Discussion on early warning, prevention and control of emerging infectious diseases from a macroscopic perspective based on big data and effective distance model : enlightenment of COVID-19 epidemic data in China
Objective: To provide a system for warning, preventing and controlling emerging infectious diseases from a macroscopic perspective, using the COVID-19 epidemic data and effective distance model. Methods: The dates of hospitalization/isolation treatment of the first confirmed cases of COVID-19 and the cumulative numbers of confirmed cases in different provinces in China reported as of 23 February, 2020 were collected. The Location Based Service (LBS) big data platform of "Baidu Migration" was employed to obtain the data of the proportion of the floating population from Wuhan to all parts of the country. Effective distance models and linear regression models were established to analyze the relationship between the effective distance and the arrival time of the epidemic as well as the number of cumulative confirmed cases at provincial and municipal levels. Results: The arrival time of the epidemic and the cumulative number of confirmed cases of COVID-19 had significant linear relationship at both provincial and municipal levels in China, and the regression coefficients of each linear model were significant (P<0.001). At the provincial level, the effective distance could explain about 71% of the variation of the model with arrival time along with around 90% of the variation for the model in the cumulative confirmed case magnitude; at the municipal level, the effective distance could explain about 66% of the variation for the model in arrival time, and about 85% of the variation of the model with the cumulative confirmed case magnitude. Conclusions: The fitting degree of the models are good. The LBS big data and effective distance model can be used to estimate the track, time and extent of epidemic spread to provide useful reference for early warning, prevention and control of emerging infectious diseases.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:41 |
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| Enthalten in: |
Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi - 41(2020), 12 vom: 10. Dez., Seite 1989-1993 |
| Sprache: |
Chinesisch |
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| Beteiligte Personen: |
Wang, Z K [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 12.01.2021 Date Revised 12.01.2021 published: Print Citation Status MEDLINE |
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| doi: |
10.3760/cma.j.cn112338-20200306-00269 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM309241448 |
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| 245 | 1 | 0 | |a Discussion on early warning, prevention and control of emerging infectious diseases from a macroscopic perspective based on big data and effective distance model |b enlightenment of COVID-19 epidemic data in China |
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| 500 | |a Date Revised 12.01.2021 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Objective: To provide a system for warning, preventing and controlling emerging infectious diseases from a macroscopic perspective, using the COVID-19 epidemic data and effective distance model. Methods: The dates of hospitalization/isolation treatment of the first confirmed cases of COVID-19 and the cumulative numbers of confirmed cases in different provinces in China reported as of 23 February, 2020 were collected. The Location Based Service (LBS) big data platform of "Baidu Migration" was employed to obtain the data of the proportion of the floating population from Wuhan to all parts of the country. Effective distance models and linear regression models were established to analyze the relationship between the effective distance and the arrival time of the epidemic as well as the number of cumulative confirmed cases at provincial and municipal levels. Results: The arrival time of the epidemic and the cumulative number of confirmed cases of COVID-19 had significant linear relationship at both provincial and municipal levels in China, and the regression coefficients of each linear model were significant (P<0.001). At the provincial level, the effective distance could explain about 71% of the variation of the model with arrival time along with around 90% of the variation for the model in the cumulative confirmed case magnitude; at the municipal level, the effective distance could explain about 66% of the variation for the model in arrival time, and about 85% of the variation of the model with the cumulative confirmed case magnitude. Conclusions: The fitting degree of the models are good. The LBS big data and effective distance model can be used to estimate the track, time and extent of epidemic spread to provide useful reference for early warning, prevention and control of emerging infectious diseases | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a Early warning, prevention and control | |
| 650 | 4 | |a Effective distance | |
| 650 | 4 | |a Infectious diseases | |
| 650 | 4 | |a Population floating | |
| 700 | 1 | |a Chen, Z S |e verfasserin |4 aut | |
| 700 | 1 | |a Du, A H |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, C Y |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, H |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Z W |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, J F |e verfasserin |4 aut | |
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