Dengue hemorrhagic fever epidemiology in Thailand: description and forecasting of epidemics

The Potential of Surveillance Data for Dengue Risk Mapping: An Evaluation of Different Approaches in Cuba

To better guide dengue prevention and control efforts, the use of routinely collected data to develop risk maps is proposed. For this purpose, dengue experts identified indicators representative of entomological, epidemiological and demographic risks, hereafter called components, by using surveillance data aggregated at the level of Consejos Populares (CPs) in two municipalities of Cuba (Santiago de Cuba and Cienfuegos) in the period of 2010-2015. Two vulnerability models (one with equally weighted components and one with data-derived weights using Principal Component Analysis), and three incidence-based risk models were built to construct risk maps. The correlation between the two vulnerability models was high (tau > 0.89). The single-component and multicomponent incidence-based models were also highly correlated (tau ≥ 0.9). However, the agreement between the vulnerability- and the incidence-based risk maps was below 0.6 in the setting with a prolonged history of dengue transmission. This may suggest that an incidence-based approach does not fully reflect the complexity of vulnerability for future transmission. The small difference between single- and multicomponent incidence maps indicates that in a setting with a narrow availability of data, simpler models can be used. Nevertheless, the generalized linear mixed multicomponent model provides information of covariate-adjusted and spatially smoothed relative risks of disease transmission, which can be important for the prospective evaluation of an intervention strategy. In conclusion, caution is needed when interpreting risk maps, as the results vary depending on the importance given to the components involved in disease transmission. The multicomponent vulnerability mapping needs to be prospectively validated based on an intervention trial targeting high-risk areas.

The practicality of Malaysia dengue outbreak forecasting model as an early warning system

Dengue is a harmful tropical disease that causes death to many people. Currently, the dengue vaccine development is still at an early stage, and only intervention methods exist after dengue cases increase. Thus, previously, two scientific experimental field studies were conducted in producing a dengue outbreak forecasting model as an early warning system. Successfully, an Autoregressive Distributed Lag (ADL) Model was developed using three factors: the epidemiological, entomological, and environmental with an accuracy of 85%; but a higher percentage is required in minimizing the error for the model to be useful. Hence, this study aimed to develop a practical and cost-effective dengue outbreak forecasting model with at least 90% accuracy to be embedded in an early warning computer system using the Internet of Things (IoT) approach. Eighty-one weeks of time series data of the three factors were used in six forecasting models, which were Autoregressive Distributed Lag (ADL), Hierarchical Forecasting (Bottom-up and Optimal combination) and three Machine Learning methods: (Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest). Five error measures were used to evaluate the consistency performance of the models in order to ensure model performance. The findings indicated Random Forest outperformed the other models with an accuracy of 95% when including all three factors. But practically, collecting mosquito related data (the entomological factor) was very costly and time consuming. Thus, it was removed from the model, and the accuracy dropped to 92% but still high enough to be of practical use, i.e., beyond 90%. However, the practical ground operationalization of the early warning system also requires several rain gauges to be located at the dengue hot spots due to localized rainfall. Hence, further analysis was conducted in determining the location of the rain gauges. This has led to the recommendation that the rain gauges should be located about 3–4 km apart at the dengue hot spots to ensure the accuracy of the rainfall data to be included in the dengue outbreak forecasting model so that it can be embedded in the early warning system. Therefore, this early warning system can save lives, and prevention is better than cure.

Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks: What is the evidence? A scoping review

Background

Early warning systems (EWSs) are of increasing importance in the context of outbreak-prone diseases such as chikungunya, dengue, malaria, yellow fever, and Zika. A scoping review has been undertaken for all 5 diseases to summarize existing evidence of EWS tools in terms of their structural and statistical designs, feasibility of integration and implementation into national surveillance programs, and the users’ perspective of their applications.

Methods

Data were extracted from Cochrane Database of Systematic Reviews (CDSR), Google Scholar, Latin American and Caribbean Health Sciences Literature (LILACS), PubMed, Web of Science, and WHO Library Database (WHOLIS) databases until August 2019. Included were studies reporting on (a) experiences with existing EWS, including implemented tools; and (b) the development or implementation of EWS in a particular setting. No restrictions were applied regarding year of publication, language or geographical area.

Findings

Through the first screening, 11,710 documents for dengue, 2,757 for Zika, 2,706 for chikungunya, 24,611 for malaria, and 4,963 for yellow fever were identified. After applying the selection criteria, a total of 37 studies were included in this review. Key findings were the following: (1) a large number of studies showed the quality performance of their prediction models but except for dengue outbreaks, only few presented statistical prediction validity of EWS; (2) while entomological, epidemiological, and social media alarm indicators are potentially useful for outbreak warning, almost all studies focus primarily or exclusively on meteorological indicators, which tends to limit the prediction capacity; (3) no assessment of the integration of the EWS into a routine surveillance system could be found, and only few studies addressed the users’ perspective of the tool; (4) almost all EWS tools require highly skilled users with advanced statistics; and (5) spatial prediction remains a limitation with no tool currently able to map high transmission areas at small spatial level.

Conclusions

In view of the escalating infectious diseases as global threats, gaps and challenges are significantly present within the EWS applications. While some advanced EWS showed high prediction abilities, the scarcity of tool assessments in terms of integration into existing national surveillance systems as well as of the feasibility of transforming model outputs into local vector control or action plans tends to limit in most cases the support of countries in controlling disease outbreaks.

