Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions

Abstract

Background: The coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with chunyun, the period of mass migration for the annual Spring Festival. To contain its spread, China adopted unprecedented nationwide interventions on January 23 2020. These policies included large-scale quarantine, strict controls on travel and extensive monitoring of suspected cases. However, it is unknown whether these policies have had an impact on the epidemic. We sought to show how these control measures impacted the containment of the epidemic.

Methods: We integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic.

Results: We found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction.

Conclusions: Our dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.

Keywords: Coronavirus disease 2019 (COVID-19); Susceptible-Exposed-Infectious-Removed (SEIR); epidemic; modeling; severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Figure 1

Data used for our models. (A) Confirmed cases of COVID-19 by province as of February 10. Data obtained from https://voice.baidu.com/act/newpneumonia/newpneumonia/?from=osari_pc_3. (B) Migration index for Hubei, Guangdong and Zhejiang provinces during the spring festival holiday, 2020. Solid lines: inflow. Dashed lines: outflow. COVID-19, coronavirus disease 2019.

Figure 2

Number of active infections predicted by the modified SEIR model for (A) Hubei province under strict quarantine, (B) Hubei province under eased quarantine, (C) Guangdong province, (D) Zhejiang province and (E) China when interventions were introduced on January 23 (blue), five days later (grey) and five days earlier (red). Actual data of daily confirmed infections were fitted onto the curve (circles). SEIR, Susceptible-Exposed-Infectious-Removed.

Figure 3

LSTM prediction for mainland China. (A) LSTM-predicted cumulative number of COVID-19 cases in China. (B) Number of new COVID-19 cases according actual data (purple), SEIR-model (orange) and LSTM model (green). SEIR, Susceptible-Exposed-Infectious-Removed; LSTM, Long-Short-Term-Memory; COVID-19, coronavirus disease 2019.

Figure S1

Summary of control measures introduced in (A) Wuhan, (B) Hubei, (C) Zhejiang and (D) Guangdong.

Figure S2

New daily confirmed cases and cumulative confirmed cases reported by the National Health Commission between 26 January to 25 February 2020 for Hubei (A,B), Guangdong (C,D) and Zhejiang (E,F). Cumulative diagnosis (red), active diagnosis (pink) and suspected cases (yellow) between 26 January to 25 February 2020 for China (G). Data accessed from https://voice.baidu.com/act/newpneumonia/newpneumonia/?from=osari_pc_3 on February 26 2020.

Figure S3

Sensitivity of epidemic curve to the change incubation period, σ.

Figure S4

Time series of 2003 SARS CoV cumulative confirmed cases (A) and new confirmed cases (B). SARS CoV, severe acute respiratory syndrome coronavirus.

Figure S5

Result of the Ljung-Box (LB) test of SARS-CoV case data. SARS CoV, severe acute respiratory syndrome coronavirus.

Figure S6

LSTM inner structure. LSTM, Long-Short-Term-Memory.

Figure S7

LSTM network structure used. Input was a fixed time step data. This model used three days of new infections as input, input dimension (3,1). The Hidden Layer received input data from the Input Layer into the middle tier of the LSTM unit, set to 25. The Dense Layer received inputs from the output vector of the Middle Layer of the LSTM into the full-connection layer, from which the output was the final regression result. LSTM, Long-Short-Term-Memory.

Figure S8

AI learning process. AI, artificial intelligence.