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10.1029/2020GH000303 http://scihub22266oqcxt.onion/10.1029/2020GH000303 33024909!7532285!33024909     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\33024909.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Geohealth 2020 ; 4 (10): e2020GH000303 Nephropedia Template TP {{tp|p=33024909|t=2020. Understanding the Epidemic Course in Order to Improve Epidemic Forecasting |pdf=|usr=}}{{33024909}} gab.com Text Understanding the Epidemic Course in Order to Improve Epidemic Forecasting JO: Geohealth http://www.kidney.de/pget.php?&tw=1&pmid=33024909 #FOAvip The epidemic course of the severe acute respiratory syndrome (SARS) has been differently divided according to its transmission pattern and the infection and mortality status. Unfortunately, such efforts for the coronavirus disease 2019 (COVID-19) have been lacking. Does every epidemic have a unique epidemic course? Can we coordinate two arbitrary courses into an integrated course, which could better reflect a common real-world progression pattern of the epidemics? To what degree can such arbitrary divisions help predict future trends of the COVID-19 pandemic and future epidemics? Spatial lifecourse epidemiology provides a new perspective to understand the course of epidemics, especially pandemics, and a new toolkit to predict the course of future epidemics on the basis of big data. In the present data-driven era, data should be integrated to inform us how the epidemic is transmitting at the present moment, how it will transmit at the next moment, and which interventions would be most cost-effective to curb the epidemic. Both national and international legislations are needed to facilitate the integration of relevant policies of data sharing and confidentiality protection into the current pandemic preparedness guidelines. Twit Text FOAVip Understanding the Epidemic Course in Order to Improve Epidemic Forecasting ????Geohealth http://www.kidney.de/pget.php?&tw=1&pmid=33024909 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33024909 #FOAvip English Wikipedia <ref>{{Cite pmid|33024909}}</ref> Understanding the Epidemic Course in Order to Improve Epidemic Forecasting #MMPMID33024909 Jia P Geohealth 2020[Oct]; 4 (10): e2020GH000303 PMID33024909show ga The epidemic course of the severe acute respiratory syndrome (SARS) has been differently divided according to its transmission pattern and the infection and mortality status. Unfortunately, such efforts for the coronavirus disease 2019 (COVID-19) have been lacking. Does every epidemic have a unique epidemic course? Can we coordinate two arbitrary courses into an integrated course, which could better reflect a common real-world progression pattern of the epidemics? To what degree can such arbitrary divisions help predict future trends of the COVID-19 pandemic and future epidemics? Spatial lifecourse epidemiology provides a new perspective to understand the course of epidemics, especially pandemics, and a new toolkit to predict the course of future epidemics on the basis of big data. In the present data-driven era, data should be integrated to inform us how the epidemic is transmitting at the present moment, how it will transmit at the next moment, and which interventions would be most cost-effective to curb the epidemic. Both national and international legislations are needed to facilitate the integration of relevant policies of data sharing and confidentiality protection into the current pandemic preparedness guidelines. äUnderstanding the Epidemic Course in Order to Improve Epidemic Forecas(epmc).pdf DeepDyve Pubget Overpricing