Infectious Disease Relational Data Analysis Using String Grammar Non-Euclidean Relational Fuzzy C-Means

Statistical analysis in infectious diseases is becoming more important, especially in prevention policy development. To achieve that, the epidemiology, a study of the relationship between the occurrence and who/when/where, is needed. In this paper, we develop the string grammar non-Euclidean relational fuzzy C-means (sgNERF-CM) algorithm to determine a relationship inside the data from the age, career, and month viewpoint for all provinces in Thailand for the dengue fever, influenza, and Hepatitis B virus (HBV) infection. The Dunn's index is used to select the best models because of its ability to identify the compact and well-separated clusters. We compare the results of the sgNERF-CM algorithm with the string grammar relational hard C-means (sgRHCM) algorithm. In addition, their numerical counterparts, i.e., relational hard C-means (RHCM) and non-Euclidean relational fuzzy C-means (NERF-CM) algorithms are also applied in the comparison. We found that the sgNERF-CM algorithm is far better than the numerical counterparts and better than the sgRHCM algorithm in most cases. From the results, we found that the month-based dataset does not help in relationship-finding since the diseases tend to happen all year round. People from different age ranges in different regions in Thailand have different numbers of dengue fever infections. The occupations that have a higher chance to have dengue fever are student and teacher groups from the central, north-east, north, and south regions. Additionally, students in all regions, except the central region, have a high risk of dengue infection. For the influenza dataset, we found that a group of people with the age of more than 1 year to 64 years old has higher number of influenza infections in every province. Most occupations in all regions have a higher risk of infecting the influenza. For the HBV dataset, people in all regions with an age between 10 to 65 years old have a high risk in infecting the disease. In addition, only farmer and general contractor groups in all regions have high chance of infecting HBV as well.

Epidemiological characteristics and determination of spatio-temporal clusters during the 2013 dengue outbreak in Chiang Mai, Thailand

Dengue is the worldwide most important mosquito-borne viral disease in humans. A large dengue outbreak occurred in Chiang Mai, Thailand in 2013. The aims of this study were to describe the epidemiology of this outbreak and determine the spatio-temporal pattern in the sub-district with the highest number of dengue cases. Data on patients, including date of illness, were obtained from the Chiang Mai Provincial Public Health Center and analyzed descriptively using R statistical software. The geographic location of patients' residences was determined from available geographical information databases supplemented with coordinated data collection in the field. A space-time permutation model from SaTScan™ was used to determine disease clusters corresponding to space and time. Results showed that Muang District, the centre of the province, had a higher number of cases than the other 25 districts. The Suthep subdistrict, part of Muang District, had most of the patients: 625 subjects distributed between 213 residences. The space-time analysis identified a primary cluster and 7 secondary clusters in different time periods. The primary cluster had 128 patients in a period of approximately 3 months. The number of patients in the secondary clusters ranged between 7 and 65. Most of the clusters occurred in densely populated areas during June and July (the rainy season). The finding from this study may support health agencies to plan surveillance campaigns for people at specified local areas with a high incidence of the disease.

Climate change induced vulnerability and adaption for dengue incidence in Colombo and Kandy districts: the detailed investigation in Sri Lanka

Background

Assessing the vulnerability of an infectious disease such as dengue among endemic population is an important requirement to design proactive programmes in order to improve resilience capacity of vulnerable communities. The current study aimed to evaluate the climate change induced socio-economic vulnerability of local communities to dengue in Colombo and Kandy districts of Sri Lanka.

Methods

A total of 42 variables (entomological, epidemiological, meteorological parameters, land-use practices and socio-demographic data) of all the 38 Medical Officer of Health (MOH) areas in the districts of Colombo and Kandy were considered as candidate variables for a composite index based vulnerability assessment. The Principal Component Analysis (PCA) was used in selecting and setting the weight for each indicator. Exposure, Sensitivity, Adaptive Capacity and Vulnerability of all MOH areas for dengue were calculated using the composite index approach recommended by the Intergovernmental Panel on Climate Change.

Results

Out of 42 candidate variables, only 23 parameters (Exposure Index: six variables; Sensitivity Index: 11 variables; Adaptive Capacity Index: six variables) were selected as indicators to assess climate change vulnerability to dengue. Colombo Municipal Council (CMC) MOH area denoted the highest values for exposure (0.89: exceptionally high exposure), sensitivity (0.86: exceptionally high sensitivity) in Colombo, while Kandy Municipal Council (KMC) area reported the highest exposure (0.79: high exposure) and sensitivity (0.77: high sensitivity) in Kandy. Piliyandala MOH area denoted the highest level of adaptive capacity (0.66) in Colombo followed by Menikhinna (0.68) in Kandy. The highest vulnerability (0.45: moderate vulnerability) to dengue was indicated from CMC and the lowest indicated from Galaha MOH (0.15; very low vulnerability) in Kandy. Interestingly the KMC MOH area had a notable vulnerability of 0.41 (moderate vulnerability), which was the highest within Kandy.

Conclusions

In general, vulnerability for dengue was relatively higher within the MOH areas of Colombo, than in Kandy, suggesting a higher degree of potential susceptibility to dengue within and among local communities of Colombo. Vector Controlling Entities are recommended to consider the spatial variations in vulnerability of local communities to dengue for decision making, especially in allocation of limited financial, human and mechanical resources for dengue epidemic management.

Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics

The study of infectious disease behavior has been a scientific concern for many years as early identification of outbreaks provides great advantages including timely implementation of public health measures to limit the spread of an epidemic. We propose a methodology that merges the predictions of (i) a computational model with machine learning, (ii) a projection model, and (iii) a proposed smoothed endemic channel calculation. The predictions are made on weekly acute respiratory infection (ARI) data obtained from epidemiological reports in Mexico, along with the usage of key terms in the Google search engine. The results obtained with this methodology were compared with state-of-the-art techniques resulting in reduced root mean squared percentage error (RMPSE) and maximum absolute percent error (MAPE) metrics, achieving a MAPE of 21.7%. This methodology could be extended to detect and raise alerts on possible outbreaks on ARI as well as for other seasonal infectious diseases.

The association between dengue incidences and provincial-level weather variables in Thailand from 2001 to 2014

Dengue and dengue hemorrhagic pose significant burdens in many tropical countries. Dengue incidences have perpetually increased, leading to an annual (uncertain) peak. Dengue cases cause an enormous public health problem in Thailand because there is no anti-viral drug against the dengue virus. Searching for means to reduce the dengue incidences is a challenging and appropriate strategy for primary prevention in a dengue outbreak. This study constructs the best predictive model from past statistical dengue incidences at the provincial level and studies the relationships among dengue incidences and weather variables. We conducted experiments for 65 provinces (out of 77 provinces) in Thailand since there is no dengue information for the remaining provinces. Predictive models were constructed using weekly data during 2001-2014. The training set are data during 2001-2013, and the test set is the data from 2014. Collected data were separated into two parts: current dengue cases as the dependent variable, and weather variables and previous dengue cases as the independent variables. Eight weather variables are used in our models: average pressure, maximum temperature, minimum temperature, average humidity, precipitation, vaporization, wind direction, wind power. Each weather variable includes the current week and one to three weeks of lag time. A total of 32 independent weather variables are used for each province. The previous one to three weeks of dengue cases are also used as independent variables. There is a total of 35 independent variables. Predictive models were constructed using five methods: Poisson regression, negative binomial regression, quasi-likelihood regression, ARIMA(3,1,4) and SARIMA(2,0,1)(0,2,0). The best model is determined by combinations of 1–12 variables, which are 232,989,800 models for each province. We construct a total of 15,144,337,000 models. The best model is selected by the average from high to low of the coefficient of determination (R2) and the lowest root mean square error (RMSE). From our results, the one-week lag previous case variable is the most frequent in 55 provinces out of a total of 65 provinces (coefficient of determinations with a minimum of 0.257 and a maximum of 0.954, average of 0.6383, 95% CI: 0.57313 to 0.70355). The most influential weather variable is precipitation, which is used in most of the provinces, followed by wind direction, wind power, and barometric pressure. The results confirm the common knowledge that dengue incidences occur most often during the rainy season. It also shows that wind direction, wind power, and barometric pressure also have influences on the number of dengue cases. These three weather variables may help adult mosquitos to survive longer and spread dengue. In conclusion, The most influential factor for further cases is the number of dengue cases. However, weather variables are also needed to obtain better results. Predictions of the number of dengue cases should be done locally, not at the national level. The best models of different provinces use different sets of weather variables. Our model has an accuracy that is sufficient for the real prediction of future dengue incidences, to prepare for and protect against severe dengue outbreaks.

Dengue and chikungunya among outpatients with acute undifferentiated fever in Kinshasa, Democratic Republic of Congo: A cross-sectional study

Background

Pathogens causing acute fever, with the exception of malaria, remain largely unidentified in sub-Saharan Africa, given the local unavailability of diagnostic tests and the broad differential diagnosis.

Methodology

We conducted a cross-sectional study including outpatient acute undifferentiated fever in both children and adults, between November 2015 and June 2016 in Kinshasa, Democratic Republic of Congo. Serological and molecular diagnostic tests for selected arboviral infections were performed on blood, including PCR, NS1-RDT, ELISA and IFA for acute, and ELISA and IFA for past infections.

Results

Investigation among 342 patients, aged 2 to 68 years (mean age of 21 years), with acute undifferentiated fever (having no clear focus of infection) revealed 19 (8.1%) acute dengue–caused by DENV-1 and/or DENV-2 –and 2 (0.9%) acute chikungunya infections. Furthermore, 30.2% and 26.4% of participants had been infected in the past with dengue and chikungunya, respectively. We found no evidence of acute Zika nor yellow fever virus infections. 45.3% of patients tested positive on malaria Rapid Diagnostic Test, 87.7% received antimalarial treatment and 64.3% received antibacterial treatment.

Discussion

Chikungunya outbreaks have been reported in the study area in the past, so the high seroprevalence is not surprising. However, scarce evidence exists on dengue transmission in Kinshasa and based on our data, circulation is more important than previously reported. Furthermore, our study shows that the prescription of antibiotics, both antibacterial and antimalarial drugs, is rampant. Studies like this one, elucidating the causes of acute fever, may lead to a more considerate and rigorous use of antibiotics. This will not only stem the ever-increasing problem of antimicrobial resistance, but will–ultimately and hopefully–improve the clinical care of outpatients in low-resource settings.

