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Dengue fever is a common mosquito-borne viral infectious disease in the world and is widely spread, especially in tropical and subtropical regions. At this moment, one of the best ways to fight the disease is to prevent mosquito bites. In this study, we present a mathematical model that carefully considers personal protection for humans. It is an epidemiological model that translates the dengue disease through a system of differential ordinary equations which takes in consideration the dynamics of the disease between human and mosquito populations. This model incorporates a parameter that simulates personal protection measures, namely insect repellent, special clothes, or bed nets, and a parameter that asserts the effectiveness of public awareness to the importance of using personal protective equipment. In 2012 there was a dengue disease outbreak in Madeira Island, in Portugal, and this study not only tries to predict what could happen if a second outbreak occurs, where it is considered that there are two serotypes of Dengue disease, but also tries to predict the effects and the importance of taking personal protection measures. The results show that the level of personal measures and the time that people are compelled to use them have a significant impact to prevent dengue disease.

Plasma leakage is a hallmark process in dengue viral (DENV) infection that occurs due to the loss of vascular integrity in endothelial cells. Endoglin (ENG) and Syndecan-1 (SDC-1) are released by activated endothelial cells; however, the complete dynamics of its expression at the gene and protein levels during the course of DENV infection remains unknown. In the present study, we quantified the mRNA and soluble protein levels of ENG and SDC-1 in dengue cases during febrile, defervescence, and convalescence stages in Dengue without Warning Sign (DWOW-15), Dengue with Warning Sign (DWW-22), and Severe Dengue cases (SD-10) compared to nondengue Other Febrile Illness (OFI-10) and healthy control (HC-8). Respective protein and mRNA levels along with clinical characters were further analyzed for their efficacy in predicting disease outcomes using Support Vector Machine (SVM). We observed a steady and significant (P ≤ 0.01) increase in the levels of protein and mRNA of both the ENG and SDC-1 towards defervescence which is considered a critical phase in both severe and non-severe dengue cases. Importantly during the critical phase, the levels were significantly higher (P ≤ 0.001) in SD cases compared to DWW, DWOW, and OFI controls. However, at the time of admission (febrile), no such significant changes were observed within dengue, OFI, and healthy controls. SVM analysis revealed that the serum levels of ENG and SDC-1 along with other clinical symptoms could predict the disease severity with 100% accuracy. Based on the results we have proposed a mechanism on how ENG and SDC-1 could be involved in vascular dysfunction rather than just being a biomarker.

Objetivo: analisar a incidência de casos notificados de dengue em Montes Claros. Método: estudo descritivo, exploratório, documental, retrospectivo, transversal, quantitativo, realizado no SINAN online da Secretaria Municipal de Saúde. A amostra foi constituí­da por 10.879 pacientes notificados entre 2017 e 2019. O tratamento dos dados se deu por epidemiologia descritiva simples. Resultados: a incidência de casos em 2017 foi baixa, quando comparada aos anos subseqüentes. Em 2018, o aumento percentual em relação a 2017 foi de 362,3%. Em 2019, houve um aumento exponencial na incidência, de modo a atingir 912,8% em relação a 2018 e 4.592,3% em relação a 2017. Ainda, houve aumento dos casos confirmados laboratorialmente como positivos em 2019 (3,8%). Conclusão: a incidência de casos notificados para a doença da dengue mostrou-se alta em 2019 e progressivo com o tempo. O aumento sintomático dos casos mostrou-se bem altos, devendo-se intensificar o acompanhamento desses pacientes.

Dengue disease is found worldwide and has been spreading exponentially in the past decades. Severe forms affect about 2% of patients, sometimes leading to organ failure. Recent studies have shown that fulminant dengue myocarditis is more common than previously thought, and fatal cases have been reported. The treatment of fulminant dengue myocarditis remains supportive and is very challenging in cases of cardiogenic shock. Here, we describe four cases presenting with fulminant dengue myocarditis treated with extracorporeal membrane oxygenation, among two survived.

Introduccion: dengue, enfermedad viral endemica-epidemica, problema de salud publica internacional en terminos de morbilidad y mortalidad, transmitida por el mosquito Aedes aegypti . Objetivo: confirmar la presencia de la enfermedad del dengue durante todo el ano en una region costera del Ecuador. Metodologia: estudio retrospectivo, observacional, de casos de dengue atendidos en el Hospital Liborio Panchana de la ciudad Santa Elena, Ecuador entre enero y noviembre de 2012. Resultados: en el 2012 se encontraron 29 pacientes cuyo diagnostico inicial fue sospecha y posterior confirmacion de dengue. 18 (62.1%) pertenecientes al sexo femenino y 11 (37.9%) al sexo masculino. La edad con mas prevalencia de diagnostico fue los 19 anos con 13.8% (n=4), de los cuales, tres fueron mujeres y uno varon. Todos los pacientes eran de la Provincia de Santa Elena, en la costa ecuatoriana. El 82.8%(n= 24) son del Canton de Santa Elena, 10.3% (n=3) del Canton Libertad y el 6.9% (n=2) del Canton Salinas. Un 75.86% (n=27) corresponden a enfermedad de dengue sin signos de alarma, un caso de dengue con signos de alarma y uno como dengue grave. No se presentaron fallecidos por esta patologia. Se presentaron casos de dengue desde la semana epidemiologica numero 4 [4 al 10 de marzo] a la semana numero 43 [21 al 27 de octubre], el mayor porcentaje 44.8% (n=18) se presentaron en la semana numero 36 [2 al 8 de septiembre]. Conclusion: el dengue ya no es una enfermedad estacional, se presenta durante todo el ano, debido a que su transmision es multifactorial. Rev.cienc.biomed. 2015;6(1):79- 84 PALABRAS CLAVE Dengue; Medicina tropical; Infecciones por flavivirus. SUMMARY Introduction: Dengue, a viral and endemic-epidemic disease. International public health problem in terms of morbidity and mortality, transmitted by the Aedes aegypti . Objective: To confirm the presence of the Dengue disease during all the year in a coast region of Ecuador. Methods: Retrospective and observational study of dengue cases who attended to the Hospital Liborio Panchana in Santa Elena, Ecuador since January to November in 2012. Results: In 2012, 29 patients with initial diagnosis of suspicious dengue were found, and it was confirmed afterwards. 18 (62.1%) were women and 11 (37.9%) were men. The most prevalent age for diagnosis was 19 years with 13.8% (n=4), of which, three were women and one man. All patients were from the province of Santa Elena, in the Ecuatorian Caribbean. 82.8%(n= 24) were from Canton de Santa Elena, 10.3% (n=3) from Canton de la Libertad and 6.9% (n=2) were from Canton Salinas. 75.86% (n=27) had dengue without warning signs, one case of dengue with warning signs and one case had severe dengue. There were not deaths for this disease. Cases of dengue were presented since the epidemiological week number 4 [4-10 of March] to the week number 43 [21 to 27 in October], the higher percentage 44.8% (n=18) was presented in the week number 36 [2 to 8 of September]. Conclusion: The dengue already is not a seasonal disease and its presentation changes during all the year due to the transmission is multifactorial Rev.cienc.biomed. 2015;6(1):79-84 KEYWORDS Dengue; Tropical medicine; Flavivirus infections.

