Dengue Model Research Articles

Performance of Hybrid GANN in Comparison with Other Standalone Models on Dengue Outbreak Prediction

Early prediction of diseases especially dengue fever in the case of Malaysia, is very crucial to enable health authorities to develop response strategies and context preventive intervention programs such as awareness campaigns for the high risk population before an outbreak occurs. Some of the deficiencies in dengue epidemiology are insufficient awareness on the parameter as well as the combination among them. Most of the studies on dengue prediction use standalone models which face problem of finding the appropriate parameter since they need to apply try and error approach. The aim of this paper is to conduct experiments for determining the best network structure that has effective variable and fitting parameters in predicting the spread of the dengue outbreak. Four model structures were designed in order to attain optimum prediction performance. The best model structure was selected as predicting model to solve the time series prediction of dengue. The result showed that neighboring location of dengue cases was very effective in predicting the dengue outbreak and it is proven that the hybrid Genetic Algorithm and Neural Network (GANN) model significantly outperforms standalone models namely regression and Neural Network (NN).

Optimal control approach to dengue reduction and prevention in Cali, Colombia

The aim of this paper is to propose optimal strategies for dengue reduction and prevention in Cali, Colombia. For this purpose, we consider two variants of a simple dengue transmission model, epidemic and endemic, each of which is amended with two control variables. These variables express feasible control actions to be taken by an external decision‐maker. First control variable stands for the insecticide spraying and thus targets to suppress the vector population. The second one expresses the protective measures (such as use of repellents, mosquito nets, and insecticide‐treated clothes) that are destined to reduce the number of contacts (bites) between female mosquitoes (principal dengue transmitters) and human individuals. We use the Pontryagin's maximum principle in order to derive the optimal strategies for dengue control and then perform the cost‐effectiveness analysis of these strategies in order to choose the most sustainable one in terms of cost–benefit relationship. Copyright © 2016 John Wiley & Sons, Ltd.

Projected Impact of Dengue Vaccination in Yucatán, Mexico.

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

The Role of Serotype Interactions and Seasonality in Dengue Model Selection and Control: Insights from a Pattern Matching Approach.

The epidemiology of dengue fever is characterized by highly seasonal, multi-annual fluctuations, and the irregular circulation of its four serotypes. It is believed that this behaviour arises from the interplay between environmental drivers and serotype interactions. The exact mechanism, however, is uncertain. Constraining mathematical models to patterns characteristic to dengue epidemiology offers a means for detecting such mechanisms. Here, we used a pattern-oriented modelling (POM) strategy to fit and assess a range of dengue models, driven by combinations of temporary cross protective-immunity, cross-enhancement, and seasonal forcing, on their ability to capture the main characteristics of dengue dynamics. We show that all proposed models reproduce the observed dengue patterns across some part of the parameter space. Which model best supports the dengue dynamics is determined by the level of seasonal forcing. Further, when tertiary and quaternary infections are allowed, the inclusion of temporary cross-immunity alone is strongly supported, but the addition of cross-enhancement markedly reduces the parameter range at which dengue dynamics are produced, irrespective of the strength of seasonal forcing. The implication of these structural uncertainties on predicted vulnerability to control is also discussed. With ever expanding spread of dengue, greater understanding of dengue dynamics and control efforts (e.g. a near-future vaccine introduction) has become critically important. This study highlights the capacity of multi-level pattern-matching modelling approaches to offer an analytic tool for deeper insights into dengue epidemiology and control.

Therapeutic Potential of Spirooxindoles as Antiviral Agents.

Antiviral therapeutics with profiles of high potency, low resistance, panserotype, and low toxicity remain challenging, and obtaining such agents continues to be an active area of therapeutic development. Due to their unique three-dimensional structural features, spirooxindoles have been identified as privileged chemotypes for antiviral drug development. Among them, spiro-pyrazolopyridone oxindoles have been recently reported as potent inhibitors of dengue virus NS4B, leading to the discovery of an orally bioavailable preclinical candidate (R)-44 with excellent in vivo efficacy in a dengue viremia mouse model. This review highlights recent advances in the development of biologically active spirooxindoles for their antiviral potential, primarily focusing on the structure-activity relationships (SARs) and modes of action, as well as future directions to achieve more potent analogues toward a viable antiviral therapy.

Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China.

