Closed johnurbanik closed 4 years ago
I performed some analysis using the empirical dataset from Wuhan over the later stages of the initial outbreak. It isn't my best analysis (I tried to get it out quickly after work and I'd welcome anyone contributing), but the data certainly seems to point to the data from Zhou et. al. being pretty far off in terms of the mean and tails of the distribution asymptotically.
Please feel free to take a look.
This is pretty useful, thanks! I think we will:
How does that sound?
As mentioned in https://github.com/thibautjombart/covid19_bed_occupancy/issues/40, I'm very worried about the validity of the Zhou et. al. statistics, here and in general across the epidemiological modeling space right now. I'm not sure that the experimental design leads to a result where the length of hospitalization is valid. The experiment is still useful in terms of understanding co-morbidities, but I think it may be necessary to find a different source for length of stay information.
I'd really appreciate if you all take a look. I'm hoping I'm wrong here, as it seems that a lot of modeling is using the medians from this study (and using it as a mean, not even a distribution like you guys are). Perhaps another study shows similar results.
Quoting the study:
The two biggest red flags for me are that 'patients were sometimes transferred late in their illness to the two included' hospitals. The fact that the study includes 'time from illness onset to death discharge' >>> 'hospital length of stay' combined with the graphics in figure 1 and 2 suggest that a large percentage of the patients were hospitalized for other reasons before transfer (how else would they have labs for these patients and why else would they suspect such a large infectious time before hospitalization?).
I expect that the bar for admission in most hospitals will be higher than just fever and a positive test (i.e. I've heard reports that in some cases in New York, those without dyspnea are sent home, sometimes without testing), but it still remains that dyspnea is median 4 days earlier in the dataset than hospital admission (which is inexplicably in Table 1 and not copied into Table 2).
Further, the fact that patients who did not have a definite outcome were not included in the sample means that patients who were admitted before Jan 19 but were not yet discharged would skew the distribution toward a longer hospitalization time.
If these two variables interact it is possible that the actual distributions we'd see could have a substantially larger median than suggested (or at least a fatter tail). I hope that I am wrong.