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10.1017/S0950268820001065 http://scihub22266oqcxt.onion/10.1017/S0950268820001065 32418559!7251289!32418559     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 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Epidemiol+Infect 2020 ; 148 (ä): e97 Nephropedia Template TP {{tp|p=32418559|t=2020. Back-projection of COVID-19 diagnosis counts to assess infection incidence and control measures: analysis of Australian data |pdf=|usr=}}{{32418559}} gab.com Text Back-projection of COVID-19 diagnosis counts to assess infection incidence and control measures: analysis of Australian data JO: Epidemiol Infect http://www.kidney.de/pget.php?&tw=1&pmid=32418559 #FOAvip Back-projection is an epidemiological analysis method that was developed to estimate HIV incidence using surveillance data on AIDS diagnoses. It was used extensively during the 1990s for this purpose as well as in other epidemiological contexts. Surveillance data on COVID-19 diagnoses can be analysed by the method of back-projection using information about the probability distribution of the time between infection and diagnosis, which is primarily determined by the incubation period. This paper demonstrates the value of such analyses using daily diagnoses from Australia. It is shown how back-projection can be used to assess the pattern of COVID-19 infection incidence over time and to assess the impact of control measures by investigating their temporal association with changes in incidence patterns. For Australia, these analyses reveal that peak infection incidence coincided with the introduction of border closures and social distancing restrictions, while the introduction of subsequent social distancing measures coincided with a continuing decline in incidence to very low levels. These associations were not directly discernible from the daily diagnosis counts, which continued to increase after the first stage of control measures. It is estimated that a one week delay in peak incidence would have led to a fivefold increase in total infections. Furthermore, at the height of the outbreak, half to three-quarters of all infections remained undiagnosed. Automated data analytics of routinely collected surveillance data are a valuable monitoring tool for the COVID-19 pandemic and may be useful for calibrating transmission dynamics models. Twit Text FOAVip Back-projection of COVID-19 diagnosis counts to assess infection incidence and control measures: analysis of Australian data ????Epidemiol Infect http://www.kidney.de/pget.php?&tw=1&pmid=32418559 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32418559 #FOAvip English Wikipedia <ref>{{Cite pmid|32418559}}</ref> Back-projection of COVID-19 diagnosis counts to assess infection incidence and control measures: analysis of Australian data #MMPMID32418559 Marschner IC Epidemiol Infect 2020[May]; 148 (ä): e97 PMID32418559show ga Back-projection is an epidemiological analysis method that was developed to estimate HIV incidence using surveillance data on AIDS diagnoses. It was used extensively during the 1990s for this purpose as well as in other epidemiological contexts. Surveillance data on COVID-19 diagnoses can be analysed by the method of back-projection using information about the probability distribution of the time between infection and diagnosis, which is primarily determined by the incubation period. This paper demonstrates the value of such analyses using daily diagnoses from Australia. It is shown how back-projection can be used to assess the pattern of COVID-19 infection incidence over time and to assess the impact of control measures by investigating their temporal association with changes in incidence patterns. For Australia, these analyses reveal that peak infection incidence coincided with the introduction of border closures and social distancing restrictions, while the introduction of subsequent social distancing measures coincided with a continuing decline in incidence to very low levels. These associations were not directly discernible from the daily diagnosis counts, which continued to increase after the first stage of control measures. It is estimated that a one week delay in peak incidence would have led to a fivefold increase in total infections. Furthermore, at the height of the outbreak, half to three-quarters of all infections remained undiagnosed. Automated data analytics of routinely collected surveillance data are a valuable monitoring tool for the COVID-19 pandemic and may be useful for calibrating transmission dynamics models. |Asymptomatic Infections/epidemiology[MESH] |Australia/epidemiology[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Communicable Disease Control/methods/statistics & numerical data[MESH] |Coronavirus Infections/diagnosis/*epidemiology[MESH] |Humans[MESH] |Incidence[MESH] |Models, Statistical[MESH] |Pandemics/statistics & numerical data[MESH] |Pneumonia, Viral/diagnosis/*epidemiology[MESH] |Probability[MESH]Back-projection of COVID-19 diagnosis counts to assess infection incid(epmc).pdf DeepDyve Pubget Overpricing