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.

Evolving Epidemiology and Impact of Non-pharmaceutical Interventions on the Outbreak of Coronavirus Disease 2019 in Wuhan, China

“…In addition, many previous modeling studies have used the date of laboratory confirmation in the analysis without considering the long lag between onset and confirmation date for the early cases. [11][12][13] Moreover, several recent studies have reported a nonnegligible proportion of asymptomatic cases [14][15][16] and transmissibility of the asymptomatic or presymptomatic cases, 17-19 which were not considered by previous models.…”

Effects of Chinese strategies for controlling the diffusion and deterioration of novel coronavirus–infected pneumonia in China

“…To inhibit the spread of COVID-19 as quickly as possible, the Chinese government took actions rapidly and implemented a series of strategies [5,6,9] . For example, ( According to our study, we found that these Chinese strategies for controlling the diffusion of COVID-19 are significantly effective and powerful.…”

Feasibility Study of Mitigation and Suppression Strategies for Controlling COVID-19 Outbreaks in London and Wuhan

“…The next step is to estimate and normalize other parameters in the model. Assuming that COVID-19 has similar transmission ratio and incubation rate in all four cases, we use parameter values fitted from [11], where incubation rate is 1/7; the rate of transmission for the I to S is 0.157; the rate of transmission for the E to S is 0.787. As for estimation of other parameters, we follow the COVID-19 official report from WHO [15], including the proportion of Mild, Severe and Critical cases, the probability of death, etc.…”

“…The suppression strategy was recognised as the most effective solution to reduce the infectious population, where it was taken in Wuhan on 23 rd January 2020. In the work [11], Zhong has reported that taking suppression strategy has successfully limits the overall infectious population in Hubei on 22 nd February 2020 to 50K. And if the suppression strategy was taken one week earlier, this figure would reduce to 18K.…”