Trial registration

ClinicalTrials.gov NCT02656862.

Malaria remains one of the most important causes of fever in sub-Saharan Africa. However, its share is declining, since the diagnosis and treatment of malaria have improved significantly over the years. Hence leading to an increase in the number of patients presenting with non-malarial fever. Often, obvious clinical signs and symptoms like cough or diarrhea are absent, probing the question: “What causes the fever?” Previous studies have shown that the burden of arboviral infections–like dengue and chikungunya–in sub-Saharan Africa is underestimated, which is why we screened for four common arboviral infections in patients presenting with ‘undifferentiated fever’ at an outpatient clinic in suburban Kinshasa, Democratic Republic of Congo. Among the patients tested, we found that one in ten presented with an acute arboviral infection and that almost one in three patients had been infected in the past. These findings suggest that clinicians should think about arboviral infections more often, thereby refraining from the prescription of antibiotics, a practice increasingly problematic given the global rise of antimicrobial resistance.

A framework for evaluating the effects of observational type and quality on vector-borne disease forecast

Recent research has advanced infectious disease forecasting from an aspiration to an operational reality. The accuracy of such operational forecasting depends on the quantity and quality of observations available for system optimization. In particular, for forecasting systems that use combined mechanistic model-inference approaches, a broad suite of epidemiological observations could be utilized, if these data were available in near real time. In cases where such data are limited, an in silica, synthetic framework for evaluating the potential benefits of observations on forecasting accuracy can allow researchers and public health officials to more optimally allocate resources for disease surveillance and monitoring. Here, we demonstrate the application of such a framework, using a model-inference system designed to predict dengue, and identify the type and quality of observations that would improve forecasting accuracy.

Modelling the association of dengue fever cases with temperature and relative humidity in Jeddah, Saudi Arabia-A generalised linear model with break-point analysis

The aim of this study was to examine the role of environmental factors in the temporal distribution of dengue fever in Jeddah, Saudi Arabia. The relationship between dengue fever cases and climatic factors such as relative humidity and temperature was investigated during 2006-2009 to determine whether there is any relationship between dengue fever cases and climatic parameters in Jeddah City, Saudi Arabia. A generalised linear model (GLM) with a break-point was used to determine how different levels of temperature and relative humidity affected the distribution of the number of cases of dengue fever. Break-point analysis was performed to modelled the effect before and after a break-point (change point) in the explanatory parameters under various scenarios. Akaike information criterion (AIC) and cross validation (CV) were used to assess the performance of the models. The results showed that maximum temperature and mean relative humidity are most probably the better predictors of the number of dengue fever cases in Jeddah. In this study three scenarios were modelled: no time lag, 1-week lag and 2-weeks lag. Among these scenarios, the 1-week lag model using mean relative humidity as an explanatory variable showed better performance. This study showed a clear relationship between the meteorological variables and the number of dengue fever cases in Jeddah. The results also demonstrated that meteorological variables can be successfully used to estimate the number of dengue fever cases for a given period of time. Break-point analysis provides further insight into the association between meteorological parameters and dengue fever cases by dividing the meteorological parameters into certain break-points.

Changing paradigms in Aedes control: considering the spatial heterogeneity of dengue transmission

Current dengue vector control strategies, focusing on reactive implementation of insecticide-based interventions in response to clinically apparent disease manifestations, tend to be inefficient, short-lived, and unsustainable within the worldwide epidemiological scenario of virus epidemic recrudescence. As a result of a series of expert meetings and deliberations, a paradigm shift is occurring and a new strategy, using risk stratification at the city level in order to concentrate proactive, sustained efforts in areas at high risk for transmission, has emerged. In this article, the authors 1) outline this targeted, proactive intervention strategy, within the context of dengue epidemiology, the dynamics of its transmission, and current Aedes control strategies, and 2) provide support from published literature for the need to empirically test its impact on dengue transmission as well as on the size of disease outbreaks. As chikungunya and Zika viruses continue to expand their range, the need for a science-based, proactive approach for control of urban Aedes spp. mosquitoes will become a central focus of integrated disease management planning.

Dengue

Dengue is one of the most important mosquito-borne viral infections caused by single-stranded RNA virus that are transmitted by the Aedes aegypti and Aedes albopictus mosquito species. Dengue is endemic in over 140 countries in Asia, the USA, the Eastern Mediterranean, and Africa. The World Health Organization (WHO) estimated that there are more than 2.5 billion people—mainly occurs in children living in tropical and subtropical countries—at risk of dengue infection with one or more dengue viruses. There are estimated nearly 100 million symptomatic dengue infections occurring worldwide annually, nearly 75% in Asia and the Western Pacific region [1]. During the past decades, the outbreaks of dengue infection have been reported throughout the world with increased severity. Ecologic and demographic changes are considered to be the contributing factors to the emergence of dengue infection in the past decades. Dengue has expanded into new countries and into urban settings associated with increased distribution of A. aegypti, population growth, urbanization, development of slums, migration of population, movement of dengue virus by infected travelers, trade development, and improved diagnostic capabilities in medical practice [2, 3]. Increased transmission of dengue virus in tropical urban areas has been created by substandard housing and crowding as well as deterioration in water, sewer, and waste management systems, all of which are intimately associated with unplanned urbanization [4–7]. So it is likely that dengue will expand its geographic reach and become an increasing burden on health resources in affected areas during the next decade. An effective vector-control management is the only means to reduce dengue infection in endemic areas. Because vector control has achieved only limited success so far in reducing the transmission of dengue, the usage of effective dengue vaccine in target population along with the preventive measures already used such as raising public awareness may be the means to effectively control of this disease in endemic area [8].

