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Scientific Letter
DOI: 10.1016/j.medin.2020.04.007
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Disponible online el 30 de Mayo de 2020
Novel coronavirus (2019-nCov): do you have enough intensive care units?
Nuevo coronavirus (2019-nCov): ¿tiene suficientes unidades de cuidados intensivos?
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G. Melegaria,
Autor para correspondencia
melegari.gabriele@gmail.com

Corresponding author.
, E. Giulianib, G. Mainib, L. Barbierib, P. Baffonia, E. Bertellinia, A. Barbieric
a Department of Anaesthesia and Intensive Care, Azienda Ospedaliero-Universitaria di Modena, Italy
b University of Modena and Reggio Emilia, Italy
c School of Anaesthesia and Intensive Care of University of Modena and Reggio Emilia, Italy
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Table 1. Data from February 23 until March 15, 2020. Left panel: number of 2019-nCov positive subjects, number of hospital admissions and ICU admissions, number of deaths, and recovery. Right panel: predicted mean value calculated using non- linear regression. R2 describes the goodness of fit, where 1 indicates a complete fit.
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Recently, the World Health Organization declared the novel coronavirus (2019-nCov) a global health emergency due to its global implications for the health care system and its economic impact. Italy was one of the first European countries with registered clustered cases of acute pneumonia. On February 23, 2020, the Italian government declared the first set of quarantine measures to slow the spread of the virus.1 Estimations show that 2019-nCoV is a high-diffusion virus with a 2% fatality rate; approximately 20% all hospital admissions were directly to the ICU.2 National health care systems could collapse if this spread of pneumonia continues at the current rate. This study aims to analyze official Italian data to build a predictive model.3 From February 23, 2020 to March 15, 2020, daily data from the cumulative reports of the Protezione Civile Italiana (Italian Civil Protection) were collected, including the number of positive subjects, hospital admissions, ICU admissions, deaths, and full recovery. Statistical programs were used for the analysis. Different models were tested, and forecast values were calculated, and the best model, with a p-value <0.05, was considered to calculate the predicted values. The number of positive subjects (PS) follows a non-linear regression with p<0.001 for the number of PS and hospital admissions, PS and ICU admissions, PS and deaths, and PS and recovered subjects. Simultaneously, the number of people admitted to hospitals follows a non-linear regression with p<0.001 (Table 1). Among the 46.7% of PS admitted in hospitals, 10.0% were admitted to the ICU. The ratio of hospitalized patients to those admitted to the ICU is 22.3%, the death rate is 5%, and the recovery rate is 8%. The relationship between hospital admissions and ICU admissions follows a linear regression, with p<0.001. Recent data on 2019-n CoV present different non-linear growth patterns, besides the rapidly increasing number of PS, which are very susceptible to public health rules. It is fascinating to observe the constant ratio of hospitalized and ICU admissions. If, in the next few weeks, infections reach 1% of the Italian population, over 60.000 ICU beds will be required, which may be the breaking point for the system. These results could be confirmed and highlighted by the increasing trend of ICU admissions, and the relationship between hospitalized patients and ICU admitted subjects. The national health care system needs more time to adapt to and deal with this challenge. The 2019 n-CoV transmission probability presents the following relationship. y = ax3bx2+cx + d. Here, y indicates infected subjects, x is the intrinsic potential reproducing number, and the constants a,b,c and d are the intercepts. With environmental strategies and adequate medical treatments, infection and death rates reduced, while recovery rates increased (Fig. 1a and b). Observing Italy's data, this equation is applicable to hospital and ICU admission, and to the rate of death and recovery. As in China, quarantine and environmental strategies have a positive, but slow effect. They can reduce the rate of infection, admissions to ICU, and death, and can change the model.4 Furthermore, this is a preliminary interpretation, and not the end of this phenomenon. It will be possible to analyze, customize, and fit the best model.5 However, in this context, it is important not to forget the emergency; necessary medical and surgical procedures should be guaranteed. A possible solution is to try to re-organize the mission of the hospital as happened in different and less dramatic events.6 This model has the potential to predict the worst-case scenario. With this knowledge, we are ready to do the best to prevent the system from reaching the breaking point and to change the 2019 nCoV curve now!