BackgroundDengue is a serious vector-borne disease, and incidence rates have significantly increased during the past few years, particularly in 2014 in Guangzhou. The current situation is more complicated, due to various factors such as climate warming, urbanization, population increase, and human mobility. The purpose of this study is to detect dengue transmission patterns and identify the disease dispersion dynamics in Guangzhou, China.MethodologyWe conducted surveys in 12 districts of Guangzhou, and collected daily data of Breteau index (BI) and reported cases between September and November 2014 from the public health authority reports. Based on the available data and the Ross-Macdonald theory, we propose a dengue transmission model that systematically integrates entomologic, demographic, and environmental information. In this model, we use (1) BI data and geographic variables to evaluate the spatial heterogeneities of Aedes mosquitoes, (2) a radiation model to simulate the daily mobility of humans, and (3) a Markov chain Monte Carlo (MCMC) method to estimate the model parameters.Results/ConclusionsBy implementing our proposed model, we can (1) estimate the incidence rates of dengue, and trace the infection time and locations, (2) assess risk factors and evaluate the infection threat in a city, and (3) evaluate the primary diffusion process in different districts. From the results, we can see that dengue infections exhibited a spatial and temporal variation during 2014 in Guangzhou. We find that urbanization, vector activities, and human behavior play significant roles in shaping the dengue outbreak and the patterns of its spread. This study offers useful information on dengue dynamics, which can help policy makers improve control and prevention measures.

Detection of all four dengue serotypes in Aedes aegypti female mosquitoes collected in a rural area in Colombia.

The Aedes aegypti vector for dengue virus (DENV) has been reported in urban and periurban areas. The information about DENV circulation in mosquitoes in Colombian rural areas is limited, so we aimed to evaluate the presence of DENV in Ae. aegypti females caught in rural locations of two Colombian municipalities, Anapoima and La Mesa. Mosquitoes from 497 rural households in 44 different rural settlements were collected. Pools of about 20 Ae. aegypti females were processed for DENV serotype detection. DENV in mosquitoes was detected in 74% of the analysed settlements with a pool positivity rate of 62%. The estimated individual mosquito infection rate was 4.12% and the minimum infection rate was 33.3/1,000 mosquitoes. All four serotypes were detected; the most frequent being DENV-2 (50%) and DENV-1 (35%). Two-three serotypes were detected simultaneously in separate pools. This is the first report on the co-occurrence of natural DENV infection of mosquitoes in Colombian rural areas. The findings are important for understanding dengue transmission and planning control strategies. A potential latent virus reservoir in rural areas could spill over to urban areas during population movements. Detecting DENV in wild-caught adult mosquitoes should be included in the development of dengue epidemic forecasting models.

Fitting the Incidence Data from the City of Campinas, Brazil, Based on Dengue Transmission Modellings Considering Time-Dependent Entomological Parameters.

Four time-dependent dengue transmission models are considered in order to fit the incidence data from the City of Campinas, Brazil, recorded from October 1st 1995 to September 30th 2012. The entomological parameters are allowed to depend on temperature and precipitation, while the carrying capacity and the hatching of eggs depend only on precipitation. The whole period of incidence of dengue is split into four periods, due to the fact that the model is formulated considering the circulation of only one serotype. Dengue transmission parameters from human to mosquito and mosquito to human are fitted for each one of the periods. The time varying partial and overall effective reproduction numbers are obtained to explain the incidence of dengue provided by the models.

Dengue model rises to the challenge

Infectious Disease Human efficacy testing remains a major hurdle in bringing new vaccine candidates to the clinic. Without accepted correlates of protection, rounds of safety trials must be performed before efficacy can be tested in a large population in an endemic area. Kirkpatrick et al. developed a controlled human challenge model for dengue virus to assess the protective efficacy of the most clinically advanced dengue vaccine candidate. TV003, a live attenuated dengue vaccine that induces antibodies against all four dengue virus serotypes, protected against infection by an attenuated virus in 21 recipients when compared with 20 nonvaccinated controls. This model may serve as an early check for dengue vaccine candidates, limiting the risk of conducting large unsuccessful trials. Sci. Transl. Med. 8 , 330ra36 (2016).

Developing a Time Series Predictive Model for Dengue in Zhongshan, China Based on Weather and Guangzhou Dengue Surveillance Data.

