UNIVERSITY OF ZURICH - 2013 - Présentation : Modelling power-law spread of infectious diseases. Clinical Microbiology and Infection Volume 19, Issue 11, November 2013, Modelling in infectious diseases: between haphazard and hazard. Open Archive 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. Keywords Epidemics; epidemiology; models; pandemic; prediction Introduction Prophecy is a good line of business, but it is full of risks. Mark Twain in Following the Equator Epidemics have played a role in human history since ancient times, and will continue to do so in the foreseeable future, despite overoptimistic assurances to the contrary.
Journal of Business Continuity & Emergency Planning 08/04/12 Creating a process for incorporating epidemiological modelling into outbreak management decisions. Abstract: Modern computational models of infectious diseases greatly enhance our ability to understand new infectious threats and assess the effects of different interventions.
The recently-released CDC Framework for Preventing Infectious Diseases calls for increased use of predictive modelling of epidemic emergence for public health preparedness. Currently, the utility of these technologies in preparedness and response to outbreaks is limited by gaps between modelling output and information requirements for incident management.
The authors propose an operational structure that will facilitate integration of modelling capabilities into action planning for outbreak management, using the Incident Command System (ICS) and Synchronization Matrix framework. It is designed to be adaptable and scalable for use by state and local planners under the National Response Framework (NRF) and Emergency Support Function #8 (ESF-8). Document Type: Research Article. PLOS 16/03/15 Location-Allocation and Accessibility Models for Improving the Spatial Planning of Public Health Services. Abstract This study integrated accessibility and location-allocation models in geographic information systems as a proposed strategy to improve the spatial planning of public health services.
To estimate the spatial accessibility, we modified the two-step floating catchment area (2SFCA) model with a different impedance function, a Gaussian weight for competition among service sites, a friction coefficient, distances along a street network based on the Dijkstra’s algorithm and by performing a vectorial analysis. To check the accuracy of the strategy, we used the data from the public sterilization program for the dogs and cats of Bogot´a, Colombia. Since the proposed strategy is independent of the service, it could also be applied to any other public intervention when the capacity of the service is known.
PLOS 10/07/14 On the Use of Human Mobility Proxies for Modeling Epidemics. Abstract Human mobility is a key component of large-scale spatial-transmission models of infectious diseases.
Correctly modeling and quantifying human mobility is critical for improving epidemic control, but may be hindered by data incompleteness or unavailability. Here we explore the opportunity of using proxies for individual mobility to describe commuting flows and predict the diffusion of an influenza-like-illness epidemic. We consider three European countries and the corresponding commuting networks at different resolution scales, obtained from (i) official census surveys, (ii) proxy mobility data extracted from mobile phone call records, and (iii) the radiation model calibrated with census data.
Metapopulation models defined on these countries and integrating the different mobility layers are compared in terms of epidemic observables. Author Summary. INRA/METARISK 21/09/09 Présentation : Modelling exposure to food contaminants: a dynamic approach. PLOS 12/03/15 Predicting Epidemic Risk from Past Temporal Contact Data. Introduction Being able to promptly identify who, in a system, is at risk of infection during an outbreak is key to the efficient control of the epidemic.
The explicit pattern of potential disease-transmission contacts has been extensively used to this purpose in the framework of theoretical studies of epidemic processes, uncovering the role of the pattern’s properties in the disease propagation and epidemic outcomes [1, 2, 3, 4, 5, 6, 7, 8]. These studies are generally based on the assumption that the entire pattern of contacts can be mapped out or that its main properties are known. Although such knowledge would be a critical requirement to conduct risk assessment analyses in real-time, which need to be based on the updated and accurate description of the contacts relevant to the outbreak under study , it can hardly be obtained in reality. Few studies have so far tried to answer this question by exploiting temporal information to control an epidemic through targeted immunization.
CIDRAP 22/10/13 Study: More focus on uncertainties would improve disease modeling. Mathematical models of infectious disease outbreaks are too often developed and presented with insufficient attention to the uncertainties that underlie them, say British researchers who carefully examined how uncertainties were addressed in a number of outbreak models.
The authors, from the University of Liverpool and Lancaster University, concluded that "greater consideration of the limitations and uncertainties in infectious disease modelling would improve its usefulness and value," says a press release yesterday from the Liverpool school. Their report was published last week in PLoS One. Disease models are often used, as the press release notes, to try to predict how outbreaks will progress and to influence response policies. ADV. STUDIES THERO. PHYS. - 2013 - Modeling the Dynamics of Infectious Diseases in Different Scale-Free Networks with the Same D. JOURNAL OF TOXICOLOGY - 2012 - State of the Science: Biologically Based Modeling in Risk Assessment, ARXIV 27/09/13 On the use of human mobility proxy for the modeling of epidemics.
ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY - 2010 - Modelling parasite transmission and control. COMPUTER SCIENCE 17/01/14 Epidemiological modeling of online social network dynamics. PLOS - sept 2012 - Improvement of Disease Prediction and Modeling through the Use of Meteorological Ensembles: Human Plague in U. Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens.
Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models.
Figures Editor: Natasha S. Introduction Study area background Figure 1. Istanbul Aydin University - 1998 - COMPARATIVE ECOLOGICAL RISK ASSESSMENT MODELS. Current Topics in Public Health. Edited by Alfonso J.
Rodriguez-Morales, ISBN 978-953-51-1121-4, 742 pages, Publisher: InTech, Chapters published May 15, 2013 under CC BY 3.0 licenseDOI: 10.5772/56648. UNIVERSITAT ZURICH - Statistical methods for spatio-temporal modelling and prediction of infectious diseases. Mem Inst Oswaldo Cruz, Rio de Janeiro, Vol. 107(5): 702-704, August 2012 The use of an adequate mathematical model is crucial to. Universidad Carlos III de Madrid - JUIN 2012 - BAYESIAN MODELLING OF BACTERIAL GROWTH FOR MULTIPLE POPULATIONS. BMC Medical Informatics and Decision Making 2009, 9:21 Syndromic surveillance: STL for modeling, visualizing, and monitoring dis. Data The STL decomposition was run on all Indiana EDs for respiratory and gastro-intestinal counts.
Both are fundamental markers for a number of naturally occurring diseases, and research has shown that diseases from bioweapons have early characterization of influenza-like illness , which typically results in respiratory complaints. We present results for the respiratory time series for the 30 EDs that came online the earliest; these series end in April 2008, and start at times ranging from November 2004 to September 2005. All analyses were performed using the R statistical software environment , and an R package is available as a supplemental download [see additional file 1].
Additional file 1. Format: ZIP Size: 83KB Download file.