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10.1016/j.ijid.2020.05.049 http://scihub22266oqcxt.onion/10.1016/j.ijid.2020.05.049 32470603!7250076!32470603     free     free     free Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Infect+Dis 2020 ; 96 (ä): 519-523 Nephropedia Template TP {{tp|p=32470603|t=2020. Early network properties of the COVID-19 pandemic - The Chinese scenario |pdf=|usr=}}{{32470603}} gab.com Text Early network properties of the COVID-19 pandemic - The Chinese scenario JO: Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=32470603 #FOAvip OBJECTIVES: To control epidemics, sites more affected by mortality should be identified. METHODS: Defining epidemic nodes as areas that included both most fatalities per time unit and connections, such as highways, geo-temporal Chinese data on the COVID-19 epidemic were investigated with linear, logarithmic, power, growth, exponential, and logistic regression models. A z-test compared the slopes observed. RESULTS: Twenty provinces suspected to act as epidemic nodes were empirically investigated. Five provinces displayed synchronicity, long-distance connections, directionality and assortativity - network properties that helped discriminate epidemic nodes. The rank I node included most fatalities and was activated first. Fewer deaths were reported, later, by rank II and III nodes, while the data from rank I-III nodes exhibited slopes, the data from the remaining provinces did not. The power curve was the best fitting model for all slopes. Because all pairs (rank I vs. rank II, rank I vs. rank III, and rank II vs. rank III) of epidemic nodes differed statistically, rank I-III epidemic nodes were geo-temporally and statistically distinguishable. CONCLUSIONS: The geo-temporal progression of epidemics seems to be highly structured. Epidemic network properties can distinguish regions that differ in mortality. This real-time geo-referenced analysis can inform both decision-makers and clinicians. Twit Text FOAVip Early network properties of the COVID-19 pandemic - The Chinese scenario ????Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=32470603 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32470603 #FOAvip English Wikipedia <ref>{{Cite pmid|32470603}}</ref> Early network properties of the COVID-19 pandemic - The Chinese scenario #MMPMID32470603 Rivas AL; Febles JL; Smith SD; Hoogesteijn AL; Tegos GP; Fasina FO; Hittner JB Int J Infect Dis 2020[Jul]; 96 (ä): 519-523 PMID32470603show ga OBJECTIVES: To control epidemics, sites more affected by mortality should be identified. METHODS: Defining epidemic nodes as areas that included both most fatalities per time unit and connections, such as highways, geo-temporal Chinese data on the COVID-19 epidemic were investigated with linear, logarithmic, power, growth, exponential, and logistic regression models. A z-test compared the slopes observed. RESULTS: Twenty provinces suspected to act as epidemic nodes were empirically investigated. Five provinces displayed synchronicity, long-distance connections, directionality and assortativity - network properties that helped discriminate epidemic nodes. The rank I node included most fatalities and was activated first. Fewer deaths were reported, later, by rank II and III nodes, while the data from rank I-III nodes exhibited slopes, the data from the remaining provinces did not. The power curve was the best fitting model for all slopes. Because all pairs (rank I vs. rank II, rank I vs. rank III, and rank II vs. rank III) of epidemic nodes differed statistically, rank I-III epidemic nodes were geo-temporally and statistically distinguishable. CONCLUSIONS: The geo-temporal progression of epidemics seems to be highly structured. Epidemic network properties can distinguish regions that differ in mortality. This real-time geo-referenced analysis can inform both decision-makers and clinicians. |Betacoronavirus[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Coronavirus Infections/*epidemiology/mortality[MESH] |Humans[MESH] |Logistic Models[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology/mortality[MESH] |SARS-CoV-2[MESH]Early network properties of the COVID-19 pandemic - The Chinese scenar(epmc).pdf DeepDyve Pubget Overpricing