Epidemic analysis of COVID-19 in Italy based on spatiotemporal geographic information and Google Trends.
Transbound Emerg Dis
; 68(4): 2384-2400, 2021 Jul.
Article
in English
| MEDLINE | ID: covidwho-894799
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
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Search Engine
/
COVID-19
Type of study:
Observational study
/
Prognostic study
Limits:
Animals
Country/Region as subject:
Europa
Language:
English
Journal:
Transbound Emerg Dis
Journal subject:
Veterinary Medicine
Year:
2021
Document Type:
Article
Affiliation country:
Tbed.13902
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