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Methods on COVID-19 epidemic curve estimation during emergency based on Baidu search engine and ILI traditional surveillance in Beijing, China

Methods on COVID-19 epidemic curve estimation during emergency based on Baidu search engine and ILI traditional surveillance in Beijing, China
Methods on COVID-19 epidemic curve estimation during emergency based on Baidu search engine and ILI traditional surveillance in Beijing, China
Surveillance is an essential work on infectious diseases prevention and control. When the pandemic occurred, the inadequacy of traditional surveillance was exposed, but it also provided a valuable opportunity to explore new surveillance methods. This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness (ILI) surveillance. A novel hybrid model (multiattention bidirectional gated recurrent unit (MABG)–susceptible–exposed–infected–removed (SEIR)) was developed, which leveraged a deep learning algorithm (MABG) to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever, pyrexia, cough, sore throat, anti-fever medicine, and runny nose. By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019 (COVID-19) cases, a transmission dynamics model (SEIR) was formulated to estimate the transmission dynamics and epidemic curve of SARS-CoV-2. During the COVID-19 pandemic, when conventional surveillance measures have been suspended temporarily, cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19. In the specific case of Beijing, it has been ascertained that cumulative infection attack rate surpass 80.25% (95% confidence interval (95% CI): 77.51%–82.99%) since December 17, 2022, with the apex of the outbreak projected to transpire on December 12. The culmination of existing patients is expected to occur three days subsequent to this peak. Effective reproduction number (Rt) represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic, remained below 1 since December 17, 2022. The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support. Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks. Syndrome surveillance on COVID-19 should be established to following on the epidemic, clinical severity, and medical source demand.
Baidu search engine, COVID-19, Deep learning, Epidemic curve, Influenza-like illness, Transmission dynamics model
2095-8099
Zhang, Ting
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Yang, Liuyang
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Han, Xuan
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Fan, Guohui
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Qian, Jie
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Hu, Xuancheng
78f291bd-a74a-49be-8e68-52612fc708dc
Lai, Shengjie
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Li, Zhongjie
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Liu, Zhimin
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Feng, Luzhao
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Yang, Weizhong
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Zhang, Ting
a723a456-96d6-4bb8-8f29-ddc46523d3bb
Yang, Liuyang
e9874ffe-6130-46ea-b73c-7c6ce629afb8
Han, Xuan
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Fan, Guohui
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Qian, Jie
187ae32b-e0ed-4622-85b8-829d076ea148
Hu, Xuancheng
78f291bd-a74a-49be-8e68-52612fc708dc
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001
Li, Zhongjie
f89a98f7-f6d3-4312-995a-bc658ae9a93f
Liu, Zhimin
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Feng, Luzhao
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Yang, Weizhong
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Zhang, Ting, Yang, Liuyang, Han, Xuan, Fan, Guohui, Qian, Jie, Hu, Xuancheng, Lai, Shengjie, Li, Zhongjie, Liu, Zhimin, Feng, Luzhao and Yang, Weizhong (2023) Methods on COVID-19 epidemic curve estimation during emergency based on Baidu search engine and ILI traditional surveillance in Beijing, China. Engineering. (doi:10.1016/j.eng.2023.08.006).

Record type: Article

Abstract

Surveillance is an essential work on infectious diseases prevention and control. When the pandemic occurred, the inadequacy of traditional surveillance was exposed, but it also provided a valuable opportunity to explore new surveillance methods. This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness (ILI) surveillance. A novel hybrid model (multiattention bidirectional gated recurrent unit (MABG)–susceptible–exposed–infected–removed (SEIR)) was developed, which leveraged a deep learning algorithm (MABG) to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever, pyrexia, cough, sore throat, anti-fever medicine, and runny nose. By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019 (COVID-19) cases, a transmission dynamics model (SEIR) was formulated to estimate the transmission dynamics and epidemic curve of SARS-CoV-2. During the COVID-19 pandemic, when conventional surveillance measures have been suspended temporarily, cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19. In the specific case of Beijing, it has been ascertained that cumulative infection attack rate surpass 80.25% (95% confidence interval (95% CI): 77.51%–82.99%) since December 17, 2022, with the apex of the outbreak projected to transpire on December 12. The culmination of existing patients is expected to occur three days subsequent to this peak. Effective reproduction number (Rt) represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic, remained below 1 since December 17, 2022. The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support. Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks. Syndrome surveillance on COVID-19 should be established to following on the epidemic, clinical severity, and medical source demand.

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Submitted date: 24 February 2023
Accepted/In Press date: 28 August 2023
Published date: 6 September 2023
Additional Information: Funding Information: This study was supported by grants from the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (2021-I2M-1-044). All authors would extend thanks to Baidu for the data publication and Sinosoft Company Limited for technical support. Publisher Copyright: © 2023 Chinese Academy of Engineering
Keywords: Baidu search engine, COVID-19, Deep learning, Epidemic curve, Influenza-like illness, Transmission dynamics model

Identifiers

Local EPrints ID: 483367
URI: http://eprints.soton.ac.uk/id/eprint/483367
ISSN: 2095-8099
PURE UUID: 5efafcdd-161e-47f8-9ced-fab617db1e1c
ORCID for Shengjie Lai: ORCID iD orcid.org/0000-0001-9781-8148

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Date deposited: 30 Oct 2023 11:58
Last modified: 06 Jun 2024 02:03

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Contributors

Author: Ting Zhang
Author: Liuyang Yang
Author: Xuan Han
Author: Guohui Fan
Author: Jie Qian
Author: Xuancheng Hu
Author: Shengjie Lai ORCID iD
Author: Zhongjie Li
Author: Zhimin Liu
Author: Luzhao Feng
Author: Weizhong Yang

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