Special Issue "Big Data and Mathematical Modeling in Biomedicine"
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).
Deadline for manuscript submissions: 31 March 2022.
Interests: public health; epidemiology; infectious disorders; vaccination
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Interests: biomathematics and mathematical modelling; mathematics for public health; mathematical epidemiology; mathematical modelling of communicable disorders; Big Data analytics
In recent years, Big Data have garnered significant interest from the scientific community. The concept of Big Data refers to extremely large and massive data sets that, because of their complexity and high degree of heterogeneity, cannot be analyzed and interpreted by means of conventional approaches (such as multivariate regression analyses and similar techniques). Being technically demanding and computationally challenging, they require particular efforts: New algorithms are required to effectively handle, manipulate, and coherently integrate data (the so-called “Big Data analytics”). These methodologies enable scholars to extract significant and relevant patterns in terms of trends, interactions, associations, and correlations. Big Data are classically characterized by three Vs: (i) velocity (in terms of the speed of data acquisition and data processing, Big Data as “fast data”); (ii) volume (in terms of amount of information); and (iii) variety (in terms of the number of sources and streams that can produce Big Data). There are different kinds of Big Data, depending on their generating sources, including: (i) “molecular Big Data”, produced by wet-lab techniques (and recent OMICS-based approaches like genomics, proteomics, postgenomics, metabolomics/metabonomics, epigenomics, etc.); (ii) “digital Big Data” generated by imaging technologies and sensors (in particular, wearable sensors); and (iii) “computational Big Data” produced by sources like the internet, mobiles, smart phones, and other devices. In the field of biomedicine, Big Data can be used for a variety of purposes and tasks, such as: (i) performing disease surveillance and signal detection, (ii) predicting disease risk, (iii) targeting treatment interventions, and, last but not least, (iv) understanding the etiopathogenetic mechanisms of diseases. Mathematical modelling is of paramount importance in Big Data visualization, mining, clustering, analysis, and interpretation.
Dr. Nicola Bragazzi
Prof. Jianhong Wu
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Big Data
- Big Data analytics
- Big Data mining
- Big Data clustering
- Mathematical modelling
- Computational biomedicine