Dengue disease is one of the most dangerous diseases and it is commonly found in Bandung, one of the most populous cities in Indonesia. Some patients who need more intensive care at hospital certai...

Dengue disease is one of the major health concerns in Indonesia because of its high number of cases. There is no specific treatment to cure people infected by this mosquito-borne disease and preven...

Background:Dengue is an acute, potentially fatal viral infection that can culminate in dengue hemorrhagic fever and dengue shock syndrome. The present study was conducted to assess the correlation between platelet counts and serological markers of dengue infection. Materials and Methods: The present study was conducted among 120 patients suspected of dengue fever. Serum samples were collected from suspected dengue fever patients. The samples were tested for NS1 antigen, IgM, and IgG antibodies using the ICT test kit. The platelet count was recorded in dengue parameter-positive and -negative cases. Results: Out of 120 patients, males were 70 and females were 50. NS1 only was detected in 62, IgM only in 20, IgG only in 14, NS1 and IgM only in 10, NS1 and IgG only in 8 and IgM and IgG only in 6 cases. 62 patients that were positive for NS 1, thrombocytopenia was seen in 48, 12 out of 20 IgM positive patients, 10 out of 14 IgG positive patients, 6 out of 10 NS1 and IgM only positive patients, 4 out of 8 NS1 and IgG only positive patients and 2 out of 6 IgM and IgG only positive patients were detected. The difference was significant (P< 0.05). Conclusion:In fever, thrombocytopenia is observed more uniformly in subjects positive for dengue. WheneverIgM is detected as compared to NS1 and can be used asan indicator to reduce the complication of dengue disease. The use of NS1 in the diagnosis of dengue increases the detection rate significantly.

In this paper, a mathematical model of dengue incorporating two sub-models that: describes the linked dynamics between predator-prey of mosquitoes at the larval stage, and describes the dengue spread between humans and adult mosquitoes, is formulated to simulate the dynamics of dengue spread. The effect of predator-prey dynamics in controlling the dengue disease at the larval stage of mosquito populations is investigated. Stability analysis of the equilibrium points are carried out. Numerical simulations results indicate that the use of predator-prey dynamics of mosquitoes at the larval stage as biological control agents for controlling the larval stage of dengue mosquito assists in combating dengue virus contagion.

Dengue disease is caused by an infected mosquito bite and manifests in different clinical symptoms. The complexity of the pathogenesis of dengue virus and the limitations of biological knowledge have been barriers to completely understanding the progress of this disease. To address this concern, we developed a mathematical model of the immune response to eliminate dengue virus. The model considered both cellular and humoral immune responses, and we evaluated their contributions to the clearance of dengue virus.We also performed global sensitivity analysis and parameters estimation using clinical data. We found the global stability for virus-free equilibrium and for the virus-presence equilibrium, we concluded that to avoid oscillations in the model and to control the viral load, a strong proliferation of cytotoxic cells must prevail. However, if there exists a weak proliferation of cytotoxic cells, the way to avoid instabilities is to either inhibit the differentiation of T-CD4+ helper cells in Th1 cells or increase the proliferation of B cells.

Dengue is an infectious disease that severe health problems even death. The number of infections has been increased significantly worldwide. Dengue disease is transmitted through Aedes Aegypti vectors. Identifying high-risk areas is vital to control disease transmission. Population density is a crucial risk factor that can accelerate dengue transmission. The cities in general have higher population density than districts. Therefore, the modeling dengue risk is critical to take into account the district and city levels information. It can be done by developing multilevel models as a representation of the hierarchical models. The models were estimated by means of integrated nested Laplace approximation (INLA). We found there is a high different regression coefficients between district and city levels. Population density has a high positive impact on the city level.

It is eminent that the epidemiological patterns of dengue are threatening for both the global economy and human health. The experts in the field are always in search to have better mathematician models in order to understand the transmission dynamics of epidemics models and to suggest possible control or the minimization of the infection from the community. In this research, we construct a new fractional-order system for dengue infection with carrier and partially immune classes to visualize the intricate dynamics of dengue. By using the basics of fractional theory, we determine the fundamental results of the proposed fractional-order dengue model. We obtain the basic reproduction number $R_0$ by next generation method and present the results based on it. The stability results are established for the infection-free state of the system. Moreover, sensitivity of $R_0$ is analyzed through partial rank correlation coefficient(PRCC) method to show the importance of different parameters in $R_0$. The influence of different input factors is shown on the output of basic reproduction number $R_0$ numerically. Our result showed that the threshold parameter $R_0$ can be decreased by increasing vaccination and treatment in the system. Finally, we illustrate the solution of the suggested dengue system through a numerical scheme to notice the influence of the fractional-order $\vartheta$ on the system. We observed that the fractional-order dynamics can explain the complex system of dengue infection more precisely and accurately rather than the integer-order dynamics. In addition, we noticed that the index of memory and biting rate of vector can play a significant part in the prevention of the infection.