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.

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.

Analysis of significant factors for dengue fever incidence prediction

Background

Many popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak.

Results

The predictive power of the forecasting model-assessed by Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and the mean absolute percentage error (MAPE)-is improved by including the new parameters for dengue outbreak prediction. This study’s selected model outperforms all three other competing models with the lowest AIC, the lowest BIC, and a small MAPE value. The exclusive use of climate factors from similar locations decreases a model’s prediction power. The multivariate Poisson regression, however, effectively forecasts even when climate variables are slightly different. Female mosquitoes and seasons were strongly correlated with dengue cases. Therefore, the dengue incidence trends provided by this model will assist the optimization of dengue prevention.

Conclusions

The present work demonstrates the important roles of female mosquito infection rates from the previous season and climate factors (represented as seasons) in dengue outbreaks. Incorporating these two factors in the model significantly improves the predictive power of dengue hemorrhagic fever forecasting models, as confirmed by AIC, BIC, and MAPE.

Mining local climate data to assess spatiotemporal dengue fever epidemic patterns in French Guiana

OBJECTIVE

To identify local meteorological drivers of dengue fever in French Guiana, we applied an original data mining method to the available epidemiological and climatic data. Through this work, we also assessed the contribution of the data mining method to the understanding of factors associated with the dissemination of infectious diseases and their spatiotemporal spread.

METHODS

We applied contextual sequential pattern extraction techniques to epidemiological and meteorological data to identify the most significant climatic factors for dengue fever, and we investigated the relevance of the extracted patterns for the early warning of dengue outbreaks in French Guiana.

RESULTS

The maximum temperature, minimum relative humidity, global brilliance, and cumulative rainfall were identified as determinants of dengue outbreaks, and the precise intervals of their values and variations were quantified according to the epidemiologic context. The strongest significant correlations were observed between dengue incidence and meteorological drivers after a 4-6-week lag.

DISCUSSION

We demonstrated the use of contextual sequential patterns to better understand the determinants of the spatiotemporal spread of dengue fever in French Guiana. Future work should integrate additional variables and explore the notion of neighborhood for extracting sequential patterns.

CONCLUSIONS

Dengue fever remains a major public health issue in French Guiana. The development of new methods to identify such specific characteristics becomes crucial in order to better understand and control spatiotemporal transmission.

Dynamic spatiotemporal trends of dengue transmission in the Asia-Pacific region, 1955-2004

Background

Dengue fever (DF) is one of the most important emerging arboviral human diseases. Globally, DF incidence has increased by 30-fold over the last fifty years, and the geographic range of the virus and its vectors has expanded. The disease is now endemic in more than 120 countries in tropical and subtropical parts of the world. This study examines the spatiotemporal trends of DF transmission in the Asia-Pacific region over a 50-year period, and identified the disease’s cluster areas.

Methodology and Findings

The World Health Organization’s DengueNet provided the annual number of DF cases in 16 countries in the Asia-Pacific region for the period 1955 to 2004. This fifty-year dataset was divided into five ten-year periods as the basis for the investigation of DF transmission trends. Space-time cluster analyses were conducted using scan statistics to detect the disease clusters. This study shows an increasing trend in the spatiotemporal distribution of DF in the Asia-Pacific region over the study period. Thailand, Vietnam, Laos, Singapore and Malaysia are identified as the most likely clusters (relative risk = 13.02) of DF transmission in this region in the period studied (1995 to 2004). The study also indicates that, for the most part, DF transmission has expanded southwards in the region.

Conclusions

This information will lead to the improvement of DF prevention and control strategies in the Asia-Pacific region by prioritizing control efforts and directing them where they are most needed.

Sharing experiences: towards an evidence based model of dengue surveillance and outbreak response in Latin America and Asia

Background

The increasing frequency and intensity of dengue outbreaks in endemic and non-endemic countries requires a rational, evidence based response. To this end, we aimed to collate the experiences of a number of affected countries, identify strengths and limitations in dengue surveillance, outbreak preparedness, detection and response and contribute towards the development of a model contingency plan adaptable to country needs.

Methods

The study was undertaken in five Latin American (Brazil, Colombia, Dominican Republic, Mexico, Peru) and five in Asian countries (Indonesia, Malaysia, Maldives, Sri Lanka, Vietnam). A mixed-methods approach was used which included document analysis, key informant interviews, focus-group discussions, secondary data analysis and consensus building by an international dengue expert meeting organised by the World Health Organization, Special Program for Research and Training in Tropical Diseases (WHO-TDR).

Results

Country information on dengue is based on compulsory notification and reporting (“passive surveillance”), with laboratory confirmation (in all participating Latin American countries and some Asian countries) or by using a clinical syndromic definition. Seven countries additionally had sentinel sites with active dengue reporting, some also had virological surveillance. Six had agreed a formal definition of a dengue outbreak separate to seasonal variation in case numbers. Countries collected data on a range of warning signs that may identify outbreaks early, but none had developed a systematic approach to identifying and responding to the early stages of an outbreak. Outbreak response plans varied in quality, particularly regarding the early response. The surge capacity of hospitals with recent dengue outbreaks varied; those that could mobilise additional staff, beds, laboratory support and resources coped best in comparison to those improvising a coping strategy during the outbreak. Hospital outbreak management plans were present in 9/22 participating hospitals in Latin-America and 8/20 participating hospitals in Asia.

