Data Analysis and Predictive Mathematical Modeling for COVID-19 Epidemic Studies
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Introduction The health crisis that affected the entire planet from early 2020 has raised huge questions about our society’s response and ability to self-organize for facing a dramatic and complex problem. In this scenario, the mathematical models and the management of big data prove to be fundamental tools for the interpretation of the epidemic, its understanding and for supporting digital health. World public opinion, especially the one accustomed to decades of well-being and extraordinary achievements of scientific and technological research, reacted with surprise, if not astonishingly, in the face of the substantial unpreparedness in tackling a health problem, which however was not too dissimilar from others already sadly known in human history, even very recent. In fact, just in the last two decades we witnessed at least three similar viral epidemics (Ebola, SARS and MERS) which, despite not having proved to have the same geographic spread as COVID-19, are not completely resolved yet. From the outset, Mathematics was indicated as an essential discipline for providing, for example, forecasts of the course of the infection for time intervals of days,
Electronic Supplementary Material The online version of this article (https://doi.org/10.1007/ 16618_2020_5) contains supplementary material, which is available to authorized users. A. Quarteroni (*) MOX, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy Professor emeritus, Mathematics Institute, EPFL, Lausanne, Switzerland e-mail: alfio.quarteroni@epfl.ch L. Dede’ · N. Parolini MOX, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 A. Wonders (ed.) Math in the Time of Corona, Mathematics Online First Collections, https://doi.org/10.1007/16618_2020_5
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months, and even years, in terms of the reproduction number (R0 at the beginning, Rt in the following stages after adopting containment measures), the date of occurrence of the peak and the extent of the same, and, more sadly, the number of victims. These are numbers to which the public has quickly become accustomed thanks to fast but necessarily approximate information, as well as often not very reliable. Very likely, these expectations were cooled by the observation that the peak concept, mantra of the month of March during the outbreak in Italy, did not seem so conclusive, and the curves of the new daily infected did not have that regular and symmetrical trend (in the growth and decreasing phases) of a Gaussian. Most of the data sets provided daily by Authorities were affected by bias and errors: some more acceptable, due to the lack of coherence and the delay with which they were collected at a territorial level, others more substantial, almost all underestimating figures (just to name a couple, the number of new infections, due to an insufficient swabs, and the number of deaths attributed to the COVID-19). Furthermore, for several weeks
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