Dengue disease has serious health and socio-economic consequences. Mapping its occurrence at a fine spatiotemporal scale is a crucial element in the preparation of an early warning system for the prevention and control of dengue and other viral diseases. This paper presents a Bayesian spatiotemporal random effects (pure) model of relative dengue disease risk estimated by integrated nested Laplace approximation. Continuous isopleth mapping based on inverse distance weighting is applied to visualize the disease's geographical evolution. The model is applied to data for 30 districts in the city of Bandung, Indonesia, for the period January 2009 to December 2016. We compared the Poisson and the negative binomial distributions for the number of dengue cases, both combined with a model which included structured and unstructured spatial and temporal random effects and their interactions. Using several Bayesian and classical model performance criteria and stepwise backward selection, we chose the negative binomial distribution and the temporal model with spatiotemporal interaction for forecasting. The estimation results show that the relative risk decreased generally from 2014. However, it consistently increased in the north-western districts because of environmental and socio-economic conditions. We also found that every district has a different temporal pattern, indicating that district characteristics influence the temporal variation across space.

espanolEl trabajo de investigacion presenta un modelo computacional por Automatas Celulares (Cell-DEVS) aplicado a la transmision de la enfermedad Dengue, propagada por mosquitos hacia una poblacion susceptible.Este modelo tendra un enfoque de multinivel para poder considerar interacciones exogenas como temperatura. Esto permitira poder visualizar los puntos criticos donde la reproduccion de mosquitos es mucho mayor, y asi realizar estrategias de control. EnglishThe research paper presents a computational model by Cellular Automata (Cell-DEVS) applied to the transmission of Dengue disease, spread by mosquitoes to a susceptible population. This model will have a multilevel approach to consider exogenous interactions such as temperature. This will allow to be able to visualize the critical points where the mosquito reproduction is much greater, and thus carry out control strategies.

Dengue disease is caused by four serotypes of the dengue virus: DEN-1, DEN-2, DEN-3, and DEN-4. The chimeric yellow fever dengue tetravalent dengue vaccine (CYD-TDV) is a vaccine currently used in Thailand. This research investigates what the optimal control is when only individuals having documented past dengue infection history are vaccinated. This is the present practice in Thailand and is the latest recommendation of the WHO. The model used is the Susceptible-Infected-Recovered (SIR) model in series configuration for the human population and the Susceptible-Infected (SI) model for the vector population. Both dynamical models for the two populations were recast as optimal control problems with two optimal control parameters. The analysis showed that the equilibrium states were locally asymptotically stable. The numerical solution of the control systems and conclusions are presented.

Dengue is a very common disease typically found in a widespread hot region. It is a deadly disease caused by female Aedes mosquitoes. Patients who have diagnosed with different types of dengue needed different types of treatment. Many experts are experimenting to recognize and exploring some of the new features on dengue disease from last few years. In this research paper, we have predicted dengue fever by implementing various machine learning algorithms such as two boosted decision tree, two-class Bayes point machine, multiclass decision forest, and boosted decision tree regression in a dataset. This dataset is collected from our generated survey of different people who are currently affected by dengue fever or already suffered from dengue fever. We also used tenfold cross-validation to estimate the performance of our machine learning model. We have also used Azure machine learning studio to predict and evaluate data, and we also compare the performance of all techniques that we have used. We also showed the accuracy of all the classification and regression technique. This research paper is the first unique paper based on Bangladesh region which will be used to detecting the dengue fever with 95% accuracy. People will be aware of this dangerous disease and can take necessary actions.

Dengue is a mosquito borne viral disease which occurs frequently affecting in large numbers of people in the tropical and sub-tropical regions. The disease requires medical treatment and may turn out to be fatal if not treated properly, or a delay in treatment is reported. Therefore, there is a need for the early prediction of the disease so that treatment is being availaible as and when required. In this paper, we intend to study, implement and review the performance of various prediction techniques coupled with bioinformatics for the early prediction of Dengue disease. The primary objective of the paper is to analyze and compare the performance of the soft computing prediction techniques, the advantages and the issues associated with them on the Dengue datasets. The paper would be helpful in motivating the researchers and various government institutions to predict the number of likely Dengue cases, ramp up the medical and healthcare facilities and put various programs and plans into being. The applied approach may be used in some other medical diseases.

Dengue is among the most important infectious diseases in the world. The main contribution of our paper is to present a mixed system of partial and ordinary differential equations. This combined model is a generalization of the two presented mathematical models (A. L. de Araujo, J. L. Boldrini and B. M. Calsavara, An analysis of a mathematical model describing the geographic spread of dengue disease, J. Math. Anal. Appl. 444 (2016) 298–325) and (L. Cai, X. Li, N. Tuncer, M. Martcheva and A. A. Lashari, Optimal control of a malaria model with asymptomatic class and superinfection, Math. Biosci. 288 (2017) 94–108), describing the geographic spread of dengue disease. Our model has the ability to consider the possibility of asymptomatic infection, which leads to investigate the effect of dengue asymptomatic individuals on disease dynamics and to go into the possibility of superinfection of asymptomatic individuals. In the light of considering these factors, as well as the movements of human and mature female mosquitoes, more realistic modeling of dengue disease can be achieved. We present a mathematical analysis and show the global existence of a unique non-negative solution to this model and then establish ways to control dengue disease using numerical simulations and sensitivity analysis of model parameters (which are related to the contact rates and death rate of winged mosquitoes). To show different biological behaviors, we provide several numerical results, showing the role of parameters in controlling dengue disease transmission. From our numerical simulations, it can also be concluded that local control of dengue transmission can be done at a lower cost.

