Microbial Risk Analysis Volume 10, December 2018, FSK-Lab – An open source food safety model integration tool. 1.
Introduction. EPIDEMICS 13/05/20 Tooling-up for infectious disease transmission modelling. 1.
Introduction and motivation The motivations for transmission dynamic modelling are the motivations for this special issue: harnessing available data to inform policy on the control of infectious diseases. The focus is how to get the most valuable and actionable information out of data - models that translate data into evidence for policy. Transmission dynamic modelling is now central to designing and evaluating public health interventions against infectious diseases, especially for intervention programmes. Who should we vaccinate? BMC MEDICINE 19/08/19 Guidelines for multi-model comparisons of the impact of infectious disease interventions. The identification and selection of models for inclusion in the model comparison should be transparent and minimise selection bias Once the research question has been determined, a number of models need to be selected for inclusion in the model comparison exercise.
The choice of models depends on the policy question; a definition of the types of mathematical model that may be included should be provided, e.g. individual-based models (agent-based models, microsimulation, etc.), compartmental models, Markov models, etc. Defining the mathematical models to be considered for the study will help with the subsequent development of search terms. Journal of Theoretical Biology 29/08/19 Mathematically Modeling Spillover Dynamics of Emerging Zoonoses with Intermediate Hosts.
FOODS 06/12/19 Predictive Modeling of Microbial Behavior in Food. 1.
Microorganisms and Food Microbiology is the scientific discipline that comprises the study of microorganisms (e.g., bacteria, fungi, protozoa, and algae) involved in life cycle chains. It encompasses specialties such as cell biology; genetics; taxonomy; epidemiology; biochemistry; pathogenic bacteriology; food, environmental, industrial, and agricultural microbiology; and microbial ecology. Microbiologists have found microbes living in just about everywhere; soil  water , air , animals , plants , rocks , and humans . PLOS 07/11/18 Transmissibility of emerging viral zoonoses. Abstract Effective public health research and preparedness requires an accurate understanding of which virus species possess or are at risk of developing human transmissibility.
Unfortunately, our ability to identify these viruses is limited by gaps in disease surveillance and an incomplete understanding of the process of viral adaptation. By fitting boosted regression trees to data on 224 human viruses and their associated traits, we developed a model that predicts the human transmission ability of zoonotic viruses with over 84% accuracy.
This model identifies several viruses that may have an undocumented capacity for transmission between humans. Viral traits that predicted human transmissibility included infection of nonhuman primates, the absence of a lipid envelope, and detection in the human nervous system and respiratory tract. Modélisation et alimentation (sécurité sanitaire, TIAC) EUREKALERT 20/11/18 New model predicts which animal viruses may spread among humans. Researchers have developed a model that predicts which of the viruses that can jump from animals to people can also be transmitted from person to person--and are therefore possible sources of human diseases.
The study, published recently in PLOS One, identified several viruses that are not yet known to spread among humans but may have that potential, suggesting possible targets for future disease surveillance and research efforts. "When we get new pathogens that we look at as new human diseases, most of the time they come from pathogens that were previously circulating in animals," said John Drake, Distinguished Research Professor of Ecology and director of the Center for the Ecology of Infectious Diseases at the University of Georgia.
"As ecologists, that makes us think that there must be something about the ways parasites and pathogens interact with their hosts or the environment that confers the propensity to this process. " NIMBioS via YOUTUBE 18/06/14 Postharvest food safety and modeling opportunities. 2016 4th International Conference on Advances in Social Science, Humanities, and Management - Modeling for Food Safety Risk Assessment and Control Based on FTA. 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. It also provides a first step towards showing proof of principle for an eventual pandemic risk model. 1. Influenza A has a natural reservoir in wild aquatic birds .
NATIONAL VETERINARY INSTITUTE (SVA_SE) - 2013 - Thèse en ligne : Emerging Infectious Diseases: a model of disease transmission dynamics at the wildlife-livestock interface in Uganda. Clinical Microbiology and Infection Volume 19, Issue 11, November 2013, Modelling in infectious diseases: between haphazard and hazard. Abstract Modelling of infectious diseases is difficult, if not impossible.
No epidemic has ever been truly predicted, rather than being merely noticed when it was already ongoing. Modelling the future course of an epidemic is similarly tenuous, as exemplified by ominous predictions during the last influenza pandemic leading to exaggerated national responses. The continuous evolution of microorganisms, the introduction of new pathogens into the human population and the interactions of a specific pathogen with the environment, vectors, intermediate hosts, reservoir animals and other microorganisms are far too complex to be predictable. Our environment is changing at an unprecedented rate, and human-related factors, which are essential components of any epidemic prediction model, are difficult to foresee in our increasingly dynamic societies. Modélisation et alimentation (sécurité sanitaire, TIAC) ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY - 2010 - Modelling parasite transmission and control.
CORNELL UNIVERSITY - 2014 - Predictive Modeling of Cholera Outbreaks in Bangladesh. CORNELL UNIVERSITY 02/12/14 Avoidable errors in the modeling of outbreaks of emerging pathogens, with special reference to Ebola. Rev. sci. tech. Off. int. Epiz., 2013, 32 (3), 605-617 Modelling risk aversion to support decision making for controlling zoonotic livestock diseases. Epidemics Available online 19 September 2014 Nine challenges in modelling the emergence of novel pathogens. DFID_GOV_UK 08/02/12 Characterising livestock system ‘zoonoses hotspots’
BMC Medical Informatics and Decision Making 2013, 13:12 In silico surveillance: evaluating outbreak detection with simulation mo. EUROPEAN RESEARCH COUNCIL - Modelling waterborne epidemics. The 22nd of March is UN World Water Day.
Each year World Water Day highlights the importance of fresh water for global health: water's critical role in food security; its cultural role in shaping societies worldwide; the impact of natural disasters; the significance of clean water supplies in preventing the spread of diseases. Caption: A young boy drinking the water of the Meghna River near Matlab (Bangladesh), a place where cholera is endemic©Photos by courtesy of Professor Andrea Rinaldo A UN report, unveiled last week at the World Water Forum in Marseille (France), revealed that nearly 800 million people do not have access to a safe water supply, whilst nearly 2.5 billion people lack basic sanitation. This represents a grave threat to global health. Professor Andrea Rinaldo, an ERC Advanced grantee 2008, is at the forefront of the fast-developing field of ecohydrology with his project 'River Networks as Ecological Corridors' (RINEC).
Modélisation et Listéria. Modélisation et Campylobacter. Modélisation et ESST. BIOSS - 2011 - Modelling the immuno-magnetic separation method of detecting E. coli O157 in bovine faecal samples. Modelling the immuno-magnetic separation method of detecting E. coli O157 in bovine faecal samples Escherichia coli O157 (E. coli O157) is an important food-borne illness in humans, of major concern to health agencies.
Cattle and other ruminants act as a natural reservoir and are an important source of infection, shedding the bacteria in their faeces. Detection of shedding animals in a herd is an important step in the prevention of contamination of food. The immuno-magnetic separation method (IMS) is a relatively rapid, simple and sensitive assay to detect E. coli O157, compared to conventional direct plate culture method (PC). Using antibody-coated beads, IMS can detect E. col O157 in bovine faeces, allowing us to classify animals as positive or negative.
Working with BioSS, experimentalists at the SAC analysed faecal samples using both IMS and PC. Further details from: Mintu Nath Article date 2011. NATIONAL VETERINARY INSTITUTE (Suède) 19/12/12 Présentation : Environmental factors in modelling zoonotic infectious diseases, INRA/METARISK 03/04/12 Présentation : Inférence d’un réseau bayésien augmenté visant à confronter : Veterinary Research Communications - 2005 - Probabilistic Models for Food-Borne Disease Risk Assessment. Eurosurveillance, Volume 15, Issue 12, 25 March 2010 Q fever outbreak in Cheltenham, United Kingdom, in 2007 and the use of disp. Weekly and monthly releases: Eurosurveillance releases: Two articles on Q fever outbreaks with different epidemiological patterns Eurosurveillance, Volume 15, Issue 12, 25 March 2010.
Eurosurveillance, Volume 15, Issue 12, 25 March 2010 Q fever outbreak in Cheltenham, United Kingdom, in 2007 and the use of dispersion modelling to investigate the possibility of airborne spread – guatemalt
UNIVERSITY OF FLORIDA - 2010 - Thèse en ligne : ECOLOGICAL NICHE MODELING OF A ZOONOSIS: A CASE STUDY USING ANTHRAX OUTBREAKS AN. EHP 17/12/09 Ecological Niche Modeling of Cryptococcus gattii in British Columbia, Canada. Mathematical Biosciences and Engineering 7, 1 (2010) 199-215 MODELS FOR THE SPREAD AND PERSISTENCE OF HANTAVIRUS INFECTION IN RO. IPCSIT vol.22 (2012) © (2012) Modelling Provenance in Food Supply Chain to Track and Trace Foodborne Disease.