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10.1371/journal.pone.0239175 http://scihub22266oqcxt.onion/10.1371/journal.pone.0239175 32941485!7498003!32941485     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2020 ; 15 (9): e0239175 Nephropedia Template TP {{tp|p=32941485|t=2020. COVID-19 pandemic and Farr s law: A global comparison and prediction of outbreak acceleration and deceleration rates |pdf=|usr=}}{{32941485}} gab.com Text COVID-19 pandemic and Farr s law: A global comparison and prediction of outbreak acceleration and deceleration rates JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32941485 #FOAvip The COVID-19 outbreak has forced most of the global population to lock-down and has put in check the health services all over the world. Current predictive models are complex, region-dependent, and might not be generalized to other countries. However, a 150-year old epidemics law promulgated by William Farr might be useful as a simple arithmetical model (percent increase [R1] and acceleration [R2] of new cases and deaths) to provide a first sight of the epidemic behavior and to detect regions with high predicted dynamics. Thus, this study tested Farr's Law assumptions by modeling COVID-19 data of new cases and deaths. COVID-19 data until April 10, 2020, was extracted from available countries, including income, urban index, and population characteristics. Farr's law first (R1) and second ratio (R2) were calculated. We constructed epidemic curves and predictive models for the available countries and performed ecological correlation analysis between R1 and R2 with demographic data. We extracted data from 210 countries, and it was possible to estimate the ratios of 170 of them. Around 42.94% of the countries were in an initial acceleration phase, while 23.5% already crossed the peak. We predicted a reduction close to zero with wide confidence intervals for 56 countries until June 10 (high-income countries from Asia and Oceania, with strict political actions). There was a significant association between high R1 of deaths and high urban index. Farr's law seems to be a useful model to give an overview of COVID-19 pandemic dynamics. The countries with high dynamics are from Africa and Latin America. Thus, this is a call to urgently prioritize actions in those countries to intensify surveillance, to re-allocate resources, and to build healthcare capacities based on multi-nation collaboration to limit onward transmission and to reduce the future impact on these regions in an eventual second wave. Twit Text FOAVip COVID-19 pandemic and Farr s law: A global comparison and prediction of outbreak acceleration and deceleration rates ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32941485 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32941485 #FOAvip English Wikipedia <ref>{{Cite pmid|32941485}}</ref> COVID-19 pandemic and Farr s law: A global comparison and prediction of outbreak acceleration and deceleration rates #MMPMID32941485 Pacheco-Barrios K; Cardenas-Rojas A; Giannoni-Luza S; Fregni F PLoS One 2020[]; 15 (9): e0239175 PMID32941485show ga The COVID-19 outbreak has forced most of the global population to lock-down and has put in check the health services all over the world. Current predictive models are complex, region-dependent, and might not be generalized to other countries. However, a 150-year old epidemics law promulgated by William Farr might be useful as a simple arithmetical model (percent increase [R1] and acceleration [R2] of new cases and deaths) to provide a first sight of the epidemic behavior and to detect regions with high predicted dynamics. Thus, this study tested Farr's Law assumptions by modeling COVID-19 data of new cases and deaths. COVID-19 data until April 10, 2020, was extracted from available countries, including income, urban index, and population characteristics. Farr's law first (R1) and second ratio (R2) were calculated. We constructed epidemic curves and predictive models for the available countries and performed ecological correlation analysis between R1 and R2 with demographic data. We extracted data from 210 countries, and it was possible to estimate the ratios of 170 of them. Around 42.94% of the countries were in an initial acceleration phase, while 23.5% already crossed the peak. We predicted a reduction close to zero with wide confidence intervals for 56 countries until June 10 (high-income countries from Asia and Oceania, with strict political actions). There was a significant association between high R1 of deaths and high urban index. Farr's law seems to be a useful model to give an overview of COVID-19 pandemic dynamics. The countries with high dynamics are from Africa and Latin America. Thus, this is a call to urgently prioritize actions in those countries to intensify surveillance, to re-allocate resources, and to build healthcare capacities based on multi-nation collaboration to limit onward transmission and to reduce the future impact on these regions in an eventual second wave. |*Betacoronavirus[MESH] |*Models, Biological[MESH] |Africa/epidemiology[MESH] |Asia/epidemiology[MESH] |COVID-19[MESH] |Coronavirus Infections/epidemiology/*prevention & control[MESH] |Forecasting[MESH] |Geography, Medical[MESH] |Humans[MESH] |Incidence[MESH] |Latin America/epidemiology[MESH] |Morbidity/trends[MESH] |Mortality/trends[MESH] |Pandemics/*legislation & jurisprudence/prevention & control[MESH] |Pneumonia, Viral/epidemiology/*prevention & control[MESH] |Population Dynamics[MESH] |SARS-CoV-2[MESH]COVID-19 pandemic and Farr s law: A global comparison and prediction o(epmc).pdf DeepDyve Pubget Overpricing