BackgroundDengue is a re-emerging infectious disease of humans, rapidly growing from endemic areas to dengue-free regions due to favorable conditions. In recent decades, Guangzhou has again suffered from several big outbreaks of dengue; as have its neighboring cities. This study aims to examine the impact of dengue epidemics in Guangzhou, China, and to develop a predictive model for Zhongshan based on local weather conditions and Guangzhou dengue surveillance information.MethodsWe obtained weekly dengue case data from 1st January, 2005 to 31st December, 2014 for Guangzhou and Zhongshan city from the Chinese National Disease Surveillance Reporting System. Meteorological data was collected from the Zhongshan Weather Bureau and demographic data was collected from the Zhongshan Statistical Bureau. A negative binomial regression model with a log link function was used to analyze the relationship between weekly dengue cases in Guangzhou and Zhongshan, controlling for meteorological factors. Cross-correlation functions were applied to identify the time lags of the effect of each weather factor on weekly dengue cases. Models were validated using receiver operating characteristic (ROC) curves and k-fold cross-validation.ResultsOur results showed that weekly dengue cases in Zhongshan were significantly associated with dengue cases in Guangzhou after the treatment of a 5 weeks prior moving average (Relative Risk (RR) = 2.016, 95% Confidence Interval (CI): 1.845–2.203), controlling for weather factors including minimum temperature, relative humidity, and rainfall. ROC curve analysis indicated our forecasting model performed well at different prediction thresholds, with 0.969 area under the receiver operating characteristic curve (AUC) for a threshold of 3 cases per week, 0.957 AUC for a threshold of 2 cases per week, and 0.938 AUC for a threshold of 1 case per week. Models established during k-fold cross-validation also had considerable AUC (average 0.938–0.967). The sensitivity and specificity obtained from k-fold cross-validation was 78.83% and 92.48% respectively, with a forecasting threshold of 3 cases per week; 91.17% and 91.39%, with a threshold of 2 cases; and 85.16% and 87.25% with a threshold of 1 case. The out-of-sample prediction for the epidemics in 2014 also showed satisfactory performance.ConclusionOur study findings suggest that the occurrence of dengue outbreaks in Guangzhou could impact dengue outbreaks in Zhongshan under suitable weather conditions. Future studies should focus on developing integrated early warning systems for dengue transmission including local weather and human movement.

Comparative proteomic analysis of growth hormone secretagogue A233 treatment of murine macrophage cells J774A.2 indicates it has a role in antiviral innate response

BackgroundGrowth hormone secretagogues (GHS), among other factors, regulate the release of GH. The biological activity of the secretagogue peptide A233 as a promoter of growth and innate immunity in teleost fish has previously been demonstrated, but its role in the immune system of mammals is not well understood. MethodsThe effect of the peptide was investigated in J774A.2 macrophage cells using a comparative proteomics approach after 6 and 12h of peptide stimulation. ResultsThe functional analysis of differentially modulated proteins showed that A233 peptide treatment appears to promote activation and ROS-dependent cytotoxic functions in macrophages and enhanced expression of antiviral protein complexes such as MAVS. In accordance with this hypothesis, we found that A233 treatment enhanced superoxide anion production and the IFN-γ level in J774A.2 cells and mouse splenocytes, respectively, and reduced viral load in a dengue virus mouse model of infection. ConclusionsThe growth hormone secretagogue A233 peptide promotes activation of ROS-dependent cytotoxic functions and exerts immunomodulatory effects that enable an antiviral state in a dengue virus mouse model. General SignificanceThe increase of IFN-γ level and the differential modulation of antiviral proteins by the A233 peptide suggest that the molecule could activate an innate immune response with a possible further impact in the treatment of acute and chronic diseases.

Estimating dengue type reproduction numbers for two provinces of Sri Lanka during the period 2013–14

Dengue is an endemic disease in the southeast Asian country Sri Lanka. Two seasonal peaks of dengue incidence were observed every year since 2002 onwards. In this study, we formulate a 2-strain dengue model for analyzing the monthly seasonal dengue incidence data from 2 provinces of Sri Lanka during the period April 2013 to September 2014. The seasonality is incorporated in the model in terms of mosquito biting rate, which we assume to be time periodic. We estimated 2 primary reproduction numbers and the basic reproduction number in a periodic environment using dengue incidence data from the western and the central provinces of Sri Lanka. We also estimated different time-average type reproduction numbers from the model using the data from these 2 provinces. Using univariate sensitivity analysis, we measured the sensitivity of the time average reproduction number () When we vary different parameters of the proposed dengue model, we find the transmission probability of human susceptibility to strain-I infection and the mosquito mortality rate parameters are the most sensitive parameters in dengue transmission in these 2 provinces.

A sensitivity analysis and simulation of a Dengue model

A sensitivity analysis and simulation of a Dengue model

Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore.

