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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095809923003752 |
| _version_ | 1797220257455144960 |
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| author | Ting Zhang Liuyang Yang Xuan Han Guohui Fan Jie Qian Xuancheng Hu Shengjie Lai Zhongjie Li Zhimin Liu Luzhao Feng Weizhong Yang |
| author_facet | Ting Zhang Liuyang Yang Xuan Han Guohui Fan Jie Qian Xuancheng Hu Shengjie Lai Zhongjie Li Zhimin Liu Luzhao Feng Weizhong Yang |
| author_sort | Ting Zhang |
| collection | DOAJ |
| description | 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 resource demand. |
| first_indexed | 2024-04-24T12:46:40Z |
| format | Article |
| id | doaj.art-4d6cadf1c7ee4732aca6162d6afb51a6 |
| institution | Directory Open Access Journal |
| issn | 2095-8099 |
| language | English |
| last_indexed | 2024-04-24T12:46:40Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering |
| spelling | doaj.art-4d6cadf1c7ee4732aca6162d6afb51a62024-04-07T04:35:28ZengElsevierEngineering2095-80992023-12-0131112119Methods on COVID-19 Epidemic Curve Estimation During Emergency Based on Baidu Search Engine and ILI Traditional Surveillance in Beijing, ChinaTing Zhang0Liuyang Yang1Xuan Han2Guohui Fan3Jie Qian4Xuancheng Hu5Shengjie Lai6Zhongjie Li7Zhimin Liu8Luzhao Feng9Weizhong Yang10School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, ChinaSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; Department of Management Science and Information System, Faculty of Management and Economics, Kunming University of Science and Technology, Kunming 650504, ChinaSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, ChinaSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, ChinaSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, ChinaDepartment of Management Science and Information System, Faculty of Management and Economics, Kunming University of Science and Technology, Kunming 650504, ChinaWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UKSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, ChinaThe Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China; Corresponding authors.School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; Corresponding authors.School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; Corresponding authors.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 resource demand.http://www.sciencedirect.com/science/article/pii/S2095809923003752COVID-19Epidemic curveBaidu search engineInfluenza-like illnessDeep learningTransmission dynamics model |
| spellingShingle | Ting Zhang Liuyang Yang Xuan Han Guohui Fan Jie Qian Xuancheng Hu Shengjie Lai Zhongjie Li Zhimin Liu Luzhao Feng Weizhong Yang Methods on COVID-19 Epidemic Curve Estimation During Emergency Based on Baidu Search Engine and ILI Traditional Surveillance in Beijing, China Engineering COVID-19 Epidemic curve Baidu search engine Influenza-like illness Deep learning Transmission dynamics model |
| title | Methods on COVID-19 Epidemic Curve Estimation During Emergency Based on Baidu Search Engine and ILI Traditional Surveillance in Beijing, China |
| title_full | Methods on COVID-19 Epidemic Curve Estimation During Emergency Based on Baidu Search Engine and ILI Traditional Surveillance in Beijing, China |
| title_fullStr | Methods on COVID-19 Epidemic Curve Estimation During Emergency Based on Baidu Search Engine and ILI Traditional Surveillance in Beijing, China |
| title_full_unstemmed | Methods on COVID-19 Epidemic Curve Estimation During Emergency Based on Baidu Search Engine and ILI Traditional Surveillance in Beijing, China |
| title_short | Methods on COVID-19 Epidemic Curve Estimation During Emergency Based on Baidu Search Engine and ILI Traditional Surveillance in Beijing, China |
| title_sort | methods on covid 19 epidemic curve estimation during emergency based on baidu search engine and ili traditional surveillance in beijing china |
| topic | COVID-19 Epidemic curve Baidu search engine Influenza-like illness Deep learning Transmission dynamics model |
| url | http://www.sciencedirect.com/science/article/pii/S2095809923003752 |
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