SCIENTIFIC REPORTS 12/03/21 Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models. Dengue is a vector-borne disease, transmitted by two types of mosquito vectors; the Aedes Aegypti and Aedes Albopictus, where the life-cycle of the vector and transmission of the disease are closely related to climate variables1.
Dengue is endemic in tropical and subtropical regions worldwide, and this includes Malaysia, specifically the state of Selangor2. Of the total number of 120,836, 101,357 and 83,849 dengue cases that occurred in Malaysia, during the years of 2015, 2016, and 2017 respectively, 52.30%, 50.96% and 54.00% of these cases occurred in the state of Selangor3. The state level health authorities would alert all the districts in the abovementioned states (namely, Petaling, Klang, Hulu Selangor, Hulu Langat, Kuala Selangor, Gombak, Sepang, Kuala Langat and Sabak Bernam) if there is/are always hotspot(s) or many confirmed dengue cases being identified within these localities4.
Thus, specifically this study consists of three research objectives. PLOS 26/03/21 Assessing the suitability for Aedes albopictus and dengue transmission risk in China with a delay differential equation model. Abstract Dengue is considered non-endemic to mainland China.
However, travellers frequently import the virus from overseas and local mosquito species can then spread the disease in the population. As a consequence, mainland China still experiences large dengue outbreaks. Temperature plays a key role in these outbreaks: it affects the development and survival of the vector and the replication rate of the virus. To better understand its implication in the transmission risk of dengue, we developed a delay differential equation model that explicitly simulates temperature-dependent development periods and tested it with collected field data for the Asian tiger mosquito, Aedes albopictus.
Author summary. HELIYON 01/01/21 Dengue infection modeling and its optimal control analysis in East Java, Indonesia. APPLIED SCIENCES 21/01/21 Comparison of Dengue Predictive Models Developed Using Artificial Neural Network and Discriminant Analysis with Small Dataset. In Indonesia, dengue has become one of the hyperendemic diseases.
Dengue consists of three clinical phases—febrile phase, critical phase, and recovery phase. Many patients have died in the critical phase due to the lack of proper and timely treatment. Therefore, we developed models that can predict the severity level of dengue based on the laboratory test results of the corresponding patients using Artificial Neural Network (ANN) and Discriminant Analysis (DA). In developing the models, we used a very small dataset. It is shown that ANN models developed using logistic and hyperbolic tangent activation function with 70% training data yielded the highest accuracy (90.91%), sensitivity (91.11%), and specificity (95.51%). Math Biosci Eng. 2020 Jun 12; Modeling the effect of temperature on dengue virus transmission with periodic delay differential equations.
Dengue fever is a rapidly spreading mosquito-borne disease all over the world, and dengue virus (DENV) is transmitted to human by Aedes (stegomyia) mosquito, a genus of mosquitoes.
ELIFESCIENCES 13/05/19 Disease Risk: Mapping the emerging burden of dengue. Dengue is a viral disease spread by mosquitoes and found in more than 120 countries around the world (Bhatt et al., 2013).
Because the species that transmits the disease, Aedes aegypti, lives in dense man-made environments, recent urbanization throughout the tropical world has accelerated the spread of dengue (Bhatt et al., 2013). Aedes aegypti is also the primary vector for a number of other diseases, including Zika, chikungunya and Yellow Fever, so understanding how to control the global emergence of dengue could help to prevent the spread of these diseases. However, getting accurate data on who is being infected is surprisingly difficult. Only a small proportion of people (11–32%) are likely to have symptoms after contracting dengue, with few being sick enough to require formal medical care (Bhatt et al., 2013; Undurraga et al., 2013).
PROCESSES 03/07/20 A Two-Patch Mathematical Model for Temperature-Dependent Dengue Transmission Dynamics. Dengue fever has been a threat to public health not only in tropical regions but non-tropical regions due to recent climate change.
Motivated by a recent dengue outbreak in Japan, we develop a two-patch model for dengue transmission associated with temperature-dependent parameters. The two patches represent a park area where mosquitoes prevail and a residential area where people live. Based on climate change scenarios, we investigate the dengue transmission dynamics between the patches. We employ an optimal control method to implement proper control measures in the two-patch model.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 30/01/20 Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia. Spatial models are becoming more popular in time-to-event data analysis.
Commonly, the intrinsic conditional autoregressive prior is placed on an area level frailty term to allow for correlation between areas. We considered a range of Bayesian Weibull and Cox semiparametric spatial models to describe a dataset on hospitalisation of dengue. This paper aimed to extend these two models, to evaluate the suitability of these models for estimation and prediction of the length of stay, compare different spatial priors, and determine factors that significantly affect the duration of hospital stay for dengue fever patients in the case study location, namely Wahidin hospital in Makassar, Indonesia.