Background:With its tropical rainforest climate, rapid urbanization, and changing demography and ecology, Singapore experiences endemic dengue; the last large outbreak in 2013 culminated in 22,170 cases. In the absence of a vaccine on the market, vector control is the key approach for prevention.Objectives:We sought to forecast the evolution of dengue epidemics in Singapore to provide early warning of outbreaks and to facilitate the public health response to moderate an impending outbreak.Methods:We developed a set of statistical models using least absolute shrinkage and selection operator (LASSO) methods to forecast the weekly incidence of dengue notifications over a 3-month time horizon. This forecasting tool used a variety of data streams and was updated weekly, including recent case data, meteorological data, vector surveillance data, and population-based national statistics. The forecasting methodology was compared with alternative approaches that have been proposed to model dengue case data (seasonal autoregressive integrated moving average and step-down linear regression) by fielding them on the 2013 dengue epidemic, the largest on record in Singapore.Results:Operationally useful forecasts were obtained at a 3-month lag using the LASSO-derived models. Based on the mean average percentage error, the LASSO approach provided more accurate forecasts than the other methods we assessed. We demonstrate its utility in Singapore’s dengue control program by providing a forecast of the 2013 outbreak for advance preparation of outbreak response.Conclusions:Statistical models built using machine learning methods such as LASSO have the potential to markedly improve forecasting techniques for recurrent infectious disease outbreaks such as dengue.Citation:Shi Y, Liu X, Kok SY, Rajarethinam J, Liang S, Yap G, Chong CS, Lee KS, Tan SS, Chin CK, Lo A, Kong W, Ng LC, Cook AR. 2016. Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environ Health Perspect 124:1369–1375; http://dx.doi.org/10.1289/ehp.1509981

Estimation of parameters related to vaccine efficacy and dengue transmission from two large phase III studies

BackgroundA tetravalent dengue vaccine was shown to be efficacious against symptomatic dengue in two phase III efficacy studies performed in five Asian and five Latin American countries. The objective here was to estimate key parameters of a dengue transmission model using the data collected during these studies. MethodsParameter estimation was based on a Sequential Monte Carlo approach and used a cohort version of the transmission model. Serotype-specific basic reproduction numbers were derived for each country. Parameters related to serotype interactions included duration of cross-protection and level of cross-enhancement characterized by differences in symptomaticity for primary, secondary and post-secondary infections. We tested several vaccine efficacy profiles and simulated the evolution of vaccine efficacy over time for the scenarios providing the best fit to the data. ResultsTwo reference scenarios were identified. The first included temporary cross-protection and the second combined cross-protection and cross-enhancement upon wild-type infection and following vaccination. Both scenarios were associated with differences in efficacy by serotype, higher efficacy for pre-exposed subjects and against severe dengue, increase in efficacy with doses for naïve subjects and by a more important waning of vaccine protection for subjects when naïve than when pre-exposed. Over 20 years, the median reduction of dengue risk induced by the direct protection conferred by the vaccine ranged from 24% to 47% according to country for the first scenario and from 34% to 54% for the second. ConclusionOur study is an important first step in deriving a general framework that combines disease dynamics and mechanisms of vaccine protection that could be used to assess the impact of vaccination at a population level.

Dengue human infection models to advance dengue vaccine development

Dengue viruses (DENV) currently infect approximately 400 million people each year causing millions to seek care and overwhelming the health care infrastructure in endemic areas. Vaccines to prevent dengue and therapeutics to treat dengue are not currently available. The efficacy of the most advanced candidate vaccine against symptomatic dengue in general and DENV-2 in particular was much lower than expected, despite the ability of the vaccine to induce neutralizing antibody against all four DENV serotypes. Because seroconversion to the DENV serotypes following vaccination was thought to be indicative of induced protection, these results have made it more difficult to assess which candidate vaccines should or should not be evaluated in large studies in endemic areas. A dengue human infection model (DHIM) could be extremely valuable to down-select candidate vaccines or therapeutics prior to engaging in efficacy trials in endemic areas. Two DHIM have been developed to assess the efficacy of live attenuated tetravalent (LATV) dengue vaccines. The first model, developed by the Laboratory of Infectious Diseases at the U. S. National Institutes of Health, utilizes a modified DENV-2 strain DEN2Δ30. This virus was derived from the DENV-2 Tonga/74 that caused only very mild clinical infection during the outbreak from which it was recovered. DEN2Δ30 induced viremia in 100%, rash in 80%, and neutropenia in 27% of the 30 subjects to whom it was given. The Walter Reed Army Institute of Research (WRAIR) is developing a DHIM the goal of which is to identify DENV that cause symptomatic dengue fever. WRAIR has evaluated seven viruses and has identified two that meet dengue fever criteria. Both of these models may be very useful in the evaluation and down-selection of candidate dengue vaccines and therapeutics.

