![]() Indeed, the usual statistical approach is based on the Kolmogorov's law of large numbers which requires the existence of the first finite moment, and the Lyapunov's version of the central limit theorem assumes an existence of the finite moment of an order higher than two. Scientists and engineers have always searched for the best statistical distribution useful to predict the behavior of the systems under consideration. However, especially in the beginning of any epidemics we have only partial access to validated data also because the number of infected people is still rather small and follows a dynamic process. Traditionally, epidemiological analyses are based on sigmoidal models, which indeed are useful if the evolution of the epidemics follows well-established patterns. Consequently, it is fundamental to develop a reliable analytical approach that allows such predictive modeling. To implement effective public health measures in a timely manner and allocate scarce resources according to geographic need, it is very important to forecast the diffusion or spread of the infection amongst the population. Consequently, the interest in forecasting the diffusion of such global infectious disease threats is continuously increasing. Moreover, epidemics and pandemics can cause also significant, widespread economic hardship and potentially lead to social unrest. Some recent examples of pandemics are the 2003 SARS (Severe Acute Respiratory Syndrome), the 2014 West Africa Ebola epidemic, and the present COVID-19 caused by the coronavirus SARS-Cov-2. Indeed, epidemics can occur in a community or region by causing illness in excess of normal expectancy pandemics are no more than a large-scale global epidemic which determine a growth in morbidity and mortality over a wide geographic area. This is particularly true in epidemiology. Consequently, in any field of research, scientists, and engineers have always taken attention to find the best statistical distribution to predict the systems behavior. In the natural, social, economic, and physical sciences a large variety of phenomena are characterized by regularities, which can be analytically described by a defined statistical distribution. ^ "Concerns Over Use of Glyphosate-based Herbicides and Risks Associated with Exposures: a Consensus Statement - Cornucopia Institute"."Facts and Fallacies in the Debate on Glyphosate Toxicity". "MDPI removed from publisher list following successful appeal. Archived from the original on 12 January 2015. "Anti-Roundup (Glyphosate) Researchers Use Easy OA Journals to Spread their Views". Archived from the original on 29 January 2014. ![]() "When Media Uncritically Cover Pseudoscience". "Glyphosate's Suppression of Cytochrome P450 Enzymes and Amino Acid Biosynthesis by the Gut Microbiome: Pathways to Modern Diseases". ![]() ^ a b c Samsel, Anthony Stephanie Seneff (2013).Antoniou, both of whom are working to limit the use of glyphosate, said that "although evidence exists that glyphosate-based herbicides are toxic below regulatory set safety limits, the arguments of Samsel and Seneff largely serve to distract rather than to give a rational direction." In 2017 researchers Robin Mesnage and Michael N. In response to the controversy, the editors of Entropy added an "Expression of Concern" to the article's frontmatter. Beall removed MDPI from his list of predatory publishers in October 2015. It was criticized as pseudo-science by the science magazine Discover and Jeffrey Beall, founder of Beall's List of predatory open-access publishers, said "Will MDPI publish anything for money?". The paper does not contain any primary research results. In 2013, Entropy published a review paper saying glyphosate may be the most important factor in the development of obesity, depression, attention deficit hyperactivity disorder, autism, Alzheimer's disease, Parkinson's disease, multiple sclerosis, cancer, and infertility. Current Contents/Physical, Chemical & Earth SciencesĪccording to the Journal Citation Reports, the journal has a 2022 impact factor of 2.7.
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