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10.3389/fpubh.2020.569669 http://scihub22266oqcxt.onion/10.3389/fpubh.2020.569669 33014985!7494973!33014985     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       Front+Public+Health 2020 ; 8 (ä): 569669 Nephropedia Template TP {{tp|p=33014985|t=2020. Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic |pdf=|usr=}}{{33014985}} gab.com Text Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic JO: Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33014985 #FOAvip The COVID-19 outbreak was first declared an international public health, and it was later deemed a pandemic. In most countries, the COVID-19 incidence curve rises sharply over a short period of time, suggesting a transition from a disease-free (or low-burden disease) equilibrium state to a sustained infected (or high-burden disease) state. Such a transition is often known to exhibit characteristics of "critical slowing down." Critical slowing down can be, in general, successfully detected using many statistical measures, such as variance, lag-1 autocorrelation, density ratio, and skewness. Here, we report an empirical test of this phenomena on the COVID-19 datasets of nine countries, including India, China, and the United States. For most of the datasets, increases in variance and autocorrelation predict the onset of a critical transition. Our analysis suggests two key features in predicting the COVID-19 incidence curve for a specific country: (a) the timing of strict social distancing and/or lockdown interventions implemented and (b) the fraction of a nation's population being affected by COVID-19 at that time. Furthermore, using satellite data of nitrogen dioxide as an indicator of lockdown efficacy, we found that countries where lockdown was implemented early and firmly have been successful in reducing COVID-19 spread. These results are essential for designing effective strategies to control the spread/resurgence of infectious pandemics. Twit Text FOAVip Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic ????Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33014985 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33014985 #FOAvip English Wikipedia <ref>{{Cite pmid|33014985}}</ref> Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic #MMPMID33014985 Kaur T; Sarkar S; Chowdhury S; Sinha SK; Jolly MK; Dutta PS Front Public Health 2020[]; 8 (ä): 569669 PMID33014985show ga The COVID-19 outbreak was first declared an international public health, and it was later deemed a pandemic. In most countries, the COVID-19 incidence curve rises sharply over a short period of time, suggesting a transition from a disease-free (or low-burden disease) equilibrium state to a sustained infected (or high-burden disease) state. Such a transition is often known to exhibit characteristics of "critical slowing down." Critical slowing down can be, in general, successfully detected using many statistical measures, such as variance, lag-1 autocorrelation, density ratio, and skewness. Here, we report an empirical test of this phenomena on the COVID-19 datasets of nine countries, including India, China, and the United States. For most of the datasets, increases in variance and autocorrelation predict the onset of a critical transition. Our analysis suggests two key features in predicting the COVID-19 incidence curve for a specific country: (a) the timing of strict social distancing and/or lockdown interventions implemented and (b) the fraction of a nation's population being affected by COVID-19 at that time. Furthermore, using satellite data of nitrogen dioxide as an indicator of lockdown efficacy, we found that countries where lockdown was implemented early and firmly have been successful in reducing COVID-19 spread. These results are essential for designing effective strategies to control the spread/resurgence of infectious pandemics. |*COVID-19[MESH] |*Pandemics[MESH] |China/epidemiology[MESH] |Communicable Disease Control[MESH] |Humans[MESH] |India/epidemiology[MESH] |SARS-CoV-2[MESH]Anticipating the Novel Coronavirus Disease (COVID-19) Pandemic (epmc).pdf DeepDyve Pubget Overpricing