Conclusions

Considerable variation between countries was observed with regard to surveillance, outbreak detection, and response. Through discussion at the expert meeting, suggestions were made for the development of a more standardised approach in the form of a model contingency plan, with agreed outbreak definitions and country-specific risk assessment schemes to initiate early response activities according to the outbreak phase. This would also allow greater cross-country sharing of ideas.

Predicting dengue outbreaks using approximate entropy algorithm and pattern recognition

OBJECTIVES

The prediction of dengue outbreaks is a critical concern in many countries. However, the setup of an ideal prediction system requires establishing numerous monitoring stations and performing data analysis, which are costly, time-consuming, and may not achieve the desired results. In this study, we developed a novel method for predicting impending dengue fever outbreaks several weeks prior to their occurrence.

METHODS

By reversing moving approximate entropy algorithm and pattern recognition on time series compiled from the weekly case registry of the Center for Disease Control, Taiwan, 1998-2010, we compared the efficiencies of two patterns for predicting the outbreaks of dengue fever.

RESULTS

The sensitivity of this method is 0.68, and the specificity is 0.54 using Pattern A to make predictions. Pattern B had a sensitivity of 0.90 and a specificity of 0.46. Patterns A and B make predictions 3.1 ± 2.2 weeks and 2.9 ± 2.4 weeks before outbreaks, respectively.

CONCLUSIONS

Combined with pattern recognition, reversed moving approximate entropy algorithm on the time series built from weekly case registry is a promising tool for predicting the outbreaks of dengue fever.

Is temperature the main cause of dengue rise in non-endemic countries? The case of Argentina

BACKGROUND

Dengue cases have increased during the last decades, particularly in non-endemic areas, and Argentina was no exception in the southern transmission fringe. Although temperature rise has been blamed for this, human population growth, increased travel and inefficient vector control may also be implicated. The relative contribution of geographic, demographic and climatic of variables on the occurrence of dengue cases was evaluated.

METHODS

According to dengue history in the country, the study was divided in two decades, a first decade corresponding to the reemergence of the disease and the second including several epidemics. Annual dengue risk was modeled by a temperature-based mechanistic model as annual days of possible transmission. The spatial distribution of dengue occurrence was modeled as a function of the output of the mechanistic model, climatic, geographic and demographic variables for both decades.

RESULTS

According to the temperature-based model dengue risk increased between the two decades, and epidemics of the last decade coincided with high annual risk. Dengue spatial occurrence was best modeled by a combination of climatic, demographic and geographic variables and province as a grouping factor. It was positively associated with days of possible transmission, human population number, population fall and distance to water bodies. When considered separately, the classification performance of demographic variables was higher than that of climatic and geographic variables.

CONCLUSIONS

Temperature, though useful to estimate annual transmission risk, does not fully describe the distribution of dengue occurrence at the country scale. Indeed, when taken separately, climatic variables performed worse than geographic or demographic variables. A combination of the three types was best for this task.

MIXED VACCINATION STRATEGY IN SIRS EPIDEMIC MODEL WITH SEASONAL VARIABILITY ON INFECTION

In many diseases seasonal fluctuations are observed. SIRS epidemic model with seasonal varying contact rate and mixed vaccination strategy (including first vaccination and pulse vaccination strategy) is investigated. The effects of the variation of dependent on the season of the contact rate and the vaccination strategy to eradicate infectious diseases are studied and discussed. A threshold for a disease to be extinct or endemic is established. The existence and global asymptotic stability of disease-free periodic solution and the permanence of the disease are illustrated. Finally, our theoretical results are confirmed by numerical simulations.

Time series analysis of dengue incidence in Guadeloupe, French West Indies: forecasting models using climate variables as predictors

Background

During the last decades, dengue viruses have spread throughout the Americas region, with an increase in the number of severe forms of dengue. The surveillance system in Guadeloupe (French West Indies) is currently operational for the detection of early outbreaks of dengue. The goal of the study was to improve this surveillance system by assessing a modelling tool to predict the occurrence of dengue epidemics few months ahead and thus to help an efficient dengue control.

Methods

The Box-Jenkins approach allowed us to fit a Seasonal Autoregressive Integrated Moving Average (SARIMA) model of dengue incidence from 2000 to 2006 using clinical suspected cases. Then, this model was used for calculating dengue incidence for the year 2007 compared with observed data, using three different approaches: 1 year-ahead, 3 months-ahead and 1 month-ahead. Finally, we assessed the impact of meteorological variables (rainfall, temperature and relative humidity) on the prediction of dengue incidence and outbreaks, incorporating them in the model fitting the best.

Results

The 3 months-ahead approach was the most appropriate for an effective and operational public health response, and the most accurate (Root Mean Square Error, RMSE = 0.85). Relative humidity at lag-7 weeks, minimum temperature at lag-5 weeks and average temperature at lag-11 weeks were variables the most positively correlated to dengue incidence in Guadeloupe, meanwhile rainfall was not. The predictive power of SARIMA models was enhanced by the inclusion of climatic variables as external regressors to forecast the year 2007. Temperature significantly affected the model for better dengue incidence forecasting (p-value = 0.03 for minimum temperature lag-5, p-value = 0.02 for average temperature lag-11) but not humidity. Minimum temperature at lag-5 weeks was the best climatic variable for predicting dengue outbreaks (RMSE = 0.72).

