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Modélisation et influenza

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Epidemics Volume 17, December 2016 Modelling the impact of co-circulating low pathogenic avian influenza viruses on epidemics of highly pathogenic avian influenza in poultry. Epidemics Available online 21 February 2017 Modelling H5N1 in Bangladesh across spatial scales: Model complexity and zoonotic transmission risk. Highlights H5N1 avian influenza remains a persistent threat to public health.

Epidemics Available online 21 February 2017 Modelling H5N1 in Bangladesh across spatial scales: Model complexity and zoonotic transmission risk

We modelled two different H5N1 outbreaks in Bangladesh at two spatial scales. Our model framework incorporated transmission at the human–poultry interface. We assessed the main contributors to poultry infection and spillover transmission to humans. The model parameters meriting inclusion are sensitive to spatial scale. Abstract Highly pathogenic avian influenza H5N1 remains a persistent public health threat, capable of causing infection in humans with a high mortality rate while simultaneously negatively impacting the livestock industry. Keywords. PLOS 20/04/07 Risk Maps for the Spread of Highly Pathogenic Avian Influenza in Poultry. Abstract Devastating epidemics of highly contagious animal diseases such as avian influenza, classical swine fever, and foot-and-mouth disease underline the need for improved understanding of the factors promoting the spread of these pathogens.

PLOS 20/04/07 Risk Maps for the Spread of Highly Pathogenic Avian Influenza in Poultry

Here the authors present a spatial analysis of the between-farm transmission of a highly pathogenic H7N7 avian influenza virus that caused a large epidemic in The Netherlands in 2003. The authors developed a method to estimate key parameters determining the spread of highly transmissible animal diseases between farms based on outbreak data. The method allows for the identification of high-risk areas for propagating spread in an epidemiologically underpinned manner. A central concept is the transmission kernel, which determines the probability of pathogen transmission from infected to uninfected farms as a function of interfarm distance. Trop Anim Health Prod (2014) 46:57–63 Modelling influenza A H5N1 vaccination strategy scenarios in the household poultry sector in Egypt.

The countries in which HPAI H5N1 virus is currently considered endemic include Bangladesh, China, Egypt, Indonesia, Vietnam as well as the Indian State of West Bengal (Hinrichs and Otte 2012).

Trop Anim Health Prod (2014) 46:57–63 Modelling influenza A H5N1 vaccination strategy scenarios in the household poultry sector in Egypt

Waves of AI outbreaks may occur in poultry populations in endemic areas whenever large parts of the population are left with a low level of immunity as a result of low VC (Magalhaes et al. 2006). The present study represents an assessment of the interaction of VS with poultry production parameters that affect population dynamics on the CAFI. The level of village CAFI depended on the VE and VC achieved. The latter varied between villages but was generally low. CAFI is also affected by the flock composition and production parameters. For Pekin duck, the CAFI modelled under the adopted strategy was limited because the required booster shot was not given and the short cycle (4–6 weeks) results in rapid removal of vaccinated Pekin ducks (to be replaced by unvaccinated ones).

COMMUNITY OF ORDINARY DIFFERENTIAL EQUATIONS EDUCATORS 03/09/14 Modeling the Effects of Avian Flu (H5N1) Vaccination Strategies on Poultry. Publication Date Keywords Disease Modeling; Avian Flu; Poultry; SIR Model Abstract The work in this article addresses a problem posed by Dr.

COMMUNITY OF ORDINARY DIFFERENTIAL EQUATIONS EDUCATORS 03/09/14 Modeling the Effects of Avian Flu (H5N1) Vaccination Strategies on Poultry

Maria Salvato to the CODEE community. 10.5642/codee.201410.01.01 Recommended Citation. VETERINARY RESEARCH Vol. 41 No. 3 (May-June 2010) Anthropogenic factors and the risk of Highly Pathogenic Avian Influenza H5N1: prospects from a spatial-based model. Vet.

