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10.1016/j.amj.2020.05.011 http://scihub22266oqcxt.onion/10.1016/j.amj.2020.05.011 32425475!7229980!32425475     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32425475&cmd=llinks): Failed to open stream: HTTP request failed! 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Royal Flying Doctor Service Coronavirus Disease 2019 Activity and Surge Modeling in Australia |pdf=|usr=}}{{32425475}} gab.com Text Royal Flying Doctor Service Coronavirus Disease 2019 Activity and Surge Modeling in Australia JO: Air Med J http://www.kidney.de/pget.php?&tw=1&pmid=32425475 #FOAvip OBJECTIVE: There is a coronavirus disease 2019 (COVID-19) pandemic. We aimed to describe the characteristics of patients transported by the Royal Flying Doctor Service (RFDS) for confirmed or suspected COVID-19 and to investigate the surge capacity of and operational implications for the RFDS in dealing with COVID-19. METHODS: This was a prospective cohort study. To determine the characteristics of patients transported for confirmed or suspected COVID-19, we included patient data from February 2, 2020, to May 6, 2020. To investigate the surge capacity and operational implications for the RFDS in dealing with COVID-19, we built and validated an interactive operations area-level discrete event simulation decision support model underpinned by RFDS air medical activity data from 2015 to 2019 (4 years). This model was subsequently used in a factorial in silico experiment to systematically investigate both the supply of RFDS air medical services and the increased rates of demand for these services for diseases of the respiratory system. RESULTS: The RFDS conducted 291 patient episodes of care for confirmed or suspected COVID-19. This included 288 separate patients, including 136 men and 119 women (sex missing?=?33), with a median age of 62.0 years (interquartile range, 43.5-74.9 years). The simulation decision support model we developed is capable of providing dynamic and real-time support for RFDS decision makers in understanding the system's performance under uncertain COVID-19 demand. With increased COVID-19-related demand, the ability of the RFDS to cope will be driven by the number of aircraft available. The simulation model provided each aviation section with estimated numbers of aircraft required to meet a range of anticipated demands. CONCLUSION: Despite the lack of certainty in the actual level of COVID-19-related demand for RFDS services, modeling demonstrates that the robustness of meeting such demand increases with the number of operational and medically staffed aircraft. Twit Text FOAVip Royal Flying Doctor Service Coronavirus Disease 2019 Activity and Surge Modeling in Australia ????Air Med J http://www.kidney.de/pget.php?&tw=1&pmid=32425475 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32425475 #FOAvip English Wikipedia <ref>{{Cite pmid|32425475}}</ref> Royal Flying Doctor Service Coronavirus Disease 2019 Activity and Surge Modeling in Australia #MMPMID32425475 Gardiner FW; Johns H; Bishop L; Churilov L Air Med J 2020[Sep]; 39 (5): 404-409 PMID32425475show ga OBJECTIVE: There is a coronavirus disease 2019 (COVID-19) pandemic. We aimed to describe the characteristics of patients transported by the Royal Flying Doctor Service (RFDS) for confirmed or suspected COVID-19 and to investigate the surge capacity of and operational implications for the RFDS in dealing with COVID-19. METHODS: This was a prospective cohort study. To determine the characteristics of patients transported for confirmed or suspected COVID-19, we included patient data from February 2, 2020, to May 6, 2020. To investigate the surge capacity and operational implications for the RFDS in dealing with COVID-19, we built and validated an interactive operations area-level discrete event simulation decision support model underpinned by RFDS air medical activity data from 2015 to 2019 (4 years). This model was subsequently used in a factorial in silico experiment to systematically investigate both the supply of RFDS air medical services and the increased rates of demand for these services for diseases of the respiratory system. RESULTS: The RFDS conducted 291 patient episodes of care for confirmed or suspected COVID-19. This included 288 separate patients, including 136 men and 119 women (sex missing?=?33), with a median age of 62.0 years (interquartile range, 43.5-74.9 years). The simulation decision support model we developed is capable of providing dynamic and real-time support for RFDS decision makers in understanding the system's performance under uncertain COVID-19 demand. With increased COVID-19-related demand, the ability of the RFDS to cope will be driven by the number of aircraft available. The simulation model provided each aviation section with estimated numbers of aircraft required to meet a range of anticipated demands. CONCLUSION: Despite the lack of certainty in the actual level of COVID-19-related demand for RFDS services, modeling demonstrates that the robustness of meeting such demand increases with the number of operational and medically staffed aircraft. |*Computer Simulation[MESH] |*Surge Capacity[MESH] |Adult[MESH] |Aged[MESH] |Air Ambulances/*statistics & numerical data[MESH] |Australia/epidemiology[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Cohort Studies[MESH] |Coronavirus Infections/*epidemiology[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Patient Transfer/*statistics & numerical data[MESH] |Pneumonia, Viral/*epidemiology[MESH] |Prospective Studies[MESH]Royal Flying Doctor Service Coronavirus Disease 2019 Activity and Surg(epmc).pdf DeepDyve Pubget Overpricing