Dengue is an acute viral infection with potential deadly complications transmitted by both Aedes aegypti and Aedes albopictus female mosquitoes and is said to be a severe and increasing public health problem with 2.5 billion individuals at risk. WHO currently estimates that with around 24,000 fatalities, 50 million cases of dengue disease may occur globally once a year. In India, too, the condition is getting worse as morbidity and mortality rise. Several policies have been adopted to decrease dengue burden through applied research, field-based training, and capacity building among appropriate regional and national public health stakeholders.

Introduction. Limited awareness and nonsystematized health education programmes have contributed adversely to the increase in dengue incidence at schools due to limited attention which has positively contributed to the increase in vector receptivity. The current study was conducted to evaluate the existing level of awareness of dengue infection among a selected group of school children and to assess the effectiveness of dengue awareness programmes to improve the existing knowledge and preventive practices on dengue. Methods. A cohort of 2,194 students (13–15 years old) from 10 schools at Kelaniya educational zone, Gampaha District, Western Province of Sri Lanka, was enrolled for the current study, which was conducted during 2015-2016. A self-administered questionnaire, comprising 20 questions, was used to evaluate the present knowledge of the students on various aspects of dengue. A structured awareness programme (2 hours) was conducted for students, followed by a reassessment. General Linear Model (GLM) and chi-square test of independence were used to investigate the variations in knowledge levels. Results. The majority of students were characterized by “Good” (46.31%, n= 1016) and “Moderate” (42.62%, n= 935) awareness, while only 2.92% (n= 64) of students fell into the “Excellent” (>80%) category prior to the awareness programme. Even though, existing knowledge of students about dengue ranged between “Moderate” and “Good” categories, awareness of “symptoms & patient care” and “control & prevention practices” were limited. After the programme, the awareness level reached the “Excellent” (41.84%, n=918) level indicating a significant increase by 38.92%, according to the chi-square test (p Conclusion. We recommend implementing school-based educational programmes in order to raise the awareness and to translate knowledge into sound practice to control dengue disease epidemics in these areas.

One of the congenital factors that affect the transmission of dengue disease is blood type. This study mainly aims to consider blood type O in humans and uses a differential equations system formul...

Objectives: Vector-borne diseases speedily infest the human population. The control techniques must be applied to such ailment and work swiftly. We proposed a compartmental model of dengue disease by incorporating the standard incidence relation between susceptible vectors and infected humans to see the effect of manageable parameters of the model on the basic reproduction number. Methods: We compute the basic reproduction number by using the next -generation matrix method to study the local and global stability of disease free and endemic equilibrium points along with sensitivity analysis of the model. Results: Numerical results are explored the global behaviourism of disease-free/endemic state for a choice of arbitrary initial conditions. Also, the biting rate of vector population has more influence on the basic reproduction number as compared the other parameters. Conclusion: In this paper, shows that controlling the route of transmission of this disease is very important if we plan to restrict the transmission potential.

Dengue fever is a gigantic general medical problem that causes mortality and severity worldwide. As of now, dengue fever is the most vital irresistible disease. Postponing recognition of dengue disease could cause dangerous difficulties and increase the death rate. In this way, exploring aims to build an exact model that could more likely identify the first signs and side effects of dengue fever and a commonsense framework for disease self-defense. These two techniques have been applied to promptly notify patients, whether they are suspected of having dengue fever or not specifically the data extraction method. Dengue detection rules are developed after meeting a professional, and these rules will be applied in an authority system using a flexible basis. Regardless of before applying the removed standards, the data mining gadget attempted the accuracy of the rules. Preliminary results show that data extraction has improved accuracy and produces a solid result for self-investigation of dengue marks.

Dengue disease is the most common mosquito-borne viral diseases in the world, especially in Bandung, Indonesia. Juvenile age group is important to be considered in dengue management since the number of cases in this age group is significantly increasing year by year especially in Bandung, West Java, Indonesia. Another concern to pay special attention to this age group is because dengue infection among juveniles shall hinder their growing process and influence their academic achievement at schools. Apart from that, it will lower parents’ productivity as they have to be absent from work, and they have to spend expenses for medication. One of the effective and efficient strategies to prevent the transmission is by analysing the spatial and temporal distribution of dengue disease incidence and its trend. In this study, the random effect Generalized Linear Mixed Model (GLMM) is applied and numerical Bayesian method through Integrated Nested Laplace Approximation (INLA) is used. The models are applied to dengue disease incidence in year 2013 for the juvenile group in Bandung.

Dengue, an illness caused by four serotypes of the dengue virus is most prevalent in Asia and Latin America. Its manifestation varies from asymptomatic cases to severe forms and even deaths and is a growing health concern. Despite attempts to curtail it, the infection continues to intensify in endemic areas and spread to new areas of the world. Development of a safe and effective vaccine and a potent antiviral drug is ongoing but so far there has been no breakthrough. Nonetheless, researchers are carrying out studies with newer vaccine candidates and antiviral moieties, and some of these hold promise for the future. The current article describes the recent patents targeted at dengue control.

Introduction: Analyzing dengue disease patterns from different parts of the world should help us formulate more evidence based treatment guidelines and appropriately allocate limited healthcare resources. Therefore, we described the disease characteristics of hospitalised pediatric patients with dengue infections from Sri Lanka during the 2017 dengue epidemic. Methods: Clinical and biochemical characteristics of pediatric dengue patients treated at a secondary care hospital in Sri Lanka from 1 June 2017 to 31 August 2017 were analyzed. Our findings were compared with previous pediatric dengue studies in Asia. Results: A total of 305 patients (number of males = 184(60%); mean age = 8.6 years) were analyzed. DF (Dengue Fever)—245 (80.3%), DHF (Dengue Hemorrhagic fever)—I:52 (17%), DHF—II:7 (2.3%), and DHF—III:1 (0.3%). Significant associations were found between DHF and abdominal symptoms/signs and overt bleeding manifestations ( P Conclusion: In Sri Lankan pediatric dengue patients, we found abdominal symptoms and signs, decreased WBC and platelet counts and bleeding manifestations were to be significantly associated with DHF. Liver enzyme derangement did not predict DHF. The time of onset of the critical phase was difficult to predict due to the considerable variations noted.

