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10.1002/sim.9020 http://scihub22266oqcxt.onion/10.1002/sim.9020 33973656!8242489!33973656     free     free     free       Stat+Med 2021 ; 40 (18): 4150-4160 Nephropedia Template TP {{tp|p=33973656|t=2021. Monitoring COVID-19 contagion growth |pdf=|usr=}}{{33973656}} gab.com Text Monitoring COVID-19 contagion growth JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=33973656 #FOAvip We present a statistical model that can be employed to monitor the time evolution of the COVID-19 contagion curve and the associated reproduction rate. The model is a Poisson autoregression of the daily new observed cases and dynamically adapt its estimates to explain the evolution of contagion in terms of a short-term and long-term dependence of case counts, allowing for a comparative evaluation of health policy measures. We have applied the model to 2020 data from the countries most hit by the virus. Our empirical findings show that the proposed model describes the evolution of contagion dynamics and determines whether contagion growth can be affected by health policies. Based on our findings, we can draw two health policy conclusions that can be useful for all countries in the world. First, policy measures aimed at reducing contagion are very useful when contagion is at its peak to reduce the reproduction rate. Second, the contagion curve should be accurately monitored over time to apply policy measures that are cost-effective. Twit Text FOAVip Monitoring COVID-19 contagion growth ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=33973656 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33973656 #FOAvip English Wikipedia <ref>{{Cite pmid|33973656}}</ref> Monitoring COVID-19 contagion growth #MMPMID33973656 Agosto A; Campmas A; Giudici P; Renda A Stat Med 2021[Aug]; 40 (18): 4150-4160 PMID33973656show ga We present a statistical model that can be employed to monitor the time evolution of the COVID-19 contagion curve and the associated reproduction rate. The model is a Poisson autoregression of the daily new observed cases and dynamically adapt its estimates to explain the evolution of contagion in terms of a short-term and long-term dependence of case counts, allowing for a comparative evaluation of health policy measures. We have applied the model to 2020 data from the countries most hit by the virus. Our empirical findings show that the proposed model describes the evolution of contagion dynamics and determines whether contagion growth can be affected by health policies. Based on our findings, we can draw two health policy conclusions that can be useful for all countries in the world. First, policy measures aimed at reducing contagion are very useful when contagion is at its peak to reduce the reproduction rate. Second, the contagion curve should be accurately monitored over time to apply policy measures that are cost-effective. |*COVID-19[MESH] |Health Policy[MESH] |Humans[MESH] |Models, Statistical[MESH]Monitoring COVID-19 contagion growth (epmc).pdf DeepDyve Pubget Overpricing