Conclusion

Temperature improves dengue outbreaks forecasts better than humidity and rainfall. SARIMA models using climatic data as independent variables could be easily incorporated into an early (3 months-ahead) and reliably monitoring system of dengue outbreaks. This approach which is practicable for a surveillance system has public health implications in helping the prediction of dengue epidemic and therefore the timely appropriate and efficient implementation of prevention activities.

[Increase of entomological indices during the pre-epidemic period of dengue in Ben Tre, South Vietnam]

Dengue has emerged in Vietnam 50 years ago and since has become endemo-epidemic throughout the whole country. Each year, major epidemics of dengue fever (DF) and dengue hemorrhagic fever (DHF) hit South Vietnam during the rainy season, causing significant morbidity and mortality, especially among young children. The only preventive measure is vector control, but it is often implemented too late or indiscriminately. The aim of this study was to investigate, in the pre-epidemic stage, the existence of significant changes in vector indices, which will predict DF/DHF outbreaks. We conducted a descriptive transversal study, repeated once a month for four months (March to June) in the village of Locthuan (province Ben Tre) in the Mekong's delta. Adult mosquitoes were caught in 30 houses, and larvae were collected in water holding containers of 50 houses. The houses were randomly selected. Vector densities were calculated according to the indices recommended by WHO. Virological analysis was carried out on lots of female Aedes and larvae in order to determine viral infection rates. Catches of adult mosquitoes collected 496 specimens including 329 Aedes, 139 Culex and 28 Anopheles. Aedes aegypti was present in 63% of visited homes that is an average density of 1.8 mosquitoes per house. The increase in imaginal indices during the 4 months was not significant. The survey of breeding sites of Ae. aegypti identified 1292 water containers in which 71,569 larval specimens were collected. The values of house index, container index [CI] and Breteau index [BI] increased each month, the latter from 166 to 442. This increase was significant for CI and BI. Breeding sites were mostly intra-home, mainly consisting of large and small ceramic jars. Larval density of Ae. aegypti in the containers also increased significantly over the 4 months. It was correlated with the lack of cover and predators such as Mesocyclops spp., Micronecta spp. and larvivorous fishes. Cultivation of 15 pools of 10 adult females and 29 pools of larvae (ie 1088 specimens) of Ae. aegypti failed to isolate dengue virus. The high Stegomyia indices measured in this South Vietnamese village and their increase before the rainy season reflect a situation at high risk of epidemics but cannot predict the occurrence of an outbreak in the absence of virus isolation from mosquitoes. They justify conducting an integrated vector control throughout the year.

Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand

In recent years, dengue has become a major international public health concern. In Thailand it is also an important concern as several dengue outbreaks were reported in last decade. This paper presents a GIS approach to analyze the spatial and temporal dynamics of dengue epidemics. The major objective of this study was to examine spatial diffusion patterns and hotspot identification for reported dengue cases. Geospatial diffusion pattern of the 2007 dengue outbreak was investigated. Map of daily cases was generated for the 153 days of the outbreak. Epidemiological data from Chachoengsao province, Thailand (reported dengue cases for the years 1999-2007) was used for this study. To analyze the dynamic space-time pattern of dengue outbreaks, all cases were positioned in space at a village level. After a general statistical analysis (by gender and age group), data was subsequently analyzed for temporal patterns and correlation with climatic data (especially rainfall), spatial patterns and cluster analysis, and spatio-temporal patterns of hotspots during epidemics. The results revealed spatial diffusion patterns during the years 1999-2007 representing spatially clustered patterns with significant differences by village. Villages on the urban fringe reported higher incidences. The space and time of the cases showed outbreak movement and spread patterns that could be related to entomologic and epidemiologic factors. The hotspots showed the spatial trend of dengue diffusion. This study presents useful information related to the dengue outbreak patterns in space and time and may help public health departments to plan strategies to control the spread of disease. The methodology is general for space-time analysis and can be applied for other infectious diseases as well.

How important is vertical transmission in mosquitoes for the persistence of dengue? Insights from a mathematical model

In many regions dengue incidence fluctuates seasonally with few if any infections reported in unfavourable periods. It has been hypothesized that vertical transmission within the mosquito population allows the virus to persist at these times. A review of the literature shows that vertical infection efficiencies are 1-4%. Using a mathematical model we argue that at these infection rates vertical transmission is not an important factor for long term virus persistence. In endemic situations, increases in reproductive number, half-life and persistence times of the disease only become significant when vertical infection efficiency exceeds 20-30%. In epidemic situations vertical infection accelerates the course of the outbreak and may actually reduce persistence time. These results stem from the fact that the mosquito life-cycle is relatively rapid and vertically acquired infections are multiplicatively diluted with every generation. When the efficiency of vertical infection is as low as reported from empirical studies, the virus is rapidly lost unless there is regular amplification in the human population. Processes such as asymptomatic human dengue cases are therefore more likely to be important in persistence than transmission within the vector population. The empirical data are not, however, unequivocal and we identify several areas of research that would further clarify the role of vertical transmission in the epidemiology of dengue.

