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'Pearson correlation (R) is a statistical measure of goodness of fit between M number of model function values (y) and COVID-19 data (z) as follows [17]: ������������������1 = ������ ������ ∑ ������������ − ∑ ������ ∑ ������ = √(������ ∑ ������2 − (∑ ������)2)(������ ∑ ������2 − (∑ ������)2)\nThe regression coefficient (R2) is another statistical measure for goodness of fit between M number of projected values (y) and COVID-19 data (z), although we used it as follows [18-19]: = − =\nIn equation (15), ������̅ is the average of COVID-19 data and better fits provide the regression coefficient (R2) values close to unity and Obj2 to zero value'
5f2d7604a58f1dfd5210abe7:
E [D(41 )] = pd (1 ) · N (1 ) · P (41 ) + pd (2 ) · N (2 ) · P (40 ) + · · · + pd (34 ) · N (34 ) · P (8 )\nE [D(42 )] = pd (2 ) · N (2 ) · P (41 ) + pd (3 ) · N (3 ) · P (40 ) + · · · + pd (35 ) · N (35 ) · P (8 ) ..\nE [D(103 )] = pd (63 ) · N (63 ) · P (41 ) + pd (64 ) · N (64 ) · P (40 )',
5ed7bd5b768935d2be5cd1df:
being susceptible to be infected by the SARS-Cov2 virus, the fraction having been exposed to it, the fraction infected, and the fraction removed (including recoveries and deceases), the epidemic is assumed to obey the following continuous-time dynamics: \uf8f1 \uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f2 ds dt (t) de dt (t) = −β(t)i(t)s(t) = β(t)i(t)s(t) − γe(t) \uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f4\uf8f3 di dt (t) dr dt (t) = γe(t) − δi(t) = δi(t) s(t) + e(t) + i(t) + r(t) = 1 where: • β(t), t ∈ R, represents the time-varying virus transmission rate; • γ denotes the rate at which the exposed subject develops the disease (this includes people presenting symptoms and asymptomatics).'
5b192c78cf58f11cd52c690c:
S ~ ~ sCynEtax into GIS for niodelling urban spaces,l r ~ k r r i n r i ~ i i d [6] Jin, G. J.. 2001, Introduction to Urbnn Design Guidelines in USA, Ur*DirriPlrvrriiirig O!wrerrs, 2: 6-10.',
5d5090dd0b45c76cafa47d80:
Thus, the rate of data-driven behaviour change p(t) can be defined as p(t) = news(t) = news(t) , max{news(t), t ∈ NT} t ∈ NT = {1, … , 38}, then we obtain the rate of behaviour change in the Shaanxi province of China during the 2009 A/H1N1 influenza epidemic from September 3 to October 10 (the first wave), which together with new hospital notifications has been used to estimate all unknown parameters (OR0, OR1, OR2, OR3, OR4, ������, ������, ������, ������, ������) of Equation 6. We took account of agent heterogeneity by allowing each individual parameter to vary across a distribution (ie, the same mean but different standard deviation), from which agents sampled their values using log-normal distributions for the ORk(k = 0, 1, … , 4) (ie, ORk ∼ exp{N(������, ������2)}) and normal distributions for ������, ������, ������, ������, and ������.'
The text was updated successfully, but these errors were encountered:
I need to think about the best path forward here. It looks like some of these are valid, but unfortunately poor quality extractions (5b192c78cf58f11cd52c690c, 5f2d7604a58f1dfd5210abe7). Others look like extraction issues that are fixable (5d5090dd0b45c76cafa47d80, 5fc4fd44d76fca4a3f0cd224). But 5ed7bd5b768935d2be5cd1df is an odd case. It looks like it just has invalid UTF characters included.
So the real question is how to systematically detect problematic documents and fix them.. Probably I'll end up having to do a full sweep but that sounds painful..
Problematic examples:
5fc4fd44d76fca4a3f0cd224:
5f2d7604a58f1dfd5210abe7:
5ed7bd5b768935d2be5cd1df:
5b192c78cf58f11cd52c690c:
5d5090dd0b45c76cafa47d80:
The text was updated successfully, but these errors were encountered: