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10.3390/ijerph18094491 http://scihub22266oqcxt.onion/10.3390/ijerph18094491 33922634!8122857!33922634     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 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 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 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Environ+Res+Public+Health 2021 ; 18 (9): ä Nephropedia Template TP {{tp|p=33922634|t=2021. A Non-Linear Biostatistical Graphical Modeling of Preventive Actions and Healthcare Factors in Controlling COVID-19 Pandemic |pdf=|usr=}}{{33922634}} gab.com Text A Non-Linear Biostatistical Graphical Modeling of Preventive Actions and Healthcare Factors in Controlling COVID-19 Pandemic JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33922634 #FOAvip BACKGROUND: With the insurgence of the COVID-19 pandemic, many people died in the past several months, and the situation is ongoing with increasing health, social, and economic panic and vulnerability. As most of the countries relying on different preventive actions to control the outcomes of COVID-19, it is necessary to boost the knowledge about the effectiveness of such actions so that the policymakers take their country-based appropriate actions. This study generates evidence of taking the most impactful actions to combat COVID-19. OBJECTIVE: In order to generate community-based scientific evidence, this study analyzed the outcome of COVID-19 in response to different control measures, healthcare facilities, life expectancy, and prevalent diseases. METHODS: It used more than a hundred countries' data collected from different databases. We performed a comparative graphical analysis with non-linear correlation estimation using R. RESULTS: The reduction of COVID-19 cases is strongly correlated with the earliness of preventive initiation. The apathy of taking nationwide immediate precaution measures has been identified as one of the critical reasons to make the circumstances worse. There is significant non-linear relationship between COVID-19 case fatality and number of physicians (NCC = 0.22; p-value </= 0.001), nurses and midwives (NCC = 0.17; p-value </= 0.001), hospital beds (NCC = 0.20; p-value </= 0.001), life expectancy of both sexes (NCC = 0.22; p-value </= 0.001), life expectancy of female (NCC = 0.27; p-value </= 0.001), and life expectancy of male (NCC = 0.19; p-value </= 0.001). COVID-19 deaths were found to be reduced with increased medical personnel and hospital beds. Interestingly, no association between the comorbidities and severity of COVID-19 was found excluding asthma, cancer, Alzheimer's, and smoking. CONCLUSIONS: Enhancing healthcare facilities and early imposing the control measures could be valuable to prevent the COVID-19 pandemic. No association between COVID-19 and other comorbidities warranted further investigation at the pathobiological level. Twit Text FOAVip A Non-Linear Biostatistical Graphical Modeling of Preventive Actions and Healthcare Factors in Controlling COVID-19 Pandemic ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33922634 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33922634 #FOAvip English Wikipedia <ref>{{Cite pmid|33922634}}</ref> A Non-Linear Biostatistical Graphical Modeling of Preventive Actions and Healthcare Factors in Controlling COVID-19 Pandemic #MMPMID33922634 Abdulla F; Nain Z; Karimuzzaman M; Hossain MM; Rahman A Int J Environ Res Public Health 2021[Apr]; 18 (9): ä PMID33922634show ga BACKGROUND: With the insurgence of the COVID-19 pandemic, many people died in the past several months, and the situation is ongoing with increasing health, social, and economic panic and vulnerability. As most of the countries relying on different preventive actions to control the outcomes of COVID-19, it is necessary to boost the knowledge about the effectiveness of such actions so that the policymakers take their country-based appropriate actions. This study generates evidence of taking the most impactful actions to combat COVID-19. OBJECTIVE: In order to generate community-based scientific evidence, this study analyzed the outcome of COVID-19 in response to different control measures, healthcare facilities, life expectancy, and prevalent diseases. METHODS: It used more than a hundred countries' data collected from different databases. We performed a comparative graphical analysis with non-linear correlation estimation using R. RESULTS: The reduction of COVID-19 cases is strongly correlated with the earliness of preventive initiation. The apathy of taking nationwide immediate precaution measures has been identified as one of the critical reasons to make the circumstances worse. There is significant non-linear relationship between COVID-19 case fatality and number of physicians (NCC = 0.22; p-value |*COVID-19[MESH] |*Pandemics[MESH] |Delivery of Health Care[MESH] |Female[MESH] |Health Personnel[MESH] |Humans[MESH] |Male[MESH]A Non-Linear Biostatistical Graphical Modeling of Preventive Actions a(epmc).pdf DeepDyve Pubget Overpricing