Abstract
On 21 February 2020, a violent COVID-19 outbreak, which was initially concentrated in Lombardy before infecting some surrounding regions exploded in Italy. Shortly after, on 9 March, the Italian Government imposed severe restrictions on its citizens, including a ban on traveling to other parts of the country. No travel, no virus spread. Many regions, such as those in southern Italy, were spared. Then, in June 2020, under pressure for the economy to reopen, many lockdown measures were relaxed, including the ban on interregional travel. As a result, the virus traveled for hundreds of kilometers, from north to south, with the effect that areas without infections, receiving visitors from infected areas, became infected. This resulted in a sharp increase in the number of infected people; i.e., the daily count of new positive cases, when comparing measurements from the beginning of July to those from at the middle of September, rose significantly in almost all the Italian regions. Upon confirmation of the effect of Italian domestic tourism on the virus spread, three computational models of increasing complexity (linear, negative binomial regression, and cognitive) have been compared in this study, with the aim of identifying the one that better correlates the relationship between Italian tourist flows during the summer of 2020 and the resurgence of COVID-19 cases across the country. Results show that the cognitive model has more potential than the others, yet has relevant limitations. The models should be considered as a relevant starting point for the study of this phenomenon, even if there is still room to further develop them up to a point where they become able to capture all the various and complex spread patterns of this disease.
Highlights
The SARS-CoV-2 virus that emerged in Wuhan, China, at the end of 2019, causing the current global pandemic, has spread globally extremely quickly
Because the above window-sliding algorithm may represent a fast approximation of alternative optimal methods that could individuate multiple change points, we have developed an additional experiment aimed at verify the validity of our method with a more complex approach based on the concept of threshold or penalty and using a penalty based Bayesian Information Criterion (BIC), as suggested in [21]
The most likely change point for the greater part of the infection curves of the Italian regions falls around the end of August, the peak of the spread of COVID-19 in those regions is around the middle of that month
Summary
The SARS-CoV-2 virus that emerged in Wuhan, China, at the end of 2019, causing the current global pandemic, has spread globally extremely quickly This is unquestionably due to its high infectiveness, but it would not have reached the planetary scale if not for the interconnectedness of the world we live in. Sensors 2020, 20, 7319 even domestic) and closing public places, schools, and offices, as well as introducing strict personal hygiene rules to reduce the possibility of virus transmission Put succinctly, these measures were quite effective in flattening the curve, up to the point that many countries lowered their guard by the end of spring 2020, thinking that the worst was behind them, and ended up risking a second wave in the autumn It was clear that we were heading towards a pandemic, and most countries in the world put in place multiple restrictions to avoid the further spread of the virus, including banning travel
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