VETERINARY RESEARCH Vol. 41 No. 3 (May-June 2010) Anthropogenic factors and the risk of Highly Pathogenic Avian Influenza H5N1: prospects from a spatial-based model

Res. (2010) 41:28 Original article Anthropogenic factors and the risk of highly pathogenic avian influenza H5N1: prospects from a spatial-based model Mathilde Paul1,2*, Saraya Tavornpanich3, David Abrial1, Patrick Gasqui1, Myriam Charras-Garrido1, Weerapong Thanapongtharm3, Xiangming Xiao4, Marius Gilbert5,6, Francois Roger2 and Christian Ducrot1. JOURNEE D'EPIDEMIOLOGIE AEEMA, 1er juin 2007 Influenza aviaire : Modélisation du risque d'infection des oiseaux à partir d'étangs contaminés. BMC 21/07/16 Model-based clustering with certainty estimation: implication for clade assignment of influenza viruses. The experimental dataset 1 was studied previous in [13], where a 2D MDS was used to visualize structure of HPAI H5N1 HA sequence data.

BMC 21/07/16 Model-based clustering with certainty estimation: implication for clade assignment of influenza viruses

Further investigation identifies 2 issues: 1) the 2D MDS may not be an optimal way to represent the complexity of the sequence data; 2) there is no estimation of confidence level for individual sequences or specific clusters. To address the first issue, we used the criterion suggested in [27] to select the dimension of MDS (d). The Mardia criterion (a parameter used for determining the number of dimensions that considerably differ) shows significant increases for d from 1 to 2 and from 2 to 3 (Fig. 1), and after that the increase becomes less obvious. Therefore, we chose d = 3. Figure 2 shows the corresponding 3D MDS plot of the H5N1 HA influenza sequences, which obviously provides better separation between clusters (i.e., clades). We compared the clusters obtained from Mclust based on the 3D MDS and those from the clade designation of WHO.

AVIAN DISEASES - MAI 2016 - The Use of Spatial and Spatiotemporal Modeling for Surveillance of H5N1 Highly Pathogenic Avian Influenza in Poultry in the Middle East. UNIVERSITE BLAISE PASCAL CLERMONT-FERRAND 26/06/14 Thèse en ligne : Mathematical modelling of the infectious spread of avian influenza on a backyard chicken production chain in Thailand. IND44395547. PLOS 27/08/14 Mathematical Modeling of Influenza A Virus Dynamics within Swine Farms and the Effects of Vaccination. Abstract Influenza A virus infections are widespread in swine herds across the world.

PLOS 27/08/14 Mathematical Modeling of Influenza A Virus Dynamics within Swine Farms and the Effects of Vaccination

Influenza negatively affects swine health and production, and represents a significant threat to public health due to the risk of zoonotic infections. Swine herds can act as reservoirs for potentially pandemic influenza strains. In this study, we develop mathematical models based on experimental data, representing typical breeding and wean-to-finish swine farms.

These models are used to explore and describe the dynamics of influenza infection at the farm level, which are at present not well understood. Nonlinear Analysis Real World Applications. 09/2008; Modeling the spread of bird flu and predicting outbreak diversity. NONLINEAR ANALYSIS: REAL WORLD APPLICATIONS - 2015 - Modeling avian influenza using Filippov systemps to determine culling of infected birds and quarantine. Computational and Mathematical Methods in Medicine - 2015 - Global Dynamics of Avian Influenza Epidemic Models with Psychological Effect. Preventive Veterinary Medicine Volume 114, Issue 1, 1 April 2014, Zero-inflated models for identifying disease risk factors when case detection is imperfect: Application to highly pathogenic avian influenza H5N1 in Thailand. Volume 114, Issue 1, 1 April 2014, Pages 28–36 Special Issue: GEOVET 2013 Edited By Michael Ward, Olaf Berke, Andres M.

