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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Swine flu infection exponent by county June 2009.svg|200px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''[[Infectious disease informatics]]''' ('''IDI''') is a multidisciplinary field of science that focuses on "the development of the science and technologies needed for collecting, sharing, reporting, analyzing, and visualizing infectious disease data and for providing data and decision-making support for infectious disease prevention, detection, and management." The field has expanded over time from analyzing [[public health laboratory]] data for potential disease vectors to a more robust syndromic surveillance of epidemiological factors and and to more advanced [[bioinformatics|bioinformatic]] approaches towards microbial, biomarker, and computational research.
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


Infectious disease informatics can help tackle problems and tasks such as optimizing developed antimicrobials, improving vaccines, discovering biomarkers for transmissibility and clinical outcomes of infectious diseases, and developing research into host-pathogen interactions. A few unique considerations must be made in IDI informatics applications, including the confidentiality of any included personal health information (PHI) and the non-binary nature of user access privileges. For example, public health director of a certain region may be able to contribute a dataset for analysis, but they'll have to ensure the right balance of PHI to meet local, state, and federal regulations. ('''[[Infectious disease informatics|Full article...]]''')<br />
[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information|Full article...]]''')<br />
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Revision as of 18:03, 10 June 2024

Fig2 Berezin PLoSCompBio23 19-12.png

"Ten simple rules for managing laboratory information"

Information is the cornerstone of research, from experimental data/metadata and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems (LIMS) to transform this large information load into useful scientific findings. The development of mathematical models that can predict the properties of biological systems is the holy grail of computational biology. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... (Full article...)

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