PMC:7033348 / 9219-9851 JSONTXT

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T48","span":{"begin":229,"end":231},"obj":"http://purl.obolibrary.org/obo/CLO_0009718"},{"id":"T49","span":{"begin":269,"end":270},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T50","span":{"begin":573,"end":575},"obj":"http://purl.obolibrary.org/obo/CLO_0009718"}],"text":"We estimate the best-fit model solution to the reported data using nonlinear least squares fitting. This process yields the set of model parameters Θ that minimizes the sum of squared errors between the model f(t,Θ) and the data yt; where ΘGLM = (r, p, K), ΘRich = (r, a, K), and ΘSub = (r, p, K 0 , q, C thr) correspond to the estimated parameter sets for the GLM, the Richards model, and the sub-epidemic model, respectively; parameter descriptions are provided in the Supplement. Thus, the best-fit solution f(t,Θˆ) is defined by the parameter set Θˆ=argmin∑t=1n(f(t,Θ)−yt)2. We fix the initial condition to the first data point."}

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T7","span":{"begin":31,"end":39},"obj":"Chemical"},{"id":"T8","span":{"begin":502,"end":510},"obj":"Chemical"}],"attributes":[{"id":"A7","pred":"chebi_id","subj":"T7","obj":"http://purl.obolibrary.org/obo/CHEBI_75958"},{"id":"A8","pred":"chebi_id","subj":"T8","obj":"http://purl.obolibrary.org/obo/CHEBI_75958"}],"text":"We estimate the best-fit model solution to the reported data using nonlinear least squares fitting. This process yields the set of model parameters Θ that minimizes the sum of squared errors between the model f(t,Θ) and the data yt; where ΘGLM = (r, p, K), ΘRich = (r, a, K), and ΘSub = (r, p, K 0 , q, C thr) correspond to the estimated parameter sets for the GLM, the Richards model, and the sub-epidemic model, respectively; parameter descriptions are provided in the Supplement. Thus, the best-fit solution f(t,Θˆ) is defined by the parameter set Θˆ=argmin∑t=1n(f(t,Θ)−yt)2. We fix the initial condition to the first data point."}

{"project":"LitCovid-sentences","denotations":[{"id":"T56","span":{"begin":0,"end":99},"obj":"Sentence"},{"id":"T57","span":{"begin":100,"end":482},"obj":"Sentence"},{"id":"T58","span":{"begin":483,"end":578},"obj":"Sentence"},{"id":"T59","span":{"begin":579,"end":632},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"We estimate the best-fit model solution to the reported data using nonlinear least squares fitting. This process yields the set of model parameters Θ that minimizes the sum of squared errors between the model f(t,Θ) and the data yt; where ΘGLM = (r, p, K), ΘRich = (r, a, K), and ΘSub = (r, p, K 0 , q, C thr) correspond to the estimated parameter sets for the GLM, the Richards model, and the sub-epidemic model, respectively; parameter descriptions are provided in the Supplement. Thus, the best-fit solution f(t,Θˆ) is defined by the parameter set Θˆ=argmin∑t=1n(f(t,Θ)−yt)2. We fix the initial condition to the first data point."}