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10.3389/fpubh.2020.00440 http://scihub22266oqcxt.onion/10.3389/fpubh.2020.00440 32850611!7430161!32850611     free     free     free       Front+Public+Health 2020 ; 8 (ä): 440 Nephropedia Template TP {{tp|p=32850611|t=2020. Global and Temporal COVID-19 Risk Evaluation |pdf=|usr=}}{{32850611}} gab.com Text Global and Temporal COVID-19 Risk Evaluation JO: Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32850611 #FOAvip The COVID-19 pandemic has caused unprecedented crisis across the world, with many countries struggling with the pandemic. In order to understand how each country is impacted by the virus and assess the risk on a global scale we present a regression based analysis using two pre-existing indexes, namely the Inform and Infectious Disease Vulnerability Index, in conjunction with the number of elderly living in the population. Further we introduce a temporal layer in our modeling by incorporating the stringency level employed by each country over a period of 6 time intervals. Our results show that the indexes and level of stringency are not ideally suited for explaining variation in COVID-19 risk, however the ratio of elderly in the population is a stand out indicator in terms of its predictive power for mortality risk. In conclusion, we discuss how such modeling approaches can assist public health policy. Twit Text FOAVip Global and Temporal COVID-19 Risk Evaluation ????Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32850611 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32850611 #FOAvip English Wikipedia <ref>{{Cite pmid|32850611}}</ref> Global and Temporal COVID-19 Risk Evaluation #MMPMID32850611 Arsalan M; Mubin O; Alnajjar F; Alsinglawi B; Zaki N Front Public Health 2020[]; 8 (ä): 440 PMID32850611show ga The COVID-19 pandemic has caused unprecedented crisis across the world, with many countries struggling with the pandemic. In order to understand how each country is impacted by the virus and assess the risk on a global scale we present a regression based analysis using two pre-existing indexes, namely the Inform and Infectious Disease Vulnerability Index, in conjunction with the number of elderly living in the population. Further we introduce a temporal layer in our modeling by incorporating the stringency level employed by each country over a period of 6 time intervals. Our results show that the indexes and level of stringency are not ideally suited for explaining variation in COVID-19 risk, however the ratio of elderly in the population is a stand out indicator in terms of its predictive power for mortality risk. In conclusion, we discuss how such modeling approaches can assist public health policy. |*Pandemics[MESH] |*Risk Assessment[MESH] |Aged[MESH] |COVID-19/*epidemiology[MESH] |Health Policy[MESH] |Humans[MESH]Global and Temporal COVID-19 Risk Evaluation (epmc).pdf DeepDyve Pubget Overpricing