We compared two different models with three different spatial priors with respect to goodness of fit and generalisability. BMC INFECTIOUS DISEASES 26/03/14 Climate change and dengue: a critical and systematic review of quantitative modelling approaches. The initial search yielded 531 studies of which 456 were deemed to be potentially relevant and were subjected to further perusal.
This led to 75 studies considered in detail and 16 that strictly met the inclusion criteria. Table 1 shows the characteristics of the 16 studies that addressed the climate variables (climate change) and future risk of dengue transmission, using climate change scenarios. Dengue data. ARXIV 06/08/19 Multi-Strain Age-Structured Dengue Transmission Model: Analysis and Optimal Control. Int. J. Environ. Res. Public Health 28/06/19 Modeling and Predicting Dengue Incidence in Highly Vulnerable Countries using Panel Data Approach. REMOTE SENS 09/08/19 Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review. The selected articles were organized into two main categories (Figure 3) with respect to the data used as dependent variables for the prevalence of the diseases: (a) epidemiological data (disease incidence, prevalence or case, mortality data) (n = 31) and (b) entomological data (n = 11), while Stilianakis et al. has examined both (a) and (b) , and Valiakos et al. has additionally used wild bird data in complement to the epidemiological data .
The first category (a) used clinical records from the general human population as the main data source. In this case the majority of the studies (n = 23) referred to the clinical data as “confirmed cases”, meaning that the patients were confirmed through laboratory testing. Buczak et al.  and Arboleda et al.  included also cases that were considered as “possible”, meaning that the patients exhibited some of the symptoms of the infection. Through our database search, mainly data-driven and statistical approaches were returned. 3.1. MATHEMATICAL AND COMPUTATIONAL APPLICATIONS 21/06/18 Qualitative Analysis of a Dengue Fever Model.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 31/05/18 Modeling the Heterogeneity of Dengue Transmission in a City. Trop. Med. Infect. Dis. 2018, 3(1), 5; Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico.
MDPI and ACS Style Laureano-Rosario, A.E.; Duncan, A.P.; Mendez-Lazaro, P.A.; Garcia-Rejon, J.E.; Gomez-Carro, S.; Farfan-Ale, J.; Savic, D.A.; Muller-Karger, F.E.
Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico. Trop. Med. Infect. AMA Style. PLOS 27/04/17 Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models. Abstract Recent epidemics of Zika, dengue, and chikungunya have heightened the need to understand the seasonal and geographic range of transmission by Aedes aegypti and Ae. albopictus mosquitoes. We use mechanistic transmission models to derive predictions for how the probability and magnitude of transmission for Zika, chikungunya, and dengue change with mean temperature, and we show that these predictions are well matched by human case data. Across all three viruses, models and human case data both show that transmission occurs between 18–34°C with maximal transmission occurring in a range from 26–29°C.
Controlling for population size and two socioeconomic factors, temperature-dependent transmission based on our mechanistic model is an important predictor of human transmission occurrence and incidence. Author summary. REMOTE SENSING 30/03/17 Niche Modeling of Dengue Fever Using Remotely Sensed Environmental Factors and Boosted Regression Trees.
Int. J. Environ. Res. Public Health 2016, DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics. † This paper is an extended version of our paper published in Proceedings of the Winter Simulation Conference 2014, by IEEE, entitled A framework for modeling and simulating Aedes aegypti and dengue fever dynamics (doi:10.1109/WSC.2014.7020001) * Author to whom correspondence should be addressed. PLOS 09/05/16 The Role of Serotype Interactions and Seasonality in Dengue Model Selection and Control: Insights from a Pattern Matching Approach. Abstract 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. BMC INFORMATICS 16/04/16 Analysis of significant factors for dengue fever incidence prediction. As mentioned previously, no specific treatment exists for dengue infection, and effective vaccines remain at the developmental stage. Therefore, interrupting pathogen transmission by mosquito control is the most effective means of controlling dengue infection.
In Thailand, although mosquito surveillance has been in regular operation for many years, surveillance has not appeared to fully prevent dengue outbreaks. Seasonal factor has been previously studied by Wongkoon et al.  which is similar to the work in this report. Nonetheless, the main differences between these works are: firstly, we did not exploit only the number of Ae. aegypti populations found in urban and rural areas as demonstrated by Wongkoon et al.  but the dengue virus infection rate of larva and adult Ae. aegypti mosquitoes, which has never been determined for dengue fever transmission, was exploited. Infected female mosquito has also been used to predict dengue cases in our previous work .
