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10.1016/j.epidem.2020.100395 http://scihub22266oqcxt.onion/10.1016/j.epidem.2020.100395 32405321!7219405!32405321     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Epidemics 2020 ; 32 (ä): 100395 Nephropedia Template TP {{tp|p=32405321|t=2020. Tooling-up for infectious disease transmission modelling |pdf=|usr=}}{{32405321}} gab.com Text Tooling-up for infectious disease transmission modelling JO: Epidemics http://www.kidney.de/pget.php?&tw=1&pmid=32405321 #FOAvip In this introduction to the Special Issue on methods for modelling of infectious disease epidemiology we provide a commentary and overview of the field. We suggest that the field has been through three revolutions that have focussed on specific methodological developments; disease dynamics and heterogeneity, advanced computing and inference, and complexity and application to the real-world. Infectious disease dynamics and heterogeneity dominated until the 1980s where the use of analytical models illustrated fundamental concepts such as herd immunity. The second revolution embraced the integration of data with models and the increased use of computing. From the turn of the century an emergence of novel datasets enabled improved modelling of real-world complexity. The emergence of more complex data that reflect the real-world heterogeneities in transmission resulted in the development of improved inference methods such as particle filtering. Each of these three revolutions have always kept the understanding of infectious disease spread as its motivation but have been developed through the use of new techniques, tools and the availability of data. We conclude by providing a commentary on what the next revoluition in infectious disease modelling may be. Twit Text FOAVip Tooling-up for infectious disease transmission modelling ????Epidemics http://www.kidney.de/pget.php?&tw=1&pmid=32405321 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32405321 #FOAvip English Wikipedia <ref>{{Cite pmid|32405321}}</ref> Tooling-up for infectious disease transmission modelling #MMPMID32405321 Baguelin M; Medley GF; Nightingale ES; O'Reilly KM; Rees EM; Waterlow NR; Wagner M Epidemics 2020[Sep]; 32 (ä): 100395 PMID32405321show ga In this introduction to the Special Issue on methods for modelling of infectious disease epidemiology we provide a commentary and overview of the field. We suggest that the field has been through three revolutions that have focussed on specific methodological developments; disease dynamics and heterogeneity, advanced computing and inference, and complexity and application to the real-world. Infectious disease dynamics and heterogeneity dominated until the 1980s where the use of analytical models illustrated fundamental concepts such as herd immunity. The second revolution embraced the integration of data with models and the increased use of computing. From the turn of the century an emergence of novel datasets enabled improved modelling of real-world complexity. The emergence of more complex data that reflect the real-world heterogeneities in transmission resulted in the development of improved inference methods such as particle filtering. Each of these three revolutions have always kept the understanding of infectious disease spread as its motivation but have been developed through the use of new techniques, tools and the availability of data. We conclude by providing a commentary on what the next revoluition in infectious disease modelling may be. |*Models, Theoretical[MESH] |Communicable Diseases/*epidemiology/*transmission[MESH]Tooling-up for infectious disease transmission modelling (epmc).pdf DeepDyve Pubget Overpricing