Acute kidney injury (AKI) is a serious and potentially lethal complication of dengue disease; however, the actual incidence in dengue patients in Vietnam is unknown. This study aimed to determine the prevalence, clinical and laboratory characteristics, and risk factors for AKI in adults with dengue. This is a multicenter, cross-sectional study conducted between January and December 2017 in 2,417 adult patients with dengue. Indicators of demographic, clinical, and laboratory findings were evaluated. The prevalence of dengue disease–associated AKI was 2.7% (64/2,417), and dengue patients with AKI had a higher mortality rate than patients without AKI (12.5% versus 0.6%; P < 0.01). Characteristics of AKI were more common in male gender (male: female was 6.1:1), a history of hypertension (7.8% versus 2.3%; P < 0.02), the situations of hemoconcentration (hematocrit > 44%; P < 0.008), hypovolemic shock (21.9% versus 6.8%; P < 0.01), organ failure (42.3% versus 2.3%; P < 0.01), or manifestations of myocarditis (20.3% versus 6.6%; P < 0.01), hyperbilirubinemia (28.7 ± 7.6 versus 12.0 ± 0.9; P < 0.01), elevated enzymes such as alanine aminotransferase (ALT) (407 ± 151 versus 113 ± 6; P < 0.01) and aspartate aminotransferase (AST) (891 ± 475 versus 172 ± 11; P < 0.01), and prolonged PT Prothrombin Time (s) (13.9 ± 4.6 versus 12.3 ± 1.5; P < 0.01). Independent risk factors for AKI by multivariate analysis were male gender (odds ratio [OR]: 43.6; 95% CI: 2.4–810), severe dengue classification (OR: 25.7; 95% CI: 2–333), and creatine kinase > 190 U/L (OR: 11.7; 95% CI: 1.1–122.4). The study results indicate a need to continue studying the association between AKI and mortality in dengue disease and the need for improved management of AKI with dengue.

Dengue is the transmission disease occurred by biting of infected Aedes aegypti and infected Aedes albopictus. The temperature of each area influences to the transmission of this disease. In this paper, we describe the transmission of this disease by using mathematical model. We separate the human population into susceptible, exposed, infectious and recovered classes. The vector population is divided into Aedes aegypti and Aedes albopictus. The mosquito population is separated into susceptible, exposed and infectious classes. We analyzed our mathematical model by finding the equilibrium points and determined the stability of each steady state. The numerical solutions are shown. The difference of temperatures is shown to describe the behaviors of human and vector populations. From the results, we will see that the temperature influences to the transmission of dengue disease.

Mosquito-borne disease such as dengue fever is a pervasive public health problem around the world and further investigation is needed to rectify the misunderstanding of the disease among communities. This requires a personalized information delivery, which will effectively fix the problem. The process of personalizing information requires several major steps: (i) determine the attributes which will be used to interpret a person, (ii) selects an algorithm which will accurately and efficiently classify the person according to the retrieved background information, and (iii) recommends the correct information to rectify the particular misunderstanding. This research paper considers the first two steps. First, data regarding the knowledge, attitudes and prevention practices are determined from the established literature where some variables give a significant impact on the predictive model. In the second step, five performed machine learning algorithms were tested for the classification task. The result indicates that the use of Support Vector Machine and Decision Tree algorithms provide the best performance in classifying the person’s understanding regarding the dengue fever.

Dengue disease encompasses various clinical manifestations including dengue fever (DF) and dengue hemorrhagic fever (DHF). In this article, we aimed to systematically review and analyze the association between different blood groups and severity of dengue. We searched nine databases for eligible papers reporting prevalence, distribution, and frequency of blood group type among dengue patients. Network meta-analysis using R software was used to analyze the data. Of a total of 63 reports screened, we included 10 studies with total sample size 1977 patients (1382 DF and 595 DHF). Blood group O was found to have the worst outcome with the highest risk of developing DF (P-score = 0.01) followed by group B (P-score = 0.34), group A (P-score = 0.64), and group AB (P-score = 1), respectively. Blood group O also had the worst outcome with highest risk of developing DHF (P-score = 0.1) followed by group B (P-score = 0.29), group A (P-score = 0.61), and group AB (P-score = 1), respectively. There was a significant increase (P-value