What does dengue disease surveillance contribute to predicting and detecting outbreaks and describing trends?

OBJECTIVE

To review the evidence on the application of tools for dengue outbreak prediction/detection and trend monitoring in passive and active disease surveillance systems in order to develop recommendations for endemic countries and identify research needs.

METHOD

Systematic review of literature in the Cochrane Database of Systematic Reviews, PubMed, EMBASE, Lilacs, WHO library database, manual reference search and grey literature. Two reviewers independently applied pre-defined inclusion and exclusion criteria and assessed the level of evidence. Studies describing the outcome of dengue disease surveillance with respect to trend monitoring and outbreak prediction/detection based on empirical data were included.

RESULTS

Twenty-four studies (of 1804 references) met the eligibility criteria. Different indicators and their respective threshold values were identified as potential triggers for outbreak alerts through retrospective analysis of data from passive and/or active surveillance systems. Some indicators are potentially useful for predicting imminent outbreaks in the low transmission season and others for detecting outbreaks at an early stage. However, the information collected is mainly retrospective and often site-specific and appropriate levels of sensitivity and specificity of the outbreak indicators/triggers could not be determined. Retrospective and prospective virus surveillance studies were not conclusive regarding the question of whether a newly introduced serotype is an outbreak predictor, but contributed additional indicators for outbreak prediction/detection. Under-reporting was a major concern. Taking cost and feasibility issues into account, it remains an open question whether dengue surveillance should be passive (based on routine reporting) or active (based on more costly sentinel or other active population based surveillance systems). Adding active surveillance elements to a well-functioning passive surveillance system improves sensitivity; adding laboratory elements to the system improves specificity.

CONCLUSIONS

In view of the lack of evidence about the most feasible and sustainable surveillance system in a country context, countries could use a stepwise approach to locally adapt their passive routine surveillance system into an improved combined active/passive surveillance approach. Prospective studies are needed to better define the most appropriate dengue surveillance system and trigger for dengue emergency response.

The influence of immune cross-reaction on phase structure in resonant solutions of a multi-strain seasonal SIR model

Resonance in seasonally forced SIR epidemiological models may lead to stable solutions in which the epidemic period is an integer multiple of the forcing period. We examine the influence of immune cross-protection and cross-enhancement on the epidemic phase relationship of resonance solutions in an annually forced two-strain SIR model. Solutions with epidemics of the two strains in-phase commonly occur for wide ranges of cross-reaction intensity. Solutions with epidemics out-of-phase are less common and limited to narrow ranges of cross-reaction intensity. This is broadly as predicted by the two natural periods of the system. The natural period corresponding to out-of-phase solutions is sensitive to changes in the cross-reaction parameter but the natural period corresponding to in-phase solutions is constant. Bifurcation analysis indicates that the stability of in-phase orbits is controlled by pitchfork and period doubling bifurcations while out-of-phase orbits may also be influenced by Andronov-Hopf bifurcations. In order to develop an intuitive understanding of the epidemiological factors governing the occurrence of different solutions we consider how the susceptible, infected and removed components of the system must interact to form a stable solution. This shows that the impact of cross-reaction is moderated by in-phase structures but amplified by out-of-phase structures. Although the average infection rate over long time periods is not affected by phase structure, this analysis indicates that in-phase epidemic patterns are likely to be more consistent and thus allow more effective health care management.

Dengue antibody prevalence in German travelers

We studied 2,259 German citizens after they returned from dengue-endemic countries from 1996 to 2004. Serotype-specific dengue antibodies indicated acute infections in 51 (4.7%) travelers with recent fever and 13 (1.1%) travelers with no recent fever, depending largely on destination and epidemic activity in the countries visited.

Dengue and dengue hemorrhagic fever, Brazil, 1981-2002

Brazil has experienced an increase in dengue disease severity in the past 5 years.

In the last 5 years, Brazil has accounted for ≈70% of reported dengue fever cases in the Americas. We analyzed trends of dengue and dengue hemorrhagic fever (DHF) from the early 1980s to 2002 by using surveillance data from the Brazilian Ministry of Health. Two distinct epidemiologic patterns for dengue were observed: localized epidemics (1986–1993), and endemic and epidemic virus circulation countrywide (1994–2002). Currently, serotypes 1, 2, and 3 cocirculate in 22 of 27 states. Dengue and DHF affected mainly adults; however, an increase in occurrence of DHF among children has been recently detected in northern Brazil, which suggests a shift in the occurrence of severe disease to younger age groups. In 2002, hospitalizations increased, which points out the change in disease severity compared to that seen in the 1990s. We describe the epidemiology of dengue in Brazil, characterizing the changing patterns of it and DHF during the last 20 years.

Global climate change and the emergence/re-emergence of infectious diseases

Variation in the incidence of vector-borne diseases is associated with extreme weather events and annual changes in weather conditions. Moreover, it is assumed that global warming might lead to an increase of infectious disease outbreaks. While a number of reports link disease outbreaks to single weather events, the El Niño/Southern Oscillation and other large-scale climate fluctuations, no report unequivocally associates vector-borne diseases with increased temperature and the environmental changes expected to accompany it. The complexity of not yet fully understood pathogen transmission dynamics with numerous variables might be an explanation of the problems in assessing the risk factors.