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. 2021 Jul;68(4):2384-2400.
doi: 10.1111/tbed.13902. Epub 2020 Nov 27.

Epidemic analysis of COVID-19 in Italy based on spatiotemporal geographic information and Google Trends

Bing Niu  1 , Ruirui Liang  1 , Shuwen Zhang  1 , Hui Zhang  1 , Xiaosheng Qu  2 , Qiang Su  3   4 , Linfeng Zheng  5 , Qin Chen  1
Affiliations

Epidemic analysis of COVID-19 in Italy based on spatiotemporal geographic information and Google Trends

Bing Niu et al. Transbound Emerg Dis. 2021 Jul.

Abstract

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.

Keywords: Google Trends; coronavirus; epidemic; geographic information system; machine learning.

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References

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