. Therefore, mass gatherings can be thought of as global-to-local-to-global events because of the initial convergence of global populations and the subsequent divergence of populations throughout the world. This chapter discusses three active areas of geographic research that have emerged from our understanding of disease surveillance at mass gatherings: the role of transportation and population geographies in disease surveillance; the spatial and temporal dimensions of environmental geography in the spread of disease; and the advances in GIScience that provide real-world surveillance and monitoring of disease and injuries at mass gatherings.

Thailand momentum on policy and practice in local legislation on dengue vector control

Over a past decade, an administrative decentralization model, adopted for local administration development in Thailand, is replacing the prior centralized (top-down) command system. The change offers challenges to local governmental agencies and other public health agencies at all the ministerial, regional, and provincial levels. A public health regulatory and legislative framework for dengue vector control by local governmental agencies is a national topic of interest because dengue control program has been integrated into healthcare services at the provincial level and also has been given priority in health plans of local governmental agencies. The enabling environments of local administrations are unique, so this critical review focuses on the authority of local governmental agencies responsible for disease prevention and control and on the functioning of local legislation with respect to dengue vector control and practices.

Evaluation of Internet-based dengue query data: Google Dengue Trends

Dengue is a common and growing problem worldwide, with an estimated 70–140 million cases per year. Traditional, healthcare-based, government-implemented dengue surveillance is resource intensive and slow. As global Internet use has increased, novel, Internet-based disease monitoring tools have emerged. Google Dengue Trends (GDT) uses near real-time search query data to create an index of dengue incidence that is a linear proxy for traditional surveillance. Studies have shown that GDT correlates highly with dengue incidence in multiple countries on a large spatial scale. This study addresses the heterogeneity of GDT at smaller spatial scales, assessing its accuracy at the state-level in Mexico and identifying factors that are associated with its accuracy. We used Pearson correlation to estimate the association between GDT and traditional dengue surveillance data for Mexico at the national level and for 17 Mexican states. Nationally, GDT captured approximately 83% of the variability in reported cases over the 9 study years. The correlation between GDT and reported cases varied from state to state, capturing anywhere from 1% of the variability in Baja California to 88% in Chiapas, with higher accuracy in states with higher dengue average annual incidence. A model including annual average maximum temperature, precipitation, and their interaction accounted for 81% of the variability in GDT accuracy between states. This climate model was the best indicator of GDT accuracy, suggesting that GDT works best in areas with intense transmission, particularly where local climate is well suited for transmission. Internet accessibility (average ∼36%) did not appear to affect GDT accuracy. While GDT seems to be a less robust indicator of local transmission in areas of low incidence and unfavorable climate, it may indicate cases among travelers in those areas. Identifying the strengths and limitations of novel surveillance is critical for these types of data to be used to make public health decisions and forecasting models.

Dengue is a common and growing problem worldwide. Delays in traditional surveillance systems limit the ability of public health agencies to identify and respond to dengue outbreaks efficiently. Internet search queries provide near real-time indicators of infectious disease activity and have proven effective for monitoring disease activity in some countries, but have not been assessed on smaller geographic areas. We compared Google Dengue Trends data for 17 states in Mexico to traditional surveillance data from those states. We found that the utility of Google Dengue Trends at the state-level is highly variable and depends on climatic conditions supporting dengue virus transmission. Novel surveillance tools like Google Dengue Trends can provide timely information to public health agencies, but to be useful on a local scale, they must be considered within the local context of dengue transmissibility.

Projecting the impact of climate change on dengue transmission in Dhaka, Bangladesh

Weather variables, mainly temperature and humidity influence vectors, viruses, human biology, ecology and consequently the intensity and distribution of the vector-borne diseases. There is evidence that warmer temperature due to climate change will influence the dengue transmission. However, long term scenario-based projections are yet to be developed. Here, we assessed the impact of weather variability on dengue transmission in a megacity of Dhaka, Bangladesh and projected the future dengue risk attributable to climate change. Our results show that weather variables particularly temperature and humidity were positively associated with dengue transmission. The effects of weather variables were observed at a lag of four months. We projected that assuming a temperature increase of 3.3°C without any adaptation measure and changes in socio-economic condition, there will be a projected increase of 16,030 dengue cases in Dhaka by the end of this century. This information might be helpful for the public health authorities to prepare for the likely increase of dengue due to climate change. The modelling framework used in this study may be applicable to dengue projection in other cities.

Crouching tiger, hidden trouble: urban sources of Aedes albopictus (Diptera: Culicidae) refractory to source-reduction

Our ultimate objective is to design cost-effective control strategies for Aedes albopictus, the Asian tiger mosquito, an important urban nuisance and disease vector that expanded worldwide during the last 40 years.  We conducted mosquito larval surveys from May through October 2009 in the City of Trenton, New Jersey, USA, while performing intensive monthly source-reduction campaigns that involved removing, emptying, or treating all accessible containers with larvicides and pupicides. We examined patterns of occurrence of Ae. albopictus and Culex pipiens, another urban mosquito, among different container types by comparing observed and expected number of positive containers of each type. Expected use was based on the relative frequency of each container type in the environment. Aedes albopictus larvae and pupae were found significantly more often than expected in medium volumes of water in buckets and plant saucers but were rarely collected in small volumes of water found in trash items such as discarded cups and cans. They were also absent from large volumes of water such as in abandoned swimming pools and catch basins, although we consistently collected Cx. pipiens from those habitats. The frequency of Ae. albopictus in tires indicated rapid and extensive use of these ubiquitous urban containers. Standard larval-based indices did not correlate with adult catches in BG-Sentinel traps, but when based only on Ae. albopictus key containers (buckets, plant saucers, equipment with pockets of water, and tires) they did. Although we found that only 1.2% of the 20,039 water-holding containers examined contained immature Ae. albopictus (5.3% if only key containers were counted), adult populations were still above nuisance action thresholds six times during the 2009 mosquito season. We conclude that in urban New Jersey, effective source reduction for Ae. albopictus control will require scrupulous and repeated cleaning or treatment of everyday use containers and extensive homeowner collaboration.