Background Approximately 70% of the global burden of dengue disease occurs on the Asian continent, where many large urban centres provide optimal environments for sustained endemic transmission and periodic epidemic cycles. Jakarta, the capital of Indonesia, is a densely populated megacity with hyperendemic dengue transmission. Characterization of the spatiotemporal distribution of dengue transmission intensity is of key importance for optimal implementation of novel control and prevention programmes, including vaccination. In this paper we use mathematical models to provide the first detailed description of spatial and temporal variability in dengue transmission intensity in Jakarta. Methodology/Principal findings We applied catalytic models in a Bayesian framework to age-stratified dengue case notification data to estimate dengue force of infection and reporting probabilities in 42 subdistricts of Jakarta. The model was fitted to yearly and average annual data covering a 10-year period between 2008 and 2017. We estimated a long-term average annual transmission intensity of 0.130 (95%CrI: 0.129–0.131) per year in Jakarta province, ranging from 0.090 (95%CrI: 0.077–0.103) to 0.164 (95%CrI: 0.153–0.174) across subdistricts. Annual average transmission intensity in Jakarta province during the 10-year period ranged from 0.012 (95%CrI: 0.011–0.013) in 2017 to 0.124 (95%CrI: 0.121–0.128) in 2016. Conclusions/Significance While the absolute number of dengue case notifications cannot be relied upon as a measure of endemicity, the age-distribution of reported dengue cases provides valuable insights into the underlying nature of transmission. Our estimates from yearly and average annual case notification data represent the first detailed estimates of dengue transmission intensity in Jakarta’s subdistricts. These will be important to consider when assessing the population-level impact and cost-effectiveness of potential control and prevention programmes in Jakarta province, such as the controlled release of Wolbachia-carrying mosquitoes and vaccination., Author summary Characterization of the spatiotemporal distribution of dengue transmission intensity, a key measure of population infection risk, can inform the optimal use and deployment of control and prevention programmes. Jakarta, the capital of Indonesia, is a large urban centre with hyperendemic dengue transmission. We fitted catalytic models to age-stratified dengue surveillance data reported in Jakarta’s subdistricts from 2008 to 2017. We estimated a long-term average annual transmission intensity of 0.130 (95%CrI: 0.129–0.131) per year in Jakarta province, which varied across subdistricts from 0.090 (95%CrI: 0.077–0.103) per year in Sawah Besar to 0.164 (95%CrI: 0.153–0.174) per year in Pasar Rebo. We observed significant spatiotemporal variation and clustering of transmission intensity in Jakarta. Our estimates obtained from the analysis of yearly and cumulative case-notification data reported between 2008 and 2017 represent the first detailed estimates of average dengue transmission intensity, which will be key to assess the potential impact of future control and prevention programmes in Jakarta province.

Introduction: Due to the shortcomings in the 1997-World Health Organisation (WHO) dengue case classification (DCC), a revised classification was proposed in 2009. This study was aimed to assess the clinical usefulness of the two classifications during a large dengue epidemic. Methodology: Clinical data of dengue patients admitted to selected units at National Hospital of Sri Lanka, Panadura Base Hospital and Nawaloka Hospital Colombo between June and August 2017 were collected prospectively. Cases were classified using the 1997 and 2009 WHO DCCs. Results: 1,878 patients [adult = 1,573 (83.8%)] were studied. Based on 1997-WHO-DCC-DF (Dengue Fever): 1,316 (70.1%), DHF (Dengue Haemorrhagic Fever) -1: 468 (24.9%), DHF-2: 86 (4.6%) and DHF-3: 8 (0.4%). Based on 2009-WHO-DCC–Dengue with warning signs (WS): 1647 (87.7%), Dengue without WS: 231 (12.3%) and severe dengue (SD): 41 (2.18%). A total of 1,088 (82.7%) DF and 559 (99.5%) DHF patients developed WS. Of those without WS, 228 (17.3%) were DF patients and 3 (0.5%) were DHF patients. Three (0.23%) DF and 38 (6.76%) DHF patients had SD. All SD patients had WS. The level of agreement between the two systems of classification was poor (Kappa = - 0.035, p < 0.001). Conclusions: The 2009-WHO-DCC was more useful than 1997-WHO-DCC in predicting dengue disease severity as few DF patients also had SD. Furthermore, the presence of WS identified patients with SD. However, the 2009-WHO-DCC may not suit the resource limited countries as WS are non-specific, and lack of diagnostic tests can result in case overload.

Dengue is a severe health problem. In recent decades, the number of infections has increased significantly worldwide, and approximately 70% of cases were found in Asia. To predict local mosquito distribution and estimate the local risk, a combination of ecological factors has been widely used. However, it is not easy to control the dengue transmission because the transmission is influenced by several ecological factors that might have complex interactions among the factors. In addition, ecological information is commonly limited. The elevation is an important weather proxy for the Aedes Aegypti life cycle because it is correlated to a variety of vital ecological factors, especially temperature. This study evaluated the effect of the elevation and identified high-low dengue risk provinces in Indonesia using the clustering Bayesian spatial hierarchical model. We found that the elevation's effect decreased for the provinces with an elevation higher than 289.44m.

OBJECTIVE To compare WHO's traditional (1997) and revised (2009) guidelines for dengue classification, using a large sample of patients of all ages with varying clinical conditions from a dengue-endemic area in Brazil. METHODS We compared 30 670 laboratory-confirmed dengue cases (1998-2012) using both WHO's dengue classification guidelines. Stereotype ordinal logistic regressions were used to analyse the association between patients' demographics and signs and symptoms related to dengue infection severity, as defined in the 1997 and 2009 guidelines. We then compared the degree of agreement in dengue classification of both guidelines. RESULTS Dengue signs and symptoms in patients were poorly correlated to disease severity as defined by both guidelines (Cramer's V test

Oesapa Health Center is a health center that has the highest number of dengue cases in Kupang City in 2019 and spread throughout the Puskesmas working area. Regional-based disease management analysis techniques using spatial analysis can be used to facilitate the determination of the DHF problem solving intervention program. Spatial analysis is used to determine the pattern of disease spread and potential areas of dengue transmission based on area. The purpose of this study was to determine the pattern of dengue delivery, the spread of dengue incidence based on population density, the House Index and Container Index and the buffer. This type of research is a descriptive survey with a case study design. The sampling technique used total sampling with 98 people. The results of spatial analysis using Quantum GIS show the distribution pattern of dengue incidence with Clustered pattern with NNI value of 0,60

CYD-TDV is a live, attenuated, tetravalent dengue vaccine licensed in 21 countries. We undertook a post-hoc analysis of the long-term efficacy of CYD-TDV during the surveillance expansion phase (SEP) of two Phase III studies (CYD14 in the Asia-Pacific region; CYD15 in Latin America). The SEP included approximately Year 5 and the entire Year 6 of follow-up after the first study injection. Vaccine efficacy against symptomatic virologically-confirmed dengue (VCD) was assessed by participant age (any age, ≥9,9, 2-5, and 6-8 years at the time of the first injection) and baseline dengue serostatus using a case-cohort framework. Baseline dengue serostatus was estimated by several methods including logistic regression-based multiple imputation (MI) to predict PRNT

