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10.1016/j.chaos.2020.110046 http://scihub22266oqcxt.onion/10.1016/j.chaos.2020.110046 C7315964/?report=reader!7315964 !32834601     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\32834601 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Chaos+Solitons+Fractals 2020 ; 139 (ä): 110046 Nephropedia Template TP {{tp|p=32834601 |t=2020. Forecasting COVID-19 pandemic: A data-driven analysis |pdf=|usr=}}{{32834601 }} gab.com Text Forecasting COVID-19 pandemic: A data-driven analysis JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834601 #FOAvip In this paper, a new Susceptible-Exposed-Symptomatic Infectious-Asymptomatic Infectious-Quarantined-Hospitalized-Recovered-Dead (SEI(D)I(U)QHRD) deterministic compartmental model has been proposed and calibrated for interpreting the transmission dynamics of the novel coronavirus disease (COVID-19). The purpose of this study is to give tentative predictions of the epidemic peak for Russia, Brazil, India and Bangladesh which could become the next COVID-19 hotspots in no time by using a newly developed algorithm based on well-known Trust-region-reflective (TRR) algorithm, which is one of the robust real-time optimization techniques. Based on the publicly available epidemiological data from late January until 10 May, it has been estimated that the number of daily new symptomatic infectious cases for the above mentioned countries could reach the peak around the middle of June with the peak size of ???15, 774 (95% CI, 12,814-16,734) symptomatic infectious cases in Russia, ???26, 449 (95% CI, 25,489-31,409) cases in Brazil, ???9, 504 (95% CI, 8,378-13,630) cases in India and ???2, 209 (95% CI, 2,078-2,840) cases in Bangladesh if current epidemic trends hold. As of May 11, 2020, incorporating the infectiousness capability of asymptomatic carriers, our analysis estimates the value of the basic reproductive number (R (0)) was found to be ???4.234 (95% CI, 3.764-4.7) in Russia, ???5.347 (95% CI, 4.737-5.95) in Brazil, ???5.218 (95% CI, 4.56-5.81) in India, ???4.649 (95% CI, 4.17-5.12) in the United Kingdom and ???3.53 (95% CI, 3.12-3.94) in Bangladesh. Moreover, Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC) which is a global sensitivity analysis (GSA) method has been applied to quantify the uncertainty of our model mechanisms, which elucidates that for Russia, the recovery rate of undetected asymptomatic carriers, the rate of getting home-quarantined or self-quarantined and the transition rate from quarantined class to susceptible class are the most influential parameters, whereas the rate of getting home-quarantined or self-quarantined and the inverse of the COVID-19 incubation period are highly sensitive parameters in Brazil, India, Bangladesh and the United Kingdom which could significantly affect the transmission dynamics of the novel coronavirus disease (COVID-19). Our analysis also suggests that relaxing social distancing restrictions too quickly could exacerbate the epidemic outbreak in the above-mentioned countries. Twit Text FOAVip Forecasting COVID-19 pandemic: A data-driven analysis ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=32834601 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32834601 #FOAvip English Wikipedia <ref>{{Cite pmid|32834601 }}</ref> Forecasting COVID-19 pandemic: A data-driven analysis #MMPMID32834601 Nabi KN Chaos Solitons Fractals 2020[Oct]; 139 (ä): 110046 PMID32834601 show ga In this paper, a new Susceptible-Exposed-Symptomatic Infectious-Asymptomatic Infectious-Quarantined-Hospitalized-Recovered-Dead (SEI(D)I(U)QHRD) deterministic compartmental model has been proposed and calibrated for interpreting the transmission dynamics of the novel coronavirus disease (COVID-19). The purpose of this study is to give tentative predictions of the epidemic peak for Russia, Brazil, India and Bangladesh which could become the next COVID-19 hotspots in no time by using a newly developed algorithm based on well-known Trust-region-reflective (TRR) algorithm, which is one of the robust real-time optimization techniques. Based on the publicly available epidemiological data from late January until 10 May, it has been estimated that the number of daily new symptomatic infectious cases for the above mentioned countries could reach the peak around the middle of June with the peak size of ???15, 774 (95% CI, 12,814-16,734) symptomatic infectious cases in Russia, ???26, 449 (95% CI, 25,489-31,409) cases in Brazil, ???9, 504 (95% CI, 8,378-13,630) cases in India and ???2, 209 (95% CI, 2,078-2,840) cases in Bangladesh if current epidemic trends hold. As of May 11, 2020, incorporating the infectiousness capability of asymptomatic carriers, our analysis estimates the value of the basic reproductive number (R (0)) was found to be ???4.234 (95% CI, 3.764-4.7) in Russia, ???5.347 (95% CI, 4.737-5.95) in Brazil, ???5.218 (95% CI, 4.56-5.81) in India, ???4.649 (95% CI, 4.17-5.12) in the United Kingdom and ???3.53 (95% CI, 3.12-3.94) in Bangladesh. Moreover, Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC) which is a global sensitivity analysis (GSA) method has been applied to quantify the uncertainty of our model mechanisms, which elucidates that for Russia, the recovery rate of undetected asymptomatic carriers, the rate of getting home-quarantined or self-quarantined and the transition rate from quarantined class to susceptible class are the most influential parameters, whereas the rate of getting home-quarantined or self-quarantined and the inverse of the COVID-19 incubation period are highly sensitive parameters in Brazil, India, Bangladesh and the United Kingdom which could significantly affect the transmission dynamics of the novel coronavirus disease (COVID-19). Our analysis also suggests that relaxing social distancing restrictions too quickly could exacerbate the epidemic outbreak in the above-mentioned countries. äForecasting COVID-19 pandemic: A data-driven analysis (epmc).pdf DeepDyve Pubget Overpricing