How Data Analytics and Big Data Can Help Scientists in Managing COVID-19 Diffusion: Modeling Study to Predict the COVID-19 Diffusion in Italy and the Lombardy Region

Tosi, Davide; Campi, Alessandro

BACKGROUND: COVID-19 is the most widely discussed topic worldwide in 2020, and at the beginning of the Italian epidemic, scientists tried to understand the virus diffusion and the epidemic curve of positive cases with controversial findings and numbers. OBJECTIVE: In this paper, a data analytics study on the diffusion of COVID-19 in Italy and the Lombardy Region is developed to define a predictive model tailored to forecast the evolution of the diffusion over time. METHODS: Starting with all available official data collected worldwide about the diffusion of COVID-19, we defined a predictive model at the beginning of March 2020 for the Italian country. RESULTS: This paper aims at showing how this predictive model was able to forecast the behavior of the COVID-19 diffusion and how it predicted the total number of positive cases in Italy over time. The predictive model forecasted, for the Italian country, the end of the COVID-19 first wave by the beginning of June. CONCLUSIONS: This paper shows that big data and data analytics can help medical experts and epidemiologists in promptly designing accurate and generalized models to predict the different COVID-19 evolutionary phases in other countries and regions, and for second and third possible epidemic waves.