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10.1016/j.ijid.2020.10.075 http://scihub22266oqcxt.onion/10.1016/j.ijid.2020.10.075 33130212!7603985!33130212     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Infect+Dis 2021 ; 102 (ä): 463-471 Nephropedia Template TP {{tp|p=33130212|t=2021. Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China |pdf=|usr=}}{{33130212}} gab.com Text Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China JO: Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=33130212 #FOAvip OBJECTIVES: In this data collation study, we aimed to provide a comprehensive database describing the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19) throughout the main provinces in China. METHODS: From mid-January to March 2020, we extracted publicly available data regarding the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted descriptive analyses of the epidemic in the six most-affected provinces. RESULTS: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends differed among provinces. Compared with Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as the local transmission of COVID-19 declined, switching the focus of measures to the testing and quarantine of inbound travellers may have helped to sustain the control of the epidemic. CONCLUSIONS: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database containing these indicators and information regarding control measures is a useful resource for further research and policy planning in response to the COVID-19 epidemic. Twit Text FOAVip Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China ????Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=33130212 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33130212 #FOAvip English Wikipedia <ref>{{Cite pmid|33130212}}</ref> Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China #MMPMID33130212 Fu H; Wang H; Xi X; Boonyasiri A; Wang Y; Hinsley W; Fraser KJ; McCabe R; Olivera Mesa D; Skarp J; Ledda A; Dewe T; Dighe A; Winskill P; van Elsland SL; Ainslie KEC; Baguelin M; Bhatt S; Boyd O; Brazeau NF; Cattarino L; Charles G; Coupland H; Cucunuba ZM; Cuomo-Dannenburg G; Donnelly CA; Dorigatti I; Eales OD; FitzJohn RG; Flaxman S; Gaythorpe KAM; Ghani AC; Green WD; Hamlet A; Hauck K; Haw DJ; Jeffrey B; Laydon DJ; Lees JA; Mellan T; Mishra S; Nedjati-Gilani G; Nouvellet P; Okell L; Parag KV; Ragonnet-Cronin M; Riley S; Schmit N; Thompson HA; Unwin HJT; Verity R; Vollmer MAC; Volz E; Walker PGT; Walters CE; Watson OJ; Whittaker C; Whittles LK; Imai N; Bhatia S; Ferguson NM Int J Infect Dis 2021[Jan]; 102 (ä): 463-471 PMID33130212show ga OBJECTIVES: In this data collation study, we aimed to provide a comprehensive database describing the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19) throughout the main provinces in China. METHODS: From mid-January to March 2020, we extracted publicly available data regarding the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted descriptive analyses of the epidemic in the six most-affected provinces. RESULTS: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends differed among provinces. Compared with Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as the local transmission of COVID-19 declined, switching the focus of measures to the testing and quarantine of inbound travellers may have helped to sustain the control of the epidemic. CONCLUSIONS: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database containing these indicators and information regarding control measures is a useful resource for further research and policy planning in response to the COVID-19 epidemic. |*SARS-CoV-2[MESH] |COVID-19/*epidemiology/prevention & control[MESH] |China/epidemiology[MESH] |Contact Tracing[MESH] |Databases, Factual[MESH]Database of epidemic trends and control measures during the first wave(epmc).pdf DeepDyve Pubget Overpricing