Preventive Veterinary Medicine Volume 114, Issue 1, 1 April 2014, Zero-inflated models for identifying disease risk factors when case detection is imperfect: Application to highly pathogenic avian influenza H5N1 in Thailand

Perez, Dirk Pfeiffer and Mark Stevenson a AGIRs Unit (UR22), Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Montpellier, Franceb Laboratoire de Santé Animale, Agence de Sécurité Sanitaire, Maisons-Alfort, Francec Veterinary Epidemiology Economics and Public Health, Royal Veterinary College, London, United Kingdomd Université de Toulouse, INP-ENVT, INRA UMR 1225 IHAP, Toulouse, Francee Department of Livestock Development, Bangkok, Thailandf Université Libre de Bruxelles, Bruxelles, Belgiumg Fonds National de la Recherche Scientifique, Bruxelles, Belgiumh Ecole Nationale Vétérinaire d’Alfort, Maisons-Alfort, Francei Faculty of Veterinary Medicine, Kasetsart University, Bangkok, Thailand Available online 16 January 2014 Choose an option to locate/access this article: Check access doi:10.1016/j.prevetmed.2014.01.011 Get rights and content.

Preventive veterinary medicine 2014 Mar. 1, v. 113, no. 4 Using quantitative disease dynamics as a tool for guiding response to avian influenza in poultry in the United States of America. Search National Agricultural Library Digital Collections Back to Search NALDC Record Details: Using quantitative disease dynamics as a tool for guiding response to avian influenza in poultry in the United States of America.

Preventive veterinary medicine 2014 Mar. 1, v. 113, no. 4 Using quantitative disease dynamics as a tool for guiding response to avian influenza in poultry in the United States of America

Theory Biosci. (2014) 133:23–38 A mathematical model of avian influenza with half-saturated incidence. INTERNATIONAL JOURNAL OF BIOLOGICAL SCIENCES 26/08/14 Modelling an Econometric Regional Equations for Avian Influenza Outbreaks in Egypt under Current Climate Conditions. International Journal of Biological Sciences and Applications Manuscript Information Modelling an Econometric Regional Equations for Avian Influenza Outbreaks in Egypt under Current Climate Conditions International Journal of Biological Sciences and Applications Vol.1 , No. 3, Publication Date: August 26, 2014, Page: 1-6 150 Views Since August 26, 2014, 6 Downloads Since April 14, 2015.

INTERNATIONAL JOURNAL OF BIOLOGICAL SCIENCES 26/08/14 Modelling an Econometric Regional Equations for Avian Influenza Outbreaks in Egypt under Current Climate Conditions

BMC Bioinformatics 2014, 15:276 Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks. INFECTIOUS DISEASE OF POVERTY - 2013 - Research priorities in modeling the transmission risks of H7N9 bird flu. SCIENTIFIC REPORTS 12/07/13 Modeling highly pathogenic avian influenza transmission in wild birds and poultry in West Bengal, India. Data collection The National Institute of Virology and the Ela Foundation of India collected migratory bird data as part of an avian influenza surveillance project in 2010.

The data included observations of known wintering grounds along with incidentally found congregations on large natural water-bodies, flooded rice fields, and reservoirs. UNIVERSITY OF FLORIDA 23/11/13 Avian Flu: Modeling and Implications for Control. IFPRI - AVRIL 2009 - Mapping the Likelihood of Introduction and Spread of Highly Pathogenic Avian Influenza Virus H5N1 in Africa, Ghana, Ethiopia, Kenya and Nigeria using Multicriteria Decision Modelling. IFPRI - SEPT 2006 - Assessing potential impact of avian influenza on poultry in West Africa - A spatial equilibrium model analysis. BIOMED 14/06/13 A global model of avian influenza prediction in wild birds. Keiko Herrick and colleagues at the University of Alaska Fairbanks have put forward a model of avian influenza virus (AIV) prediction in wild birds, published yesterday in Veterinary Research. This is the first large-scale large ecological niche model for avian influenza in wild birds based on machine-learning algorithms.

The authors mined surveillance data for 2005-2010 from the Influenza Research Database, which yielded a large set of georeferenced sample points, complete with AIV detection status, viral subtype and other related parameters. The sampling data was then layered into ArcMap, along with 41 predictor layers – accounting for environmental and anthropogenic variables such as geographic elevation, adjusted mean temperatures and human population density – all obtained from open source projects. The model of avian influenza prediction reached by the authors, while not definitive, is a first accurate and global-scale predictive model. The full article is available at. Procedia Environmental Sciences Volume 7, 2011, Pages 104–109 Spatio-temporal model of avian influenza spread risk. Volume 7, 2011, Pages 104–109 Spatial Statistics 2011: Mapping Global Change Edited By Alfred Stein, Edzer Pebesma and Gerard Heuvelink Abstract HPAI virus has caused significant economic losses in the poultry industry.

WAGENINGEN UNIVERSITY 21/01/13 Thèse en ligne : Mechanisms of Avian Influenza virus transmission between farms: combining data c. MASSACHUSETTS INSTITUTE OF TECHNOLOGY - 2009 - Thèse en ligne : Modelling pandemic influenza progression using Spatiotemporal Ep. BMC - 2012 - Development of a resource modelling tool to support decision makers in pandemic influenza preparedness: the AsiaFlu. Bull World Health Organ vol.90 no.4 Genebra Apr. 2012 Epidemic and intervention modelling - a scientific rationale for policy de. Epidemic and intervention modelling - a scientific rationale for policy decisions?

Bull World Health Organ vol.90 no.4 Genebra Apr. 2012 Epidemic and intervention modelling - a scientific rationale for policy decisions? Lessons from the 2009 influenza pandemic – guatemalt

MEDICAL NEWS 06/04/11 Mathematical Modeling Of Migratory Birds, Domestic Poultry And Bird Flu. Eurosurveillance, Volume 14, Issue 26, 02 July 2009. Eurosurveillance, Volume 14, Issue 26, 02 July 2009 Table of Contents Rapid communications. Epidemiol. Infect. (2011), 139, 68–79. Model predictions and evaluation of possible control strategies for the 2009 A/H1N1v infl.

Epidemiol. Infect. (2011), 139, 68–79. Model predictions and evaluation of possible control strategies for the 2009 A/H1N1v influenza pandemic in Italy – guatemalt

INRA 07/08/09 Des modèles mathématiques et statistiques pour prévoir les évolutions des épidémies et pandémies de grippes. INVS 17/01/06 Au sommaire BEH n°02-03 Au sommaire: Risques infectieux : approches méthodologiques de la veille et de l'aide à la. PLOS 14/02/12 Modelling the Wind-Borne Spread of Highly Pathogenic Avian Influenza Virus between Farms. Abstract A quantitative understanding of the spread of contaminated farm dust between locations is a prerequisite for obtaining much-needed insight into one of the possible mechanisms of disease spread between farms. Here, we develop a model to calculate the quantity of contaminated farm-dust particles deposited at various locations downwind of a source farm and apply the model to assess the possible contribution of the wind-borne route to the transmission of Highly Pathogenic Avian Influenza virus (HPAI) during the 2003 epidemic in the Netherlands.

The model is obtained from a Gaussian Plume Model by incorporating the dust deposition process, pathogen decay, and a model for the infection process on exposed farms. ROYAL SOCIETY - SEPT 2015 - Modelling the species jump: towards assessing the risk of human infection from novel avian influenzas. Abstract The scientific understanding of the driving factors behind zoonotic and pandemic influenzas is hampered by complex interactions between viruses, animal hosts and humans. This complexity makes identifying influenza viruses of high zoonotic or pandemic risk, before they emerge from animal populations, extremely difficult and uncertain.

As a first step towards assessing zoonotic risk of influenza, we demonstrate a risk assessment framework to assess the relative likelihood of influenza A viruses, circulating in animal populations, making the species jump into humans. The intention is that such a risk assessment framework could assist decision-makers to compare multiple influenza viruses for zoonotic potential and hence to develop appropriate strain-specific control measures.