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Abstract
<p class="first" id="d2409148e145">Since the first two novel coronavirus cases appeared
in January of 2020, the outbreak
of the COVID-19 epidemic seriously threatens the public health of Italy. In this article,
the distribution characteristics and spreading of COVID-19 in various regions of Italy
were analysed by heat maps. Meanwhile, spatial autocorrelation, spatiotemporal clustering
analysis and kernel density method were also applied to analyse the spatial clustering
of COVID-19. The results showed that the Italian epidemic has a temporal trend and
spatial aggregation. The epidemic was concentrated in northern Italy and gradually
spread to other regions. Finally, the Google Trends index of the COVID-19 epidemic
was further employed to build a prediction model combined with machine learning algorithms.
By using Adaboost algorithm for single-factor modelling,the results show that the
AUC of these six features (mask, pneumonia, thermometer, ISS, disinfection and disposable
gloves) are all >0.9, indicating that these features have a large contribution
to
the prediction model. It is also implied that the public's attention to the epidemic
is increasing as well as the awareness of the need for protective measures. This increased
awareness of the epidemic will prompt the public to pay more attention to protective
measures, thereby reducing the risk of coronavirus infection.
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