{"id":"W3192016882","doi":"10.2196/28195","title":"Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Multivariate statistics; Incidence (geometry); Statistics; Mean absolute percentage error; Staffing; Pandemic; Time series; Medicine; Econometrics; Demography; Geography; Coronavirus disease 2019 (COVID-19); Mathematics; Mean squared error","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002427243,0.0008178397,0.0006827703,0.001007604,0.0003474687,0.001135802,0.001366945,0.0008925709,0.001596073],"category_scores_gemma":[0.004887029,0.0003868725,0.0008051697,0.001071916,0.0003976469,0.0008086047,0.0006544763,0.001296211,0.000259596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001556838,"about_ca_system_score_gemma":0.001520396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09029511,"about_ca_topic_score_gemma":0.04839067,"domain_scores_codex":[0.9994246,0.0002243095,0.00002855943,0.0001706452,0.00006023642,0.0000915649],"domain_scores_gemma":[0.9978335,0.001285984,0.0003702376,0.00007116424,0.0003026492,0.0001363541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007662902,0.0000581923,0.02323746,0.00001554478,0.00003796321,0.00007287209,0.00004970924,0.9702535,0.0001668113,0.001423137,0.0005599633,0.004048279],"study_design_scores_gemma":[0.000002562646,0.000009642536,0.00125096,0.000001894163,0.000003822667,0.000003015788,0.00001116239,0.9984654,0.0000187697,0.0001786802,0.00005095029,0.000003115935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9224548,0.0002263557,0.07172737,0.0009042833,0.00008490338,0.0001095208,0.002026778,0.0002718847,0.002194173],"genre_scores_gemma":[0.987558,0.0001549832,0.00881436,0.00003929282,0.00003283296,0.00008464779,0.001343683,0.00001856618,0.001953629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09029511,"threshold_uncertainty_score":0.1795391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1954714093503818,"score_gpt":0.411638922549406,"score_spread":0.2161675131990242,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}