In:
Frontiers in Big Data, Frontiers Media SA, Vol. 6 ( 2023-1-25)
Abstract:
Machine learning (ML) is an innovative method that is widely used in data prediction. Predicting the COVID-19 distribution using ML is essential for urban security risk assessment and governance. This study uses conditional generative adversarial network (CGAN) to construct a method to predict the COVID-19 hotspot distribution through urban texture and business formats and establishes a relationship between urban elements and COVID-19 so that machines can automatically predict the epidemic hotspots in cities. Taking Macau as an example, this method is used to determine the correlation between the urban texture and business hotspots of Macau and the new epidemic hotspot clusters. Different types of samples afforded different epidemic prediction accuracies. The results show the following: (1) CGAN can accurately predict the distribution area of COVID-19, and the accuracy can exceed 70%. (2) The results of predicting the COVID-19 distribution through urban texture and POI data of hospitals and stations are the best, with an accuracy of more than 60% in experiments in different regions of Macau. (3) The proposed method can also predict other areas in the city that may be at risk of COVID-19 and help urban epidemic prevention and control.
Type of Medium:
Online Resource
ISSN:
2624-909X
DOI:
10.3389/fdata.2023.1008292
DOI:
10.3389/fdata.2023.1008292.s001
DOI:
10.3389/fdata.2023.1008292.s002
DOI:
10.3389/fdata.2023.1008292.s003
DOI:
10.3389/fdata.2023.1008292.s004
DOI:
10.3389/fdata.2023.1008292.s005
DOI:
10.3389/fdata.2023.1008292.s006
DOI:
10.3389/fdata.2023.1008292.s007
DOI:
10.3389/fdata.2023.1008292.s008
DOI:
10.3389/fdata.2023.1008292.s009
DOI:
10.3389/fdata.2023.1008292.s010
DOI:
10.3389/fdata.2023.1008292.s011
DOI:
10.3389/fdata.2023.1008292.s012
DOI:
10.3389/fdata.2023.1008292.s013
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2023
detail.hit.zdb_id:
2957497-3
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