Trans R Soc Trop Med Hyg 2015;109: 303–312 Dengue on islands: a Bayesian approach to understanding the global ecology of dengue viruses. Universiti Kebangsaan Malaysia - AVRIL 2013 - Classification of Dengue Outbreak Using Data Mining Models. PLOS 10/10/14 A Probabilistic Spatial Dengue Fever Risk Assessment by a Threshold-Based-Quantile Regression Method. Abstract Understanding the spatial characteristics of dengue fever (DF) incidences is crucial for governmental agencies to implement effective disease control strategies. We investigated the associations between environmental and socioeconomic factors and DF geographic distribution, are proposed a probabilistic risk assessment approach that uses threshold-based quantile regression to identify the significant risk factors for DF transmission and estimate the spatial distribution of DF risk regarding full probability distributions.
To interpret risk, return period was also included to characterize the frequency pattern of DF geographic occurrences. The study area included old Kaohsiung City and Fongshan District, two areas in Taiwan that have been affected by severe DF infections in recent decades. INTERNATIONAL JOURNAL OF GEO INFORMATION 10/12/14 Mapping Entomological Dengue Risk Levels in Martinique Using High-Resolution Remote-Sensing Environmental Data. Epidemics Volume 11, June 2015, Dengue disease outbreak definitions are implicitly variable. Open Access Highlights With appropriate and timely control, disease outbreak burden can be minimised. NANYANG TECHNOLOGICAL UNIVERSITY, Singapore - 1999 - Modelling the spread of dengue in Singapore. Indian J Med Res 136, July 2012, pp 7-9 Advances in developing a climate based dengue outbreak models in Dhaka, Bangladesh: chal. Indian J Med Res 136, July 2012, pp 32-39 Climatic factors influencing dengue cases in Dhaka city: a model for dengue prediction.
Indian J Med Res 137, April 2013, pp 811-812 An analysis on model development for climatic factors influencing prediction of den. World Applied Sciences Journal 22 (4): 506-515, 2013 Dengue fever Outburst and its Relationship with Climatic Factors. National Remote Sensing Centre (ISRO), India, 31/05/13 Alternating Decision trees for early diagnosis of dengue fever. Mathematical Biosciences Volume 244, Issue 1, July 2013, Pages 22–28 A simple periodic-forced model for dengue fitted to inciden. MAHIDOL UNIVERSITY Vol 44 No. 2 March 2013 TEMPORAL PATTERNS AND A DISEASE FORECASTING MODEL OF DENGUE HEMORRHAGIC FEVER IN JAKA. International Journal of Computer Mathematics 20/05/13 Bioeconomic Perspectives to an Optimal Control Dengue Model. Conference Papers in Mathematics Volume 2013 (2013), Sensitivity Analysis in a Dengue Epidemiological Model. INDIAN JOURNAL OF MEDICAL RESEARCH - 2012 - Advances in developing a climate based dengue outbreak models in Dhaka, Bangladesh:
Mem. Inst. Oswaldo Cruz vol.98 no.7 Rio de Janeiro Oct. 2003 Uncertainties regarding dengue modeling in Rio de Janeiro, Brazil. University of New South Wales - 2011 - A Predictive Spatial Model to Quantify the Risk of Air Travel Associated Dengue Importati.
University of New South Wales - 2011 - A Predictive Spatial Model to Quantify the Risk of Air Travel Associated Dengue Importation into the United States and Europe – guatemalt
PLOS 11/01/11 Modeling the Dynamic Transmission of Dengue Fever: Investigating Disease Persistence. Abstract Background. PLOS 31/05/11 Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance. Abstract Background. PLOS 22/05/12 Surveillance of Dengue Fever Virus: A Review of Epidemiological Models and Early Warning Systems. Abstract Dengue fever affects over a 100 million people annually hence is one of the world's most important vector-borne diseases. ARCHIVES OUVERTES - 2009 - Modelling Dengue Epidemics with Autoregressive Switching Markov Models (AR-HMM)
UNIVERSITE ANTILLES GUYANE - Modélisation mathématique de phénomènes liés à la dengue. Cad. Saúde Pública, Rio de Janeiro, 28(11):2189-2197, nov, 2012 Temporal analysis of the relationship between dengue and meteoro. Asian Pacific Journal of Tropical Disease, Volume 3, Issue 5, October 2013, Pages 352-361 Generating temporal model using climat. UNIVERSITE DE ROUEN/CNRS via ANR - 2013 - Présentation : AEDESS: Analyse de l’Emergence de la Dengue Et Simulation Spatiale (Del.