A diffusive dengue disease model with nonlocal delayed transmission

In this paper, we derive a nonlocal delayed and diffusive dengue transmission model with a spatial domain being bounded as well as unbounded. We first address the well-posedness to the initial-value problem for the model. In the case of a bounded spatial domain, we establish the threshold dynamics for the spatially heterogeneous system in terms of the basic reproduction number R0. Also, a set of sufficient conditions is further obtained for the global attractivity of the endemic steady state where all the parameters are spatially independent. In the case of an unbounded spatial domain, and when the coefficients are all constants, we show that there exist traveling wave solutions of the model. Numerical simulations are performed to illustrate our main analytic results.

Multiobjective approach to optimal control for a dengue transmission model

During the last decades, the global prevalence of dengue progressed dramatically. It is a disease which is now endemic in more than one hundred countries of Africa, America, Asia and the Western Pacific. This study addresses a mathematical model for the dengue disease transmission and finding the most effective ways of controlling the disease. The model is described by a system of ordinary differential equations representing human and vector dynamics. Multiobjective optimization is applied to find the optimal control strategies, considering the simultaneous minimization of infected humans and costs due to insecticide application. The obtained results show that multiobjective optimization is an effective tool for finding the optimal control. The set of trade-off solutions encompasses a whole range of optimal scenarios, providing valuable information about the dynamics of infection transmissions. The results are discussed for different values of model parameters.During the last decades, the global prevalence of dengue progressed dramatically.It is a disease which is now endemic in more than one hundred countries of Africa,America, Asia and the Western Pacific. This study addresses a mathematical modelfor the dengue disease transmission and finding the most effective waysof controlling the disease. The model is described by a system of ordinarydifferential equations representing human and vector dynamics. Multiobjectiveoptimization is applied to find the optimal control strategies, consideringthe simultaneous minimization of infected humans and costs due to insecticide application.The obtained results show that multiobjective optimization is an effective toolfor finding the optimal control. The set of trade-off solutions encompassesa whole range of optimal scenarios, providing valuable information about thedynamics of infection transmissions. The results are discussed for differentvalues of model parameters.

Multiobjective approach to optimal control for a dengue transmission model

During the last decades, the global prevalence of dengue progressed dramatically. It is a disease which is now endemic in more than one hundred countries of Africa, America, Asia and the Western Pacific. This study addresses a mathematical model for the dengue disease transmission and finding the most effective ways of controlling the disease. The model is described by a system of ordinary differential equations representing human and vector dynamics. Multiobjective optimization is applied to find the optimal control strategies, considering the simultaneous minimization of infected humans and costs due to insecticide application. The obtained results show that multiobjective optimization is an effective tool for finding the optimal control. The set of trade-off solutions encompasses a whole range of optimal scenarios, providing valuable information about the dynamics of infection transmissions. The results are discussed for different values of model parameters.

Meteorologically Driven Simulations of Dengue Epidemics in San Juan, PR.

Meteorological factors influence dengue virus ecology by modulating vector mosquito population dynamics, viral replication, and transmission. Dynamic modeling techniques can be used to examine how interactions among meteorological variables, vectors and the dengue virus influence transmission. We developed a dengue fever simulation model by coupling a dynamic simulation model for Aedes aegypti, the primary mosquito vector for dengue, with a basic epidemiological Susceptible-Exposed-Infectious-Recovered (SEIR) model. Employing a Monte Carlo approach, we simulated dengue transmission during the period of 2010–2013 in San Juan, PR, where dengue fever is endemic. The results of 9600 simulations using varied model parameters were evaluated by statistical comparison (r2) with surveillance data of dengue cases reported to the Centers for Disease Control and Prevention. To identify the most influential parameters associated with dengue virus transmission for each period the top 1% of best-fit model simulations were retained and compared. Using the top simulations, dengue cases were simulated well for 2010 (r2 = 0.90, p = 0.03), 2011 (r2 = 0.83, p = 0.05), and 2012 (r2 = 0.94, p = 0.01); however, simulations were weaker for 2013 (r2 = 0.25, p = 0.25) and the entire four-year period (r2 = 0.44, p = 0.002). Analysis of parameter values from retained simulations revealed that rain dependent container habitats were more prevalent in best-fitting simulations during the wetter 2010 and 2011 years, while human managed (i.e. manually filled) container habitats were more prevalent in best-fitting simulations during the drier 2012 and 2013 years. The simulations further indicate that rainfall strongly modulates the timing of dengue (e.g., epidemics occurred earlier during rainy years) while temperature modulates the annual number of dengue fever cases. Our results suggest that meteorological factors have a time-variable influence on dengue transmission relative to other important environmental and human factors.