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The art of “DIVI-nation” – predicting tomorrow’s ICU capacities from today’s infection numbers
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<p>Preventing the health system from collapse has
been repeatedly stated as one of the main objectives for the German containment
policy for SARS COV 2. The exact relation between infections recorded in the public
surveillance system maintained by the German center for disease control (RKI) and
data on hospital occupation published by the German association for intensive
care an emergency medicine (DIVI) has not been analyzed to date. Using a
stepwise approach as described in the paper a linear regression model based on
recorded infections with known disease onset was found to be the most suitable
predictor for the number of ICU patients with a positive test for SARS COV 2 one
month later. The model showed an excellent model fit with nearly 90% explained variance
and reliable prediction of the maximum when applied to data beyond the
construction dataset. Still, the number of additional patients with a diagnosis
of COVID 19 does not necessarily mean a reduction of ICU capacities in the same
dimension. Based on a examination of interrelations between parameters published
in the DIVI registry it is concluded that a temporary reorganization of hospital
care for SARS COV 2 positive patients would probably help to mitigate the risks
coming with increasing infection rates.</p>
Title: The art of “DIVI-nation” – predicting tomorrow’s ICU capacities from today’s infection numbers
Description:
<p>Preventing the health system from collapse has
been repeatedly stated as one of the main objectives for the German containment
policy for SARS COV 2.
The exact relation between infections recorded in the public
surveillance system maintained by the German center for disease control (RKI) and
data on hospital occupation published by the German association for intensive
care an emergency medicine (DIVI) has not been analyzed to date.
Using a
stepwise approach as described in the paper a linear regression model based on
recorded infections with known disease onset was found to be the most suitable
predictor for the number of ICU patients with a positive test for SARS COV 2 one
month later.
The model showed an excellent model fit with nearly 90% explained variance
and reliable prediction of the maximum when applied to data beyond the
construction dataset.
Still, the number of additional patients with a diagnosis
of COVID 19 does not necessarily mean a reduction of ICU capacities in the same
dimension.
Based on a examination of interrelations between parameters published
in the DIVI registry it is concluded that a temporary reorganization of hospital
care for SARS COV 2 positive patients would probably help to mitigate the risks
coming with increasing infection rates.
</p>.
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