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 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.

Fig. 1

Susceptible, Exposed, Infectious, Recovered (SEIR) model diagram

Fig. 2

The estimation of the number of confirmed patients, dead patients, recovered patients and suspected patients based on multilayer perceptron. a, c, e, g The fitting effect on the number of confirmed patients, dead patients, recovered patients and suspected patients in China. b, d, f, h The loss curves fitting the number of confirmed patients, dead patients, recovered patients and suspected patients in China

Fig. 3

SEIR model of Wuhan and its dependence graph. a, b Box chart and line chart of recovery rate in Wuhan. c Line chart of the number of confirmed cases, recovered cases and dead cases in Wuhan. d, e Box chart and line chart of mortality rate in Wuhan. f SEIR model estimates the trend of epidemic situation in Wuhan

Fig. 4

SEIR model simulates the epidemic situation in Wuhan without additional beds

Fig. 5

SEIR model of Shanghai and its dependence graph. a, b Box chart and line chart of recovery rate in Shanghai. c Line chart of the number of confirmed cases, recovered cases and dead cases in Shanghai. d, e Box chart and line chart of mortality rate in Shanghai. f SEIR model simulates the trend of epidemic situation in Shanghai

Fig. 6

SEIR model of Beijing and its dependence graph. a, b Box chart and line chart of recovery rate in Beijing. c Line chart of the number of confirmed cases, recovered cases and dead cases in Beijing. d, e Box chart and line chart of mortality rate in Beijing. f SEIR model simulates the trend of epidemic situation in Beijing

Fig. 7

SEIR model of Guangzhou and its dependence graph. a, b Box chart and line chart of recovery rate in Guangzhou. c Line chart of the number of confirmed cases, recovered cases and dead cases in Guangzhou. d, e Box chart and line chart of mortality rate in Guangzhou. (F) SEIR model simulates the trend of epidemic situation in Guangzhou.

Fig. 8

The migrant population of three metropolises in the first quarter of 2019 and 2020. a Line chart of migration scale index and reduction of immigration in 2020 compared with 2019(%) of Beijing. b Line chart of migration scale index and reduction of immigration in 2020 compared with 2019(%) of Guangzhou. c Line chart of migration scale index and reduction of immigration in 2020 compared with 2019(%) of Shanghai.