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Modélisation et dengue

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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.

PLOS 27/04/17 Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models

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 Understanding the drivers of recent Zika, dengue, and chikungunya epidemics is a major public health priority. Copyright: © 2017 Mordecai et al. Introduction Results Fig 1. 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.

Int. J. Environ. Res. Public Health 2016, DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics

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.

PLOS 09/05/16 The Role of Serotype Interactions and Seasonality in Dengue Model Selection and Control: Insights from a Pattern Matching Approach

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. Author Summary The fluctuations of multi-serotype infectious diseases are often highly irregular and hard to predict. Editor: Samuel V. 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.

BMC INFORMATICS 16/04/16 Analysis of significant factors for dengue fever incidence prediction

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. [31] 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. [31] 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 [15].

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.

PLOS 10/10/14 A Probabilistic Spatial Dengue Fever Risk Assessment by a Threshold-Based-Quantile Regression Method

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. 1 Laboratoire d'Aérologie, Observatoire Midi-Pyrénées (OMP), Université Paul Sabatier, Toulouse 31400, France 2 Service de Démoustication et de Lutte Anti-vectorielle, Conseil Général de la Martinique/Agence Régionale de Santé (SD-LAV), Fort-de-France, Martinique 97262, France 3 Direction de la Stratégie et des Programmes/Terre-Environnement-Climat, Centre National d'Etudes Spatiales (CNES), Toulouse 31400, France 4 France Direction Inter-Régionale Antilles-Guyane, Fort-de-France, Martinique 97888, France 5 Lamont-Doherty Earth Observatory (LDEO) of Columbia University, Palisades, New York, NY 10964-1000, USA * Author to whom correspondence should be addressed.

INTERNATIONAL JOURNAL OF GEO INFORMATION 10/12/14 Mapping Entomological Dengue Risk Levels in Martinique Using High-Resolution Remote-Sensing Environmental Data

Received: 10 September 2014 / Revised: 6 November 2014 / Accepted: 25 November 2014 / Published: 10 December 2014 Controlling dengue virus transmission mainly involves integrated vector management. Risk maps at appropriate scales can provide valuable information for assessing entomological risk levels. MDPI and ACS Style AMA Style. 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.

Epidemics Volume 11, June 2015, Dengue disease outbreak definitions are implicitly variable

Many different case data-based statistical methods are used to trigger outbreak response. Here we show that these methods are inconsistent and incomparable. This may hinder the effectiveness of outbreak response. Clear quantitative definitions of an outbreak are a prerequisite for effective outbreak early warning and response. 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. A Centre for Health Economics Research and Modelling of Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Belgiumb Interuniversity Institute of Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgiumc School of Public Health and Community Medicine, University of New South Wales, Australia Received 19 June 2012, Revised 3 April 2013, Accepted 5 April 2013, Available online 19 April 2013 Choose an option to locate/access this article: Check if you have access through your login credentials or your institution Check access doi:10.1016/j.mbs.2013.04.001 Get rights and content Highlights Fit of a periodic model to dengue incidence data in Singapore from 2003 to 2007.

Mathematical Biosciences Volume 244, Issue 1, July 2013, Pages 22–28 A simple periodic-forced model for dengue fitted to inciden

Estimation of the parameters governing the vector population dynamics. 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. Uncertainties regarding dengue modeling in Rio de Janeiro, Brazil Paula Mendes LuzI,1; Cláudia Torres CodeçoI; Eduardo MassadII; Claudio José StruchinerI.

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. PLOS 11/01/11 Modeling the Dynamic Transmission of Dengue Fever: Investigating Disease Persistence. Abstract Background Dengue is a disease of great complexity, due to interactions between humans, mosquitoes and various virus serotypes as well as efficient vector survival strategies.

PLOS 11/01/11 Modeling the Dynamic Transmission of Dengue Fever: Investigating Disease Persistence

Thus, understanding the factors influencing the persistence of the disease has been a challenge for scientists and policy makers. The aim of this study is to investigate the influence of various factors related to humans and vectors in the maintenance of viral transmission during extended periods. Methodology/Principal Findings We developed a stochastic cellular automata model to simulate the spread of dengue fever in a dense community. Conclusions/Significance This study contributed to a better understanding of the dynamics of dengue subsistence.

Author Summary. PLOS 31/05/11 Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance. Abstract Background A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist. In this study, we aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics. Methodology/Principal Findings. 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. The transmission area of this disease continues to expand due to many direct and indirect factors linked to urban sprawl, increased travel and global warming. Current preventative measures include mosquito control programs, yet due to the complex nature of the disease and the increased importation risk along with the lack of efficient prophylactic measures, successful disease control and elimination is not realistic in the foreseeable future. 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.