http://precedings.nature.com/tags/epidemiology Recently posted documents tagged with 'epidemiology' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://dx.doi.org/10.1038/npre.2009.3555.1 Models of epidemics allow decision makers to explore the consequences of different interventions. The Models of Infectious Disease Agent Study (MIDAS) project has been collecting studies, models, data supporting the models, and publications providing historical evidence about epidemics.An ontology has been developed for MIDAS to support the collection, documentation, and dissemination of models. It uses relations to link taxonomies (including a subset of the infectious disease ontology) that define the scope of its models and supporting documentation.The ontology is used to aid in the navigation process that is part of the user interface for identifying which studies and publications are available in the MIDAS repository (MREP) that are consistent with the many parameters associated with a particular study. http://dx.doi.org/10.1038/npre.2009.3555.1 Fri, 07 Aug 2009 09:09:20 UTC An Ontology for Designing Models of Epidemics doi:10.1038/npre.2009.3555.1 2009-08-07 Geoffrey Frank Nature Precedings 2009-08-07T09:09:20Z Presentation Immunology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3080/version/1 The multi-stage hypothesis suggests that cancers develop through a single defined series of genetic alterations. This hypothesis was first suggested over 50 years ago based upon age-specific incidence data. However, recent molecular studies of tumors indicate that multiple routes exist to the formation of cancer, not a single route. This parallel route hypothesis has not been tested with age-specific incidence data.To test the parallel route hypothesis, I formulated it in terms of a mathematical equation, confirmed this equation with computer simulations, then tested whether this equation was consistent with age-specific incidence data compiled by the Surveillance Epidemiology and End Results (SEER) cancer registries since 1973. I used the chi-squared goodness of fit test to measure consistency.I found that the age-specific incidence data from most human carcinomas, including those of the colon, lung, prostate, and breast were consistent with the parallel route hypothesis. However, this hypothesis is only consistent if an immune sub-population exists, one that will never develop carcinoma. Furthermore, breast carcinoma has two distinct forms of the disease, and one of these occurs at significantly different rates in different racial groups. http://precedings.nature.com/documents/3080/version/1 Fri, 17 Apr 2009 21:06:50 UTC Parallel routes of human carcinoma development: Implications of the age-specific incidence data hdl:10101/npre.2009.3080.1 2009-04-17 James Brody Nature Precedings 2009-04-17T21:06:50Z Manuscript Cancer Genetics & Genomics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/2969/version/1 Seasonal variation in the occurrence of several classes of cancer has been observed in the past. However, evidence for such trends in adult central nervous system tumors is scant. We have analyzed the monthly occurrence rates of glioblastomas as well as carcinomas metastatic to the brain in 6,154 neurosurgical patients in Toronto selected from the University Health Network neuropathologic database over a seven-year period (July 2001 to June 2008). The electronic repository was representative of the patient population in southern Ontario, and the case accession dates in the database reflected the onset patterns of the selected tumor groups. A modification to Nam’s alternative method to the Roger test was developed to statistically quantify the differences. The results demonstrated significant cyclical occurrence rates of glioblastomas with seasonal peaks in March, June, September and December. Moreover, significant increases in the rates of carcinomas metastatic to the brain were found for January, April and August. Surprisingly, the monthly frequency for the two tumor groups resembled each other in peak/trough topology. Semiquantitative comparison of major histologic features between glioblastomas from a peak (March) and trough (November) month in the seven-year period was performed, revealing differences in the amount of perivascular lymphocytic inflammation. This novel observation may have profound implications for the understanding of the biology of adult central nervous system tumors. http://precedings.nature.com/documents/2969/version/1 Tue, 24 Mar 2009 13:06:50 UTC Seasonal patterns of presentation in primary malignant brain tumors and metastases based on a retrospective neuropathologic database hdl:10101/npre.2009.2969.1 2009-03-24 Tim-Rasmus Kiehl Nature Precedings 2009-03-24T13:06:50Z Manuscript Cancer Neuroscience Earth & Environment http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/2447/version/1 Individual perception of vaccine safety is an important factor in determining a person’s adherence to a vaccination program and its consequences for disease control. This perception, or belief, about the safety of a given vaccine, is not a static parameter but a variable subject to environmental influence. To complicate matters, perception of risk (or safety) does not correspond to actual risk. In this paper we propose a way to model the dynamics of such beliefs in the context of a realistic epidemiological scenario. The methodology proposed is based on Bayesian inference, and can be extended to model more complex belief systems associated with decision models. http://precedings.nature.com/documents/2447/version/1 Tue, 28 Oct 2008 17:13:41 UTC Dynamic Modeling of Vaccinating Behavior as a Function of Individual Beliefs hdl:10101/npre.2008.2447.1 2008-10-28 Flávio C. Coelho Nature Precedings 2008-10-28T17:13:41Z Manuscript Ecology http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/2030/version/1 The development of quantitative models of outbreaks is key to their eventual control, from human and computer viruses through to social (and antisocial) activities. Standard epidemiological models can reproduce many general features of outbreaks. Unfortunately, the large temporal fluctuations which often dominate