Dengue is a disease caused by four types of related viruses transmitted by a mosquito, most commonly Aedes Aegypti. The disease is considered an alarming threat to the health of populations spanning millions of people living in tropical and subtropical areas of the globe where the mosquito thrives. A large number of studies have confirmed that the rise of dengue disease is positively correlated with climate and climatic conditions, specifically, humidity, temperature and precipitation levels. Many of these studies include quantitative models correlating climate variables with the incidence of dengue cases. The quantitative models invite the question: how well would we be able to predict future cases of the disease based on climate variables that are included in weather forecasts? To answer this question we conducted a study on Dengue Forecasting using machine learning, which utilizes climate and dengue data (available to data scientists by US government) to forecast future dengue epidemics. In this research we proposed a novel model twofold linear regression which out perform compare to all previous models. we achieve 19.81 mean absolute error which is minimum as compare to traditional machine learning techniques. Moreover, we have analyzed various neural network, support vector machine, random forest, boosted tree, XGBoost based predictive models and evaluate their performance against proposed method.

Dengue fever (DF) could develop into dengue haemorrhagic fever (DHF) with increased mortality rate. Since the clinical characteristics and pathogen are same in DF and DHF. It’s important to identify different molecular biomarkers to predict DHF patients from DF. We conducted a clinical plasma proteomics study using quantification (TMT)-based quantitative proteomics methodology to found the differential expressed protein in DF patients before they developed into DHF. In total 441 proteins were identified up or down regulated. There proteins are enriched in diverse biological processes such as proteasome pathway, Alanine, aspartate, and glutamate metabolism and arginine biosynthesis. Several proteins such as PLAT, LAMB2, and F9 were upregulated in only DF patients which developed into DHF cases, not in DF, compared with healthy-control. In another way, FGL1, MFAP4, GLUL, and VCAM1 were upregulated in both DHF and DF cases compare with healthy-control. RT-PCR and ELISA were used to validate these upregulated gene expression and protein level in 54 individuals. Results displayed the same pattern as proteomics analysis. All including PLAT, LAMB2, F9, VCAM1, FGL1, MFAP4, and GLUL could be considered as potential markers of predicting DHF since the levels of these proteins vary between DF and DHF. These new founding identified potential molecular biomarkers for future development in precision prediction of DHF in DF patients.

We studied the dynamics of dengue disease in two epidemic regions in Sri Lanka, the densely populated Colombo district representing the wet zone and the relatively less populated Batticaloa district representing the dry zone. Regional differences in disease dynamics were analysed against regional weather factors. Wavelets, Granger causality and regression methods were used. The difference between the dynamical features of these two regions may be explained by the differences in the climatic characteristics of the two regions. Wavelet analysis revealed that Colombo dengue incidence has 6 months periodicity while Batticaloa dengue incidence has 1 year periodicity. This is well explained by the dominant 6 months periodicity in Colombo rainfall and 1 year periodicity in Batticaloa rainfall. The association between dengue incidence and temperature was negative in dry Batticaloa and was insignificant in wet Colombo. Granger causality results indicated that rainfall, rainy days, relative humidity and wind speed can be used to predict Colombo dengue incidence while only rainfall and relative humidity were significant in Batticaloa. Negative binomial and linear regression models were used to identify the weather variables which best explain the variations in dengue incidence. Most recent available incidence data performed as best explanatory variables, outweighing the importance of past weather data. Therefore we recommend the health authorities to closely monitor the number of cases and to streamline recording procedures so that most recent data are available for early detection of epidemics. We also noted that epidemic responses to weather changes appear quickly in densely populated Colombo compared to less populated Batticaloa. The past dengue incidence and weather variables explain the dengue incidence better in Batticaloa than in Colombo and thus other exogenous factors such as population density and human mobility may be affecting Colombo dengue incidence.

This article describes how Dengue fever is a fatal and hazardous disease resulting from the bite of several species of the female mosquito (principally, Aedesaegypti). Symptoms of the dengue fever mimic those of a number of other infectious and/or mosquito-borne tropical diseases such as Viral flu, Chikungunya, and Zika fever. Yet, with dengue fever, human life can be more at risk due to severe depletion of blood platelets. Thus, early detection of dengue disease can ensure saving lives; furthermore, it can help in making a preventive move before the disease progresses to epidemic proportion. Hence, the target of this article is to propose a model for an early detection and precise diagnosis of dengue disease. In this article, three prevalent machine learning methodologies, including, Artificial Neural Network (ANN), Decision Tree (DT) and Naive Bayes (NB) are evaluated for designing a diagnostic model. The performance of these models is assessed utilizing available dengue datasets. Results comparing and contrasting performance of diagnostic models utilizing accuracy, sensitivity, specificity and error rate parameters showed that ANN-based diagnostic model appears to yield better performance measures over both the DT and NB models.

Our objective was to describe the risk of hospital admission for virologically confirmed dengue (VCD) and the risk of clinically severe hospitalized VCD occurring up to 4 years after the first dose (years 1 to 4) in three randomized clinical trials comparing tetravalent dengue vaccine with placebo.The relative risks (RR) for hospitalized VCD from first dose to year 4 were estimated by year and age-group in individual and combined studies.Overall, from Year 1 to Year 4, 233 and 228 participants had at least one episode of hospitalized VCD in the vaccinated (n = 22 603) and placebo (n = 11 301) groups, respectively (RR = 0.511, 95% CI 0.42-0.62). Among these, 48 and 47 cases, respectively, were classified as clinically severe. In children aged ≥9 years, 88 and 136 participants had at least one episode of hospitalized VCD in the vaccinated (n = 17 629) and placebo (n = 8821) groups, respectively (RR = 0.324; 95% CI 0.24-0.43). In vaccinated participants aged9 years, particularly in those aged 2-5 years, there were more hospitalized VCD cases compared with the control participants in Year 3 but not in Year 4. The overall RR in those aged9 years for Year 1 to Year 4 was 0.786 (95% CI 0.60-1.03), with a higher protective effect in the 6-8 year olds than in the 2-5 year olds.The overall benefit-risk remained positive in those aged ≥9 years up to year 4, although the protective effect was lower in years 3 and 4 than in years 1 and 2.