Measurement of dengue epidemic spreading pattern using density analysis method: retrospective spatial statistical study of dengue in Subang Jaya, Malaysia, 2006-2010

BACKGROUND

Dengue has emerged as one of the major public health problems in Malaysia. The Ministry of Health, Malaysia, is committed in monitoring and controlling this disease for many years. The objective of this study is to analyze the dengue outbreak pattern on a monthly basis in Subang Jaya in terms of their spatial dissemination and hotspot identification.

METHODS

Collated dengue cases data covering a 5-year period (2006-2010) retrieved from a municipal surveillance system of Subang Jaya were georeferenced and then converted into Geographical Information System format. Average nearest neighbor (ANN) analysis and kernel density (KD) estimation were used to assess the spatial dissemination of dengue cases and detect dengue hotspots, respectively.

RESULTS

The spatial patterns of dengue fever cases during the 5-year period were spatially clustered (with R values < 1) based on the monthly frequency data. The hotspot map produced by the KD techniques showed a spatially diffused pattern.

CONCLUSION

The methodology used in the study and the result obtained could be useful not only for documentation by epidemiologists but also for active surveillance of dengue outbreak in a locality.

A shorter time interval between first and second dengue infections is associated with protection from clinical illness in a school-based cohort in Thailand

BACKGROUND

Despite the strong association between secondary dengue virus (DENV) infections and dengue hemorrhagic fever (DHF), the majority of secondary infections are subclinical or mild. The determinants of clinical severity remain unclear, though studies indicate a titer-dependent and time-dependent role of cross-protective anti-DENV antibodies.

METHODS

Data from 2 sequential prospective cohort studies were analyzed for subclinical and symptomatic DENV infections in schoolchildren in Kamphaeng Phet, Thailand (1998-2002 and 2004-2007). Children experiencing ≥ 1 DENV infection were selected as the population for analysis (contributing 2169 person-years of follow-up).

RESULTS

In total, 1696 children had ≥ 1 DENV infection detected during their enrollment; 268 experienced 2 or more infections. A shorter time interval between infections was associated with subclinical infection in children seronegative for DENV at enrollment, for whom a second-detected DENV infection is more likely to reflect a true second infection (average of 2.6 years between infections for DHF, 1.9 for DF, and 1.6 for subclinical infections).

CONCLUSIONS

These findings support a pathogenesis model where cross-reactive antibodies wane from higher-titer, protective levels to lower-titer, detrimental levels. This is one of the first studies of human subjects to suggest a window of cross-protection following DENV infection since Sabin's challenge studies in the 1940s.

A prospective cohort study to evaluate peridomestic infection as a determinant of dengue transmission: protocol

Background

Vector control programs, which have focused mainly on the patient house and peridomestic areas around dengue cases, have not produced the expected impact on transmission. This project will evaluate the assumption that the endemic/epidemic transmission of dengue begins around peridomestic vicinities of the primary cases. Its objective is to assess the relationship between symptomatic dengue case exposure and peridomestic infection incidence.

Methods/Design

A prospective cohort study will be conducted (in Tepalcingo and Axochiapan, in the state of Morelos, Mexico), using the state surveillance system for the detection of incident cases. Paired blood specimens will be collected from both the individuals who live with the incident cases and a sample of subjects residing within a 25-meter radius of such cases (exposed cohort), in order to measure dengue-specific antibodies. Other subjects will be selected from areas which have not presented any incident cases within 200 meters, during the two months preceding the sampling (non-exposed cohort). Symptomatic/asymptomatic incident infection will be considered as the dependent variable, exposure to confirmed dengue cases, as the principal variable, and the socio-demographic, environmental and socio-cultural conditions of the subjects, as additional explanatory variables.

Discussion

Results indicating a high infection rate among the exposed subjects would justify the application of peridomestic control measures and call for an evaluation of alternate causes for insufficient program impact. On the other hand, a low incidence of peridomestic-infected subjects would support the hypothesis that infection occurs outside the domicile, and would thus explain why the vector control measures applied in the past have exerted such a limited impact on cases incidence rates. The results of the present study may therefore serve to reassess site selection for interventions of this type.

Effectiveness of Space Spraying on the Transmission of Dengue/Dengue Hemorrhagic Fever (DF/DHF) in an Urban Area of Southern Thailand

Timely and extensive space spraying has been widely used to prevent the spread of dengue fever/dengue hemorrhagic fever (DF/DHF). Field evaluations on its effectiveness have been rarely reported. This study aimed to evaluate the timeliness, coverage, and effectiveness of space spraying for DF/DHF control using a geographic information system (GIS). Longitudinal monitoring of DF/DHF cases and spray activities in Songkhla municipality was done between May 2006 and April 2007. After a case was detected, subsequent cases occurring within a 100 meter radius of the index case's house and between 16-35 days of onset were considered as potential secondary cases. During the study period, 140 cases of DF/DHF were detected. Of these, 25 were identified as secondary infections from 20 index cases. Where a secondary infection occurred, the mean attack rate was 2.7 per 1,000 population. Two significant predictors for being a secondary case were both related to the house of the index case, namely, absence of window screens and being constructed with corrugated iron sheets. Our findings suggest that space spraying in the study area was inadequate and often failed to prevent secondary cases of DF/DHF. Control programs should target houses constructed with corrugated iron sheets.