{"doi":"10.1016/j.jclinepi.2021.03.014","title":"Effect estimates of COVID-19 non-pharmaceutical interventions are non-robust and highly model-dependent","abstract":"<h4>Objective</h4>To compare the inference regarding the effectiveness of the various non-pharmaceutical interventions (NPIs) for COVID-19 obtained from different SIR models.<h4>Study design and setting</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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.","journal":"Journal of Clinical Epidemiology","year":2021,"id":7387,"datarank":0.6090664515819629,"base_score":4.060443010546419,"endowment":4.060443010546419,"self_citation_contribution":0.6090664515819629,"citation_network_contribution":0.0,"self_endowment_contribution":0.6090664515819629,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":57,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0664,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-08-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":148,"name":"John P. A. Ioannidis","orcid":"0000-0003-3118-6859","position":1,"is_corresponding":false},{"id":65919,"name":"Martin A. Tanner","orcid":"0000-0002-9536-5373","position":2,"is_corresponding":false},{"id":63040,"name":"Sally Cripps","orcid":"0000-0003-3207-172X","position":3,"is_corresponding":false},{"id":65920,"name":"V.W.L. Chin","orcid":null,"position":4,"is_corresponding":false},{"id":65918,"name":"Vincent Chin","orcid":null,"position":0,"is_corresponding":true}],"reference_count":45,"raw_metadata":null,"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}