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10.1016/j.jclinepi.2021.03.014 http://scihub22266oqcxt.onion/10.1016/j.jclinepi.2021.03.014 33781862!7997643!33781862     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33781862&cmd=llinks): Failed to open stream: HTTP request failed! 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Effect estimates of COVID-19 non-pharmaceutical interventions are non-robust and highly model-dependent |pdf=|usr=}}{{33781862}} gab.com Text Effect estimates of COVID-19 non-pharmaceutical interventions are non-robust and highly model-dependent JO: J Clin Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=33781862 #FOAvip OBJECTIVE: To compare the inference regarding the effectiveness of the various non-pharmaceutical interventions (NPIs) for COVID-19 obtained from different SIR models. STUDY DESIGN AND SETTING: We explored two models developed by Imperial College that considered only NPIs without accounting for mobility (model 1) or only mobility (model 2), and a model accounting for the combination of mobility and NPIs (model 3). Imperial College applied models 1 and 2 to 11 European countries and to the USA, respectively. We applied these models to 14 European countries (original 11 plus another 3), over two different time horizons. RESULTS: While model 1 found that lockdown was the most effective measure in the original 11 countries, model 2 showed that lockdown had little or no benefit as it was typically introduced at a point when the time-varying reproduction number was already very low. Model 3 found that the simple banning of public events was beneficial, while lockdown had no consistent impact. Based on Bayesian metrics, model 2 was better supported by the data than either model 1 or model 3 for both time horizons. CONCLUSION: Inferences on effects of NPIs are non-robust and highly sensitive to model specification. In the SIR modeling framework, the impacts of lockdown are uncertain and highly model-dependent. Twit Text FOAVip Effect estimates of COVID-19 non-pharmaceutical interventions are non-robust and highly model-dependent ????J Clin Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=33781862 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33781862 #FOAvip English Wikipedia <ref>{{Cite pmid|33781862}}</ref> Effect estimates of COVID-19 non-pharmaceutical interventions are non-robust and highly model-dependent #MMPMID33781862 Chin V; Ioannidis JPA; Tanner MA; Cripps S J Clin Epidemiol 2021[Aug]; 136 (ä): 96-132 PMID33781862show ga OBJECTIVE: To compare the inference regarding the effectiveness of the various non-pharmaceutical interventions (NPIs) for COVID-19 obtained from different SIR models. STUDY DESIGN AND SETTING: We explored two models developed by Imperial College that considered only NPIs without accounting for mobility (model 1) or only mobility (model 2), and a model accounting for the combination of mobility and NPIs (model 3). Imperial College applied models 1 and 2 to 11 European countries and to the USA, respectively. We applied these models to 14 European countries (original 11 plus another 3), over two different time horizons. RESULTS: While model 1 found that lockdown was the most effective measure in the original 11 countries, model 2 showed that lockdown had little or no benefit as it was typically introduced at a point when the time-varying reproduction number was already very low. Model 3 found that the simple banning of public events was beneficial, while lockdown had no consistent impact. Based on Bayesian metrics, model 2 was better supported by the data than either model 1 or model 3 for both time horizons. CONCLUSION: Inferences on effects of NPIs are non-robust and highly sensitive to model specification. In the SIR modeling framework, the impacts of lockdown are uncertain and highly model-dependent. |*Models, Statistical[MESH] |*Physical Distancing[MESH] |COVID-19/*prevention & control[MESH] |Communicable Disease Control/*methods[MESH] |Europe[MESH] |Humans[MESH] |Quarantine/*methods[MESH]Effect estimates of COVID-19 non-pharmaceutical interventions are non-(epmc).pdf DeepDyve Pubget Overpricing