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10.1371/journal.pone.0238214 http://scihub22266oqcxt.onion/10.1371/journal.pone.0238214 32946442!7500629!32946442     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32946442&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 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2020 ; 15 (9): e0238214 Nephropedia Template TP {{tp|p=32946442|t=2020. Assessing the spread of COVID-19 in Brazil: Mobility, morbidity and social vulnerability |pdf=|usr=}}{{32946442}} gab.com Text Assessing the spread of COVID-19 in Brazil: Mobility, morbidity and social vulnerability JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32946442 #FOAvip Brazil detected community transmission of COVID-19 on March 13, 2020. In this study we identified which areas in the country were the most vulnerable for COVID-19, both in terms of the risk of arrival of cases, the risk of sustained transmission and their social vulnerability. Probabilistic models were used to calculate the probability of COVID-19 spread from Sao Paulo and Rio de Janeiro, the initial hotspots, using mobility data from the pre-epidemic period, while multivariate cluster analysis of socio-economic indices was done to identify areas with similar social vulnerability. The results consist of a series of maps of effective distance, outbreak probability, hospital capacity and social vulnerability. They show areas in the North and Northeast with high risk of COVID-19 outbreak that are also highly socially vulnerable. Later, these areas would be found the most severely affected. The maps produced were sent to health authorities to aid in their efforts to prioritize actions such as resource allocation to mitigate the effects of the pandemic. In the discussion, we address how predictions compared to the observed dynamics of the disease. Twit Text FOAVip Assessing the spread of COVID-19 in Brazil: Mobility, morbidity and social vulnerability ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32946442 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32946442 #FOAvip English Wikipedia <ref>{{Cite pmid|32946442}}</ref> Assessing the spread of COVID-19 in Brazil: Mobility, morbidity and social vulnerability #MMPMID32946442 Coelho FC; Lana RM; Cruz OG; Villela DAM; Bastos LS; Pastore Y Piontti A; Davis JT; Vespignani A; Codeco CT; Gomes MFC PLoS One 2020[]; 15 (9): e0238214 PMID32946442show ga Brazil detected community transmission of COVID-19 on March 13, 2020. In this study we identified which areas in the country were the most vulnerable for COVID-19, both in terms of the risk of arrival of cases, the risk of sustained transmission and their social vulnerability. Probabilistic models were used to calculate the probability of COVID-19 spread from Sao Paulo and Rio de Janeiro, the initial hotspots, using mobility data from the pre-epidemic period, while multivariate cluster analysis of socio-economic indices was done to identify areas with similar social vulnerability. The results consist of a series of maps of effective distance, outbreak probability, hospital capacity and social vulnerability. They show areas in the North and Northeast with high risk of COVID-19 outbreak that are also highly socially vulnerable. Later, these areas would be found the most severely affected. The maps produced were sent to health authorities to aid in their efforts to prioritize actions such as resource allocation to mitigate the effects of the pandemic. In the discussion, we address how predictions compared to the observed dynamics of the disease. |*Betacoronavirus[MESH] |*Models, Theoretical[MESH] |Brazil/epidemiology[MESH] |COVID-19[MESH] |Cluster Analysis[MESH] |Coronavirus Infections/epidemiology/*transmission[MESH] |Disease Outbreaks/statistics & numerical data[MESH] |Forecasting/methods[MESH] |Humans[MESH] |Morbidity/*trends[MESH] |Pandemics[MESH] |Pneumonia, Viral/epidemiology/*transmission[MESH] |SARS-CoV-2[MESH]Assessing the spread of COVID-19 in Brazil: Mobility, morbidity and so(epmc).pdf DeepDyve Pubget Overpricing