Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis
Abstract Background To contain the outbreak of coronavirus disease 2019 (COVID-19) in China, many unprecedented intervention measures are adopted by the government. However, these measures may interfere in the normal medical service. We sought to model the trend of COVID-19 and estimate the restorat...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-05-01
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| Series: | Global Health Research and Policy |
| Online Access: | http://link.springer.com/article/10.1186/s41256-020-00145-4 |
| _version_ | 1811322976806633472 |
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| author | Zeye Liu Shuai Huang Wenlong Lu Zhanhao Su Xin Yin Huiying Liang Hao Zhang |
| author_facet | Zeye Liu Shuai Huang Wenlong Lu Zhanhao Su Xin Yin Huiying Liang Hao Zhang |
| author_sort | Zeye Liu |
| collection | DOAJ |
| description | Abstract Background To contain the outbreak of coronavirus disease 2019 (COVID-19) in China, many unprecedented intervention measures are adopted by the government. However, these measures may interfere in the normal medical service. We sought to model the trend of COVID-19 and estimate the restoration of operational capability of metropolitan medical service in China. Methods Real-time data of COVID-19 and population mobility data were extracted from open sources. SEIR (Susceptible, Exposed, Infectious, Recovered) and neural network models (NNs) were built to model disease trends in Wuhan, Beijing, Shanghai and Guangzhou. Combined with public transportation data, Autoregressive Integrated Moving Average (ARIMA) model was used to estimate the accumulated demands for nonlocal hospitalization during the epidemic period in Beijing, Shanghai and Guangzhou. Results The number of infected people and deaths would increase by 45% and 567% respectively, given that the government only has implemented traffic control in Wuhan without additional medical professionals. The epidemic of Wuhan (measured by cumulative confirmed cases) was predicted to reach turning point at the end of March and end in later April, 2020. The outbreak in Beijing, Shanghai and Guangzhou was predicted to end at the end of March and the medical service could be fully back to normal in middle of April. During the epidemic, the number of nonlocal inpatient hospitalizations decreased by 69.86%, 57.41% and 66.85% in Beijing, Shanghai and Guangzhou respectively. After the end of epidemic, medical centers located in these metropolises may face 58,799 (95% CI 48926–67,232) additional hospitalization needs in the first month. Conclusion The COVID-19 epidemic in China has been effectively contained and medical service across the country is expected to return to normal in April. However, the huge unmet medical needs for other diseases could result in massive migration of patients and their families, bringing tremendous challenges for medical service in major metropolis and disease control for the potential asymptomatic virus carrier. |
| first_indexed | 2024-04-13T13:45:55Z |
| format | Article |
| id | doaj.art-a60ef3f70da249ffbc0707f94bcc4327 |
| institution | Directory Open Access Journal |
| issn | 2397-0642 |
| language | English |
| last_indexed | 2024-04-13T13:45:55Z |
| publishDate | 2020-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Global Health Research and Policy |
| spelling | doaj.art-a60ef3f70da249ffbc0707f94bcc43272022-12-22T02:44:29ZengBMCGlobal Health Research and Policy2397-06422020-05-015111110.1186/s41256-020-00145-4Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysisZeye Liu0Shuai Huang1Wenlong Lu2Zhanhao Su3Xin Yin4Huiying Liang5Hao Zhang6State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical CollegeClinical Data Center, Guangzhou Women and Children’s Medical CenterState Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical CollegeState Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical CollegeSchool of Software & Microelectronics, Peking UniversityClinical Data Center, Guangzhou Women and Children’s Medical CenterHeart center and Shanghai Institute of Pediatric Congenital Heart Disease, Shanghai Children’s Medical Center, National Children’s Medical Center, Shanghai Jiaotong University School of MedicineAbstract Background To contain the outbreak of coronavirus disease 2019 (COVID-19) in China, many unprecedented intervention measures are adopted by the government. However, these measures may interfere in the normal medical service. We sought to model the trend of COVID-19 and estimate the restoration of operational capability of metropolitan medical service in China. Methods Real-time data of COVID-19 and population mobility data were extracted from open sources. SEIR (Susceptible, Exposed, Infectious, Recovered) and neural network models (NNs) were built to model disease trends in Wuhan, Beijing, Shanghai and Guangzhou. Combined with public transportation data, Autoregressive Integrated Moving Average (ARIMA) model was used to estimate the accumulated demands for nonlocal hospitalization during the epidemic period in Beijing, Shanghai and Guangzhou. Results The number of infected people and deaths would increase by 45% and 567% respectively, given that the government only has implemented traffic control in Wuhan without additional medical professionals. The epidemic of Wuhan (measured by cumulative confirmed cases) was predicted to reach turning point at the end of March and end in later April, 2020. The outbreak in Beijing, Shanghai and Guangzhou was predicted to end at the end of March and the medical service could be fully back to normal in middle of April. During the epidemic, the number of nonlocal inpatient hospitalizations decreased by 69.86%, 57.41% and 66.85% in Beijing, Shanghai and Guangzhou respectively. After the end of epidemic, medical centers located in these metropolises may face 58,799 (95% CI 48926–67,232) additional hospitalization needs in the first month. Conclusion The COVID-19 epidemic in China has been effectively contained and medical service across the country is expected to return to normal in April. However, the huge unmet medical needs for other diseases could result in massive migration of patients and their families, bringing tremendous challenges for medical service in major metropolis and disease control for the potential asymptomatic virus carrier.http://link.springer.com/article/10.1186/s41256-020-00145-4 |
| spellingShingle | Zeye Liu Shuai Huang Wenlong Lu Zhanhao Su Xin Yin Huiying Liang Hao Zhang Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis Global Health Research and Policy |
| title | Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis |
| title_full | Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis |
| title_fullStr | Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis |
| title_full_unstemmed | Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis |
| title_short | Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis |
| title_sort | modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in china a machine learning and mathematical model based analysis |
| url | http://link.springer.com/article/10.1186/s41256-020-00145-4 |
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