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10.1016/j.bbrc.2020.12.010 http://scihub22266oqcxt.onion/10.1016/j.bbrc.2020.12.010 33342518!7834884!33342518     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33342518&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 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biochem+Biophys+Res+Commun 2021 ; 538 (ä): 244-252 Nephropedia Template TP {{tp|p=33342518|t=2021. A perspective on early detection systems models for COVID-19 spreading |pdf=|usr=}}{{33342518}} gab.com Text A perspective on early detection systems models for COVID-19 spreading JO: Biochem Biophys Res Commun http://www.kidney.de/pget.php?&tw=1&pmid=33342518 #FOAvip The ongoing COVID-19 epidemic highlights the need for effective tools capable of predicting the onset of infection outbreaks at their early stages. The tracing of confirmed cases and the prediction of the local dynamics of contagion through early indicators are crucial measures to a successful fight against emerging infectious diseases (EID). The proposed framework is model-free and applies Early Warning Detection Systems (EWDS) techniques to detect changes in the territorial spread of infections in the very early stages of onset. This study uses publicly available raw data on the spread of SARS-CoV-2 mainly sourced from the database of the Italian Civil Protection Department. Two distinct EWDS approaches, the Hub-Jones (H&J) and Strozzi-Zaldivar (S&Z), are adapted and applied to the current SARS-CoV-2 outbreak. They promptly generate warning signals and detect the onset of an epidemic at early surveillance stages even if working on the limited daily available, open-source data. Additionally, EWDS S&Z criterion is theoretically validated on the basis of the epidemiological SIR. Discussed EWDS successfully analyze self-accelerating systems, like the SARS-CoV-2 scenario, to precociously identify an epidemic spread through the calculation of onset parameters. This approach can also facilitate early clustering detection, further supporting common fight strategies against the spread of EIDs. Overall, we are presenting an effective tool based on solid scientific and methodological foundations to be used to complement medical actions to contrast the spread of infections such as COVID-19. Twit Text FOAVip A perspective on early detection systems models for COVID-19 spreading ????Biochem Biophys Res Commun http://www.kidney.de/pget.php?&tw=1&pmid=33342518 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33342518 #FOAvip English Wikipedia <ref>{{Cite pmid|33342518}}</ref> A perspective on early detection systems models for COVID-19 spreading #MMPMID33342518 Vianello C; Strozzi F; Mocellin P; Cimetta E; Fabiano B; Manenti F; Pozzi R; Maschio G Biochem Biophys Res Commun 2021[Jan]; 538 (ä): 244-252 PMID33342518show ga The ongoing COVID-19 epidemic highlights the need for effective tools capable of predicting the onset of infection outbreaks at their early stages. The tracing of confirmed cases and the prediction of the local dynamics of contagion through early indicators are crucial measures to a successful fight against emerging infectious diseases (EID). The proposed framework is model-free and applies Early Warning Detection Systems (EWDS) techniques to detect changes in the territorial spread of infections in the very early stages of onset. This study uses publicly available raw data on the spread of SARS-CoV-2 mainly sourced from the database of the Italian Civil Protection Department. Two distinct EWDS approaches, the Hub-Jones (H&J) and Strozzi-Zaldivar (S&Z), are adapted and applied to the current SARS-CoV-2 outbreak. They promptly generate warning signals and detect the onset of an epidemic at early surveillance stages even if working on the limited daily available, open-source data. Additionally, EWDS S&Z criterion is theoretically validated on the basis of the epidemiological SIR. Discussed EWDS successfully analyze self-accelerating systems, like the SARS-CoV-2 scenario, to precociously identify an epidemic spread through the calculation of onset parameters. This approach can also facilitate early clustering detection, further supporting common fight strategies against the spread of EIDs. Overall, we are presenting an effective tool based on solid scientific and methodological foundations to be used to complement medical actions to contrast the spread of infections such as COVID-19. |*Epidemiological Monitoring[MESH] |*SARS-CoV-2[MESH] |COVID-19/*epidemiology/*prevention & control[MESH] |Disease Outbreaks/*prevention & control[MESH] |Humans[MESH]A perspective on early detection systems models for COVID-19 spreading(epmc).pdf DeepDyve Pubget Overpricing