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10.2196/18933 http://scihub22266oqcxt.onion/10.2196/18933 33629957!7945976!33629957     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33629957&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       JMIR+Med+Inform 2021 ; 9 (3): e18933 Nephropedia Template TP {{tp|p=33629957|t=2021. Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study |pdf=|usr=}}{{33629957}} gab.com Text Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study JO: JMIR Med Inform http://www.kidney.de/pget.php?&tw=1&pmid=33629957 #FOAvip BACKGROUND: COVID-19 has been declared a worldwide emergency and a pandemic by the World Health Organization. It started in China in December 2019, and it rapidly spread throughout Italy, which was the most affected country after China. The pandemic affected all countries with similarly negative effects on the population and health care structures. OBJECTIVE: The evolution of the COVID-19 infections and the way such a phenomenon can be characterized in terms of resources and planning has to be considered. One of the most critical resources has been intensive care units (ICUs) with respect to the infection trend and critical hospitalization. METHODS: We propose a model to estimate the needed number of places in ICUs during the most acute phase of the infection. We also define a scalable geographic model to plan emergency and future management of patients with COVID-19 by planning their reallocation in health structures of other regions. RESULTS: We applied and assessed the prediction method both at the national and regional levels. ICU bed prediction was tested with respect to real data provided by the Italian government. We showed that our model is able to predict, with a reliable error in terms of resource complexity, estimation parameters used in health care structures. In addition, the proposed method is scalable at different geographic levels. This is relevant for pandemics such as COVID-19, which has shown different case incidences even among northern and southern Italian regions. CONCLUSIONS: Our contribution can be useful for decision makers to plan resources to guarantee patient management, but it can also be considered as a reference model for potential upcoming waves of COVID-19 and similar emergency situations. Twit Text FOAVip Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study ????JMIR Med Inform http://www.kidney.de/pget.php?&tw=1&pmid=33629957 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33629957 #FOAvip English Wikipedia <ref>{{Cite pmid|33629957}}</ref> Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study #MMPMID33629957 Guzzi PH; Tradigo G; Veltri P JMIR Med Inform 2021[Mar]; 9 (3): e18933 PMID33629957show ga BACKGROUND: COVID-19 has been declared a worldwide emergency and a pandemic by the World Health Organization. It started in China in December 2019, and it rapidly spread throughout Italy, which was the most affected country after China. The pandemic affected all countries with similarly negative effects on the population and health care structures. OBJECTIVE: The evolution of the COVID-19 infections and the way such a phenomenon can be characterized in terms of resources and planning has to be considered. One of the most critical resources has been intensive care units (ICUs) with respect to the infection trend and critical hospitalization. METHODS: We propose a model to estimate the needed number of places in ICUs during the most acute phase of the infection. We also define a scalable geographic model to plan emergency and future management of patients with COVID-19 by planning their reallocation in health structures of other regions. RESULTS: We applied and assessed the prediction method both at the national and regional levels. ICU bed prediction was tested with respect to real data provided by the Italian government. We showed that our model is able to predict, with a reliable error in terms of resource complexity, estimation parameters used in health care structures. In addition, the proposed method is scalable at different geographic levels. This is relevant for pandemics such as COVID-19, which has shown different case incidences even among northern and southern Italian regions. CONCLUSIONS: Our contribution can be useful for decision makers to plan resources to guarantee patient management, but it can also be considered as a reference model for potential upcoming waves of COVID-19 and similar emergency situations. ?Regional Resource Assessment During the COVID-19 Pandemic in Italy: Mo(epmc).pdf DeepDyve Pubget Overpricing