Table 1.

Data from February 23 until March 15, 2020. Left panel: number of 2019-nCov positive subjects, number of hospital admissions and ICU admissions, number of deaths, and recovery. Right panel: predicted mean value calculated using non- linear regression. R2 describes the goodness of fit, where 1 indicates a complete fit.

Day  Number of patientsPredicted number
  2019-nCov positive  Hospital admission  ICU Admission  Death  Recovery  2019-nCov positive  Hospital admission  ICU Admission  Death  Recovery 
132  54  26  39  51  28  17 
229  101  27  224  89  27 
322  114  35  10  386  125  31  12  −7 
400  128  36  12  541  169  39  15  −4 
650  248  56  12  45  705  228  52  17 
888  345  64  21  46  892  312  72  20  32 
1049  401  105  29  50  1118  427  99  25  64 
1694  639  140  34  83  1399  583  133  35  106 
1835  742  166  52  149  1750  787  177  50  159 
10  2263  1034  229  79  160  2187  1048  229  73  220 
11  2706  1346  295  107  276  2725  1374  292  104  292 
12  3296  1790  351  148  414  3380  1774  366  145  373 
13  3916  2394  462  197  523  4168  2255  452  198  464 
14  5061  2651  567  233  589  5103  2826  550  264  564 
15  6378  3557  650  366  622  6202  3496  662  345  674 
16  7985  4316  733  463  733  7.495  4.151  757  474  773 
17  8514  5038  877  631  1004  9.026  4.878  881  627  940 
18  10590  5838  1028  827  1045  10789  5.675  1017  811  1134 
19  12839  6650  1153  1016  1258  12802  6.543  1165  1031  1357 
20  14995  7246  1328  1266  1439  15084  7.484  1326  1288  1610 
21  17750  8372  1518  1441  1966  17653  8.500  1498  1586  1897 
22  20603  9663  1672  2063  2335  20528  9.593  1684  1929  2218 
R2            0.999  0.998  0.999  0.993  0.988 
p            0.001  0.001  0.001  0.001  0.001 
Figure 1.

(a) Reports the model curve and its fitting curve of positive subjects of 2019-nCov, hospital and ICU admissions, death, and recovery. (b) Reports the ICU admission trend with a moving averages relation.

(0,12MB).
Funding

None declared.

The Study follows Strobe Guidelines.

Database: data are available on http://www.protezionecivile.gov.it/attivita-rischi/rischio-sanitario/emergenze/coronavirus.

Statistic calculate were performed using Microsoft Excel® and STATA 16® program (STATA Corp LP 4905 Lakeway Drive TX 77845 USA by a physician (with statistic competence) and by an engineer: all calculate were attached as supplementary file.

Author contributions

Melegari G and Barbieri A: concept design of the study, statistic calculate, Maini G: statistic calculate and control, Giuliani E: writing of the paper, Barbieri L: graphical aspects, Baffoni P and Bertellini E: manuscript revision and final approval.

Keypoints

The study analyzes the Italian Novel Coronavirus (2019-nCov) outbreaks, searching possible predicting model, underlining the risk of the trend of phenomena.

Conflict of interest

None.

Acknowledgments

Special thanks to Dr. Giuliano Carrozzi, Epidemiology Azienda AUSL Modena, Italy.

Appendix A
Supplementary data

The following are the supplementary data to this article:

References
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[2]
R.S. Wax, M.D. Christian.
Practical recommendations for critical care and anesthesiology teams caring for novel coronavirus (2019-nCoV) patients.
Can J Anesth, ([published online ahead of print, 2020 Feb 12]),
[4]
K. Roosa, Y. Lee, R. Luo, A. Kirpich, R. Rothenberg, J.M. Hyman, et al.
Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020.
Infect Dis Model, 5 (2020), pp. 256-263
[5]
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Fitting dynamic models to epidemic outbreaks with quantified uncertainty: a primer for parameter uncertainty, identifiability, and forecasts.
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[6]
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Response by twin Italian hub hospitals in a double seismic event: a retrospective observational investigation.
Prehosp Emerg Care, 22 (2018), pp. 353-360
Copyright © 2020. Elsevier España, S.L.U. y SEMICYUC
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