real-world data are thought to require more complicated, system-specific models involving super-spreaders, specific social network topologies and rewirings, and birth-death processes. However we show here that these large fluctuations have a generic explanation in terms of underlying community dynamics. Communities increasing (or decreasing) in size, act as instantaneous amplifiers (or suppressors) yielding a complex temporal evolution whose features vary dramatically according to the relative timescales of the community dynamics. We uncover, and provide an analytic theory for, a novel epidemiological phase transition driven by the population's response to an outbreak. An imminent epidemic will be suppressed if individual communities start to break up more frequently or join together less frequently, but will be amplified if the reverse is true. http://precedings.nature.com/documents/2030/version/1 Thu, 03 Jul 2008 16:15:53 UTC Community dynamics generates complex epidemiology through self-induced amplification and suppression hdl:10101/npre.2008.2030.1 2008-07-03 Neil Johnson Nature Precedings 2008-07-03T16:15:53Z Manuscript Ecology http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/1539/version/1 Most malaria-endemic countries are implementing a change in antimalarial drug policy to artemisinin combination therapy (ACT). The impact of different drug choices and implementation strategies is uncertain. A comprehensive model was constructed incorporating important epidemiological and biological factors and used to illustrate the spread of resistance in low and high transmission settings. The model predicts robustly that in low transmission settings drug resistance spreads faster than in high transmission settings, and that in low transmission areas ACTs slows the spread of drug resistance to a partner drug, especially at high coverage rates. This effect decreases exponentially with increasing delay in deploying the ACT and decreasing rates of coverage. A major obstacle to achieving the benefits of high coverage is the current cost of the drugs. This argues strongly for a global subsidy to make ACTs generally available and affordable in endemic areas. http://precedings.nature.com/documents/1539/version/1 Thu, 24 Jan 2008 14:53:00 UTC The spread of antimalarial drug resistance: A mathematical model with practical implications for ACT drug policies hdl:10101/npre.2008.1539.1 2008-08-18 Wirichada Pongtavornpinyo Nature Precedings 2008-01-24T14:53:00Z Manuscript Microbiology Pharmacology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2007.1373.1 The Centers for Disease Control (CDC) is currently attempting to eliminate syphilis in the United States (US); to ensure that their control strategies will be effective it is important to understand the transmission dynamics of syphilis. Epidemics of certain infectious diseases (e.g., influenza) can rise and fall with a well-defined periodicity; this cycling behavior is important because it can have significant implications for the design and effectiveness of control strategies. Here we discuss the methodology that has been used to identify epidemic cycles in longitudinal data sets, and the endogenous and exogenous mechanisms that generate cycling. We then examine the recently proposed hypothesis that syphilis epidemics cycle. This hypothesis was proposed based upon the results of a spectral analysis of a longitudinal data set that had been collected by the (CDC), and the analysis of a syphilis transmission model. We use spectral analysis to reanalyze the CDC’s data set, as well as to analyze a longitudinal mortality data set provided by the CDC. We also use published transmission models to predict the expected dynamics of syphilis epidemics. In contrast to the previous findings we find that: (i) that neither of the CDC’s data sets provide compelling evidence that syphilis epidemics cycle and (ii) published transmission models predict that syphilis epidemics should monotonically decrease (as a function of the treatment rate) rather than cycle. We explain the reasons why previous authors had proposed that syphilis epidemics cycle. Finally, we discuss the implications of our findings regarding the transmission dynamics of syphilis for the CDC’s elimination plan. http://dx.doi.org/10.1038/npre.2007.1373.1 Fri, 30 Nov 2007 18:29:36 UTC The transmission dynamics of syphilis and the CDC’s elimination plan doi:10.1038/npre.2007.1373.1 2007-11-30 Virginie Supervie Nature Precedings 2007-11-30T18:29:36Z Manuscript Ecology http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/378/version/1 The construction of complex statial simulation models such as those used in network epidemiology, is a daunting task due to the large amount of data involved in their parameterization. Such data, which frequently resides on large geo-referenced databases, has to be processed and assigned to the various components of the model. All this just to construct the model, then it still has to be simulated and analyzed under different epidemiological scenarios. This workflow can only be achieved efficiently by computational tools that can automate most if not all these time-consuming tasks. In this paper, we present a simulation software, Epigrass, aimed to help designing and simulating network-epidemic models with any kind of node behavior.A Network epidemiological model representing the spread of a directly transmitted disease through a bus-transportation network connecting mid-size cities in Brazil. Results show that the topological context of the starting point of the epidemic is of great importance from both control and preventive perspectives.Epigrass is shown to facilitate greatly the construction, simulation and analysis of complex network models. The output of model results in standard GIS file formats facilitate the post-processing and analysis of results by means of sophisticated GIS software. http://precedings.nature.com/documents/378/version/1 Fri, 06 Jul 2007 15:56:10 UTC Epigrass: a tool to study disease spread in complex networks. hdl:10101/npre.2007.378.1 2007-07-06 Flávio Codeço Coelho Nature Precedings 2007-07-06T15:56:10Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/2.5/