Licensing and decisions on public health use of a vaccine rely on a robust clinical development program that permits a risk-benefit assessment of the product in the target population. Studies undertaken early in clinical development, as well as well-designed pivotal trials, allow for this robust characterization. In 2012, WHO published guidelines on the quality, safety and efficacy of live attenuated dengue tetravalent vaccines. Subsequently, efficacy and longer-term follow-up data have become available from two Phase 3 trials of a dengue vaccine, conducted in parallel, and the vaccine was licensed in December 2015. The findings and interpretation of the results from these trials released both before and after licensure have highlighted key complexities for tetravalent dengue vaccines, including concerns vaccination could increase the incidence of dengue disease in certain subpopulations. This report summarizes clinical and regulatory points for consideration that may guide vaccine developers on some aspects of trial design and facilitate regulatory review to enable broader public health recommendations for second-generation dengue vaccines. Published version

Large numbers of machine learning approaches have been developed for analysis of medical data in recent years. These approaches have also proved their significance through accurate and earlier diagnosis of diseases. The objective of this work is to develop a diagnostic model for earlier diagnosis of dengue disease. Dengue fever is spread through the bite of the female mosquito (Aedes aegypti). The symptoms of this fever are similar to other fever such as that of Viral influenza, Chikungunya, Zika fever, and so on. However, in this fever, human life can be at risk due to severe depletion of blood platelets. Therefore, early diagnosis of dengue disease can help in protecting human lives by making a preventive move before it turns into an infectious disease. In this work, an effort is made to develop a PSO-ANN based diagnostic model for earlier diagnosis of dengue fever. In the proposed model, PSO technique is applied to optimize the weight and bias parameters of ANN method. Further, PSO optimized ANN approach is used to detect dengue patients. The effectiveness of the proposed model is evaluated based on accuracy, sensitivity, specificity, error rate and AUC parameters. The results of the proposed model have been compared with other existing approaches like ANN, DT, NB, and PSO. It is observed that the proposed diagnostic model is a proficient and powerful model for more accurate and earlier detection of dengue fever.

Epidemics of dengue fever in China were reported before 1940 and the outbreak of dengue fever in Guangdong province in 2014 is the most serious so far. The important question is what factors account for this serious outbreak, and how to evaluate the sensitivity of the multiple factors including weather variables and human actions on the dengue disease. Therefore, according to the relations among the temperature (daily mean temperature (DMT) and diurnal temperature range (DTR)), vector parameters and reproduction number we have proposed the analytical formula for the relative vector's capacity and effective reproduction number, and then we have the formula for the likelihood function by employing the generation interval-informed method. This allows us to estimate the unknown vector parameters by the maximum likelihood method and carry out the sensitivity analysis. The correlations between the density of mosquito vectors (the Breteau index (BI), the adult mosquito density) and the daily newly reported cases of four different districts of Guangzhou city have been studied by using the Pearson correlation and cross-correlation analyses. Our findings indicate that both the BI and the adult mosquito density are statistically significantly correlated with the daily newly reported cases, and the vector parameters are closely related to the DMT and DTR with relative complex relationships, which influence the effective reproduction number comprehensively. Moreover, the trend of the effective reproduction number is consistent with daily newly reported cases, which confirms the effectiveness of the government control measures. Sensitivity analysis results indicate that the temperature can be either an effective barrier or a facilitator of vector-borne diseases, and consequently weather variable may result in complexity of dengue disease control.

Dengue disease patients are increasing rapidly and actually dengue has recorded in every continent today according to World Health Organisation (WHO) record. By WHO report the number of dengue outbreak cases announced every year has expanded from 0.4 to 1.3 million during period of 1996 to 2005 and then it has reached to 2.2 to 3.2 million during year of 2010 to 2015 respectively. Consequently , it is fundamental to have a structure that can adequately perceive the pervasiveness of dengue outbreak in a large amount of specimens momentarily. At this critical moment, the capability of seven prominent machine learning systems was assessed for forecast of dengue outbreak. These methods are evaluated by eight miscellaneous performance parameters. LogitBoost ensemble model is reported as the topmost classification accuracy of 92% with sensitivity and specificity of 90 and 94 % respectively.

Dengue is a major health threat related to arbovirus and is endemic in more than 100 countries with an annual estimated above 390 million incidences of infection all around the world. During the period 1996-2015, a considerable increase in the number of dengue cases (more than 500%) was reported in India. Information about dengue disease burden, its prevalence, incidence and geographic distribution is critical in planning appropriate control measures against dengue fever. Till date, no specific treatment for dengue fever is available in any system of medicine, which can be accepted globally. Therefore, safe, cost-effective, and efficacious agents possessing anti-viral potential against dengue virus are needed to be searched in order to fight the dengue infection globally. The aim of the present review is to systematically revise the published research work available concerning the development and evaluation of some heterocyclic scaffolds in the management of dengue.

Fuzzy inference systems (FIS) have been widely used in medical diagnosis. FIS uses fuzzy reasoning in order to represent the knowledge of experts about certain problem in a systematic manner. In the last decade, Dengue fever has been considered as a severe disease in the western area of Saudi Arabia, where millions of pilgrims come from all over the world to perform their Hajj. FIS can be used to early diagnose this disease and reduce its severity. However, because of many number of factors associated with Dengue, using traditional FIS causes generating huge number of fuzzy rules, which leads to high complexity. In order to tackle this issue, this paper proposes a Hierarchical Fuzzy Inference system (HFIS) that can be used to diagnose Dengue fever. The proposed model is compared to one from the literature and shows more promising results from the perspective of simplicity and extendibility.