Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China

Abstract

Understanding the transmission dynamics of COVID-19 is crucial for evaluating its spread pattern, especially in metropolitan areas of China, as its spread could lead to secondary outbreaks. In addition, the experiences gained and lessons learned from China have the potential to provide evidence to support other metropolitan areas and large cities outside China with their emerging cases. We used data reported from January 24, 2020, to February 23, 2020, to fit a model of infection, estimate the likely number of infections in four high-risk metropolitan areas based on the number of cases reported, and increase the understanding of the COVID-19 spread pattern. Considering the effect of the official quarantine regulations and travel restrictions for China, which began January 23∼24, 2020, we used the daily travel intensity index from the Baidu Maps app to roughly simulate the level of restrictions and estimate the proportion of the quarantined population. A group of SEIR model statistical parameters were estimated using Markov chain Monte Carlo (MCMC) methods and fitting on the basis of reported data. As a result, we estimated that the basic reproductive number, R 0 , was 2.91 in Beijing, 2.78 in Shanghai, 2.02 in Guangzhou, and 1.75 in Shenzhen based on the data from January 24, 2020, to February 23, 2020. In addition, we inferred the prediction results and compared the results of different levels of parameters. For example, in Beijing, the predicted peak number of cases was 467 with a peak time of March 01, 2020; however, if the city were to implement different levels (strict, moderate, or weak) of travel restrictions or regulation measures, the estimation results showed that the transmission dynamics would change and that the peak number of cases would differ by between 54% and 209%. We concluded that public health interventions would reduce the risk of the spread of COVID-19 and that more rigorous control and prevention measures would Su et al. Epidemic Prediction of the COVID-19 effectively contain its further spread, and awareness of prevention should be enhanced when businesses and social activities return to normal before the end of the epidemic. Further, the experiences gained and lessons learned from China offer the potential to provide evidence supporting other metropolitan areas and big cities with their emerging cases outside China.

The COVID-19 research landscape

“…On the topic tracking analysis of epidemiology and public health interventions, we found that most of the early studies and reports were mainly focus on China's epidemic prevention and control. [ 38 , 39 ] By implementing a series of preventive control and medical treatment measures, the pandemic in China had been effectively contained, but the number of confirmed cases outside China continued to increase, as did the corresponding research on epidemiology and public health interventions, which was consistent with the continuously high popularity trending curve of this topic (blue curve), as displayed in Figure 6 .…”

A Compartmental Model that Predicts the Effect of Social Distancing and Vaccination on Controlling COVID-19

“…These can be taken as useful measures to evaluate the effectiveness of control policies and actions against the spreading of infectious diseases. Recently, several dynamical models have been presented to describe the dynamics of the evolution of COVID-19 and trying to predict the duration of epidemics and the number of infected individuals in different countries [4,5,6,7,8,9,10,11]. Note that COVID-19 as a disease caused by a new virus needs a model taking into consideration its known specic characteristics.…”

A machine learning based forecast model for the COVID-19 pandemic and investigation of the impact of government intervention on COVID-19 transmission in China

“…Using the Glob al Epidemic and Mobility Model, a study reported that the travel restrictions in Wuhan on January 23, 2020, had marked effects on reducing the spread of the new coronavirus infection globally, with an 80% importation reduction until mid-February (Chinazzi et al, 2020). Based on a dynamic transmission model, public health interventions were found to be effective in reducing the risk of 2019-nCoV spread (Su et al, 2020). Based on the regression method, a study found that, without the lockdown in Wuhan, the infected cases in other Hubei cities, excluding Wuhan, would have increased by 52.6% on February 29 (Fang et al, 2020).…”