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10.7717/peerj.9421 http://scihub22266oqcxt.onion/10.7717/peerj.9421 32612894!7319030!32612894     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       PeerJ 2020 ; 8 (ä): e9421 Nephropedia Template TP {{tp|p=32612894|t=2020. Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies |pdf=|usr=}}{{32612894}} gab.com Text Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies JO: PeerJ http://www.kidney.de/pget.php?&tw=1&pmid=32612894 #FOAvip The main objective of the present article is twofold: first, to model the fatality curves of the COVID-19 disease, as represented by the cumulative number of deaths as a function of time; and second, to use the corresponding mathematical model to study the effectiveness of possible intervention strategies. We applied the Richards growth model (RGM) to the COVID-19 fatality curves from several countries, where we used the data from the Johns Hopkins University database up to May 8, 2020. Countries selected for analysis with the RGM were China, France, Germany, Iran, Italy, South Korea, and Spain. The RGM was shown to describe very well the fatality curves of China, which is in a late stage of the COVID-19 outbreak, as well as of the other above countries, which supposedly are in the middle or towards the end of the outbreak at the time of this writing. We also analysed the case of Brazil, which is in an initial sub-exponential growth regime, and so we used the generalised growth model which is more appropriate for such cases. An analytic formula for the efficiency of intervention strategies within the context of the RGM is derived. Our findings show that there is only a narrow window of opportunity, after the onset of the epidemic, during which effective countermeasures can be taken. We applied our intervention model to the COVID-19 fatality curve of Italy of the outbreak to illustrate the effect of several possible interventions. Twit Text FOAVip Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies ????PeerJ http://www.kidney.de/pget.php?&tw=1&pmid=32612894 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32612894 #FOAvip English Wikipedia <ref>{{Cite pmid|32612894}}</ref> Modelling fatality curves of COVID-19 and the effectiveness of intervention strategies #MMPMID32612894 Vasconcelos GL; Macedo AMS; Ospina R; Almeida FAG; Duarte-Filho GC; Brum AA; Souza ICL PeerJ 2020[]; 8 (ä): e9421 PMID32612894show ga The main objective of the present article is twofold: first, to model the fatality curves of the COVID-19 disease, as represented by the cumulative number of deaths as a function of time; and second, to use the corresponding mathematical model to study the effectiveness of possible intervention strategies. We applied the Richards growth model (RGM) to the COVID-19 fatality curves from several countries, where we used the data from the Johns Hopkins University database up to May 8, 2020. Countries selected for analysis with the RGM were China, France, Germany, Iran, Italy, South Korea, and Spain. The RGM was shown to describe very well the fatality curves of China, which is in a late stage of the COVID-19 outbreak, as well as of the other above countries, which supposedly are in the middle or towards the end of the outbreak at the time of this writing. We also analysed the case of Brazil, which is in an initial sub-exponential growth regime, and so we used the generalised growth model which is more appropriate for such cases. An analytic formula for the efficiency of intervention strategies within the context of the RGM is derived. Our findings show that there is only a narrow window of opportunity, after the onset of the epidemic, during which effective countermeasures can be taken. We applied our intervention model to the COVID-19 fatality curve of Italy of the outbreak to illustrate the effect of several possible interventions. äModelling fatality curves of COVID-19 and the effectiveness of interve(epmc).pdf DeepDyve Pubget Overpricing