key: cord-325045-ak7rouhb
authors: Sotgiu, Giovanni; Gerli, Alberto G.; Centanni, Stefano; Miozzo, Monica; Canonica, G. Walter; B. Soriano, Joan; Virchow, J. Christian
title: Advanced forecasting of SARS‐CoV‐2‐related deaths in Italy, Germany, Spain, and New York State
date: 2020-05-11
journal: Allergy
DOI: 10.1111/all.14327
sha: 
doc_id: 325045
cord_uid: ak7rouhb

nan

To the Editor, An appropriate forecasting model can contribute to define strategic choices both in limiting the spread of SARS-Cov-2 virus and in reducing the related mortality rate. 1 Temporal trends of SARS-CoV-2 key epidemiological indicators (eg, mortality, incidence of infected cases) to describe the ongoing pandemic caused by SARS-CoV-2 have been estimated [2] [3] [4] ; their accuracy is key to plan and implement adequate health interventions (eg, increasing ICU availability, distribute personal protection gear, an eventual vaccine). A number of studies tried to identify the best model to forecast SARS-Cov-2-related deaths, interpolating daily cases according to a Gaussian curve. 5 Recently, 6 we suggested an innovative and robust data mining approach based on Chinese and Italian data to forecast SARS-Cov-2-related mortality using a 3rd-degree polynomial curve, which describes a growth up to the daily peak, and, then, a five parameter logistic (5PL) asymmetrical sigmoidal curve following a parametric growth (45% of aggregated cases at the peak day, which is at 20% of the estimated aggregated outbreak duration; 50% of total cases at 20.8% of the expected duration; 63.3% of total cases at 24% of the expected duration; 81.4% at 30% of the expected duration; 92.8% at 40% of the expected duration;

99.5% at 80% of the expected duration). Based on this, we derived a reliable model which was obtained by the analysis and interpolation of the aggregated cases since we do not expect a symmetric behavior of the curve after the daily peaks due to effect of different factors (eg, the social intervention, political measures, increase in ICU beds). Based on this model, we aimed at predicting SARS-Cov-2-related mortality in Italy, 7 Germany, 8 Spain, 9 and New York State. 10 To validate the model, we calculated R 2 correlations for Italy (0.995), Germany (0.996), Spain (0.988), and New York State (0.998) after 30, 18, 11, and 10 days of prediction, respectively, thus confirming the reliability of our modeling approach during the first month of this outbreak in each of these countries ( Figure 1A) . Accordingly, the expected number of SARS-Cov-2-related deaths up to May 31, 2020, was associated with a curve suggesting no consistent correlation between the number of deaths and the size of the national population ( Figure 1B) . Instead, the expected SARS-Cov-2related mortality is more closely related to early events within the first days of the outbreak and to timing to regional/national interventions (eg, social distancing, confinement), which suggests that superspreading events (eg Lombardia region, Italy) deeply impact on the magnitude of the curve and, in turn, on the number of deaths.

Importantly, the forecast is in keep with the number of days supposed to reach daily peak after the lockdown measures. Figure 1B illustrates the curves of the expected deaths based on daily peak after 28 and 21 days in Italy, Germany, Spain, and New York State. Despite being an advanced prediction, some limitations should be underscored, from an intrinsic wider uncertainty due to the fact that the underlying conditions can change to the possibility that interventions might be implemented based on such predictions, then changing the predicted outcome. We, therefore, recommend to carefully consider those estimates but with caution based on the above-mentioned uncertainties.

Our predictions were estimated for Italy, Germany, Spain, and New York State but can be translated to other settings. Based on this, it might also be possible to predict the number of respirators, health personnel needed and further spreading, downscaling, or healing curves but that would require additional data. For this purpose, positive cases would have to be monitored, for, for exam-

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