The coronavirus disease 2019 has emerged to become extremely serious and is affecting more than 200 countries and regions. On March 11, the World Health Organization (WHO) assessed COVID-19 as a pandemic 1 . The WHO has also determined that the global COVID-19 communication risk and impact risk level were very high 2 .
In December 2019, patients with pneumonia of unknown origin were reported in Wuhan, Hubei Province, China. By the end of January 2020, the epidemic had spread across the country. On January 29, confirmed cases have appeared in all provinces of Chinese mainland. On February 11, the International Committee on Taxonomy of Viruses officially named the new coronavirus as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has been proven that SARS-CoV-2 is transmitted from person to person through respiratory droplets and close contact 3 . Therefore, the occurrence of familial pneumonia is readily facilitated 4 . A range of timely and effective policies were implemented to control the spread of SARS-CoV-2. And the epidemic in China has now eased significantly. At the end of April 17 (in Beijing), 82,719 cases of COVID-19 had been confirmed in the mainland of China, with a total of 81,661 cases having been discharged and dead, and with 1,058 current cases.
In controlling the epidemic, it is critical to address the immediate problem, and to prevent a reoccurrence in the long term, which are both equally important 5 . The task now is not only to save lives, but also to improve the overall response to the epidemic 5 . After the spread of COVID-19, policy was proposed to control the spread of the SARS-CoV-2, which mean quarantine infected individuals to block This article is protected by copyright. All rights reserved. 
We selected Hubei Province and the next five provinces with the largest numbers of confirmed cases-Guangdong, Henan, Zhejiang, Hunan and Anhui provinces-for investigation. The number of current COVID-19 cases was collected from daily case reports of COVID-19 on the official website of the Health Committee of these provinces. The number of current cases, defined as the number of confirmed cases minus the number of cured cases and those who have died, was examined in this study. In this study, January 28 was the start time of the model, as day zero. This article is protected by copyright. All rights reserved.
The establishment of Policy-SEIR model Python 3.7 was used in this study to establish the Policy-SEIR model. Jupyter
Notebook was applied to implement the integrated development environment.
Using the traditional SEIR model, the population is divided into four categories: S represents the susceptible population; E indicates individuals in the incubation period after being infected; I indicates individuals who could infect susceptible individuals after the incubation period; and R represents individuals who have not affected the dynamics of epidemic transmission because of their immunity, effective isolation, and death. In the initial stage of an epidemic, susceptible individuals comprise almost the entire population, with only a few exposed and infected individuals.
To express the model more clearly, we adopted further specifications to represent the number of people in each category: S(t) represents the total number of susceptible individuals at time t; E(t) is the total number of individuals exposed at time t; I(t) is the cumulative number of infectious individuals after the incubation period at time t; and R(t) is the cumulative number of individuals who were removed from the epidemic process at time t. N represents the total number of people, giving the formula N = S (t) + E (t) + I (t) + R (t). When t = 0, N approaches S.
After the outbreak, the government introduced strict control measures, but the traditional SEIR model did not take these into account. The results of the traditional SEIR model were defective, and the predicted results differed significantly from the actual situation. It was necessary to adjust the SEIR model.
According to the known route of transmission, isolation measures are carried out to separate infected individuals into isolated and non-isolated categories. An This article is protected by copyright. All rights reserved.
isolated person is an individual who has been isolated and treated after being infected with SARS-CoV-2. A non-isolated person was an individual who had been infected with SARS-CoV-2, but who has not been isolated because no symptoms of infection are present and who has not been treated. In order to better reflect and predict the epidemic situation in the six provinces, we proposed the Policy-SEIR model based on the SEIR model, which considered whether the infected individuals were isolated or not. Iu and Is were added to the traditional model, which represent the infected but not isolated individuals and infected and isolated individuals, respectively. In addition, we considered current policies as strong policies, and evaluated the impact of different policy intensities on the results by changing the proportions of Iu and Is. The changed probabilities in the SEIR process are shown in Table 1 . The Policy-SEIR model process is shown in Figure 1 . The model consisted of the following equations:
The unknown parameters in the model were obtained through fitting the published data. A specific method was used to minimize the subsequent loss of mean square deviation in the search interval in the following equation:
Among the equation, is the real data and � is the data obtained through the fitting model. When the error between the model prediction and the real data was the smallest within the N-day observation range, relevant estimated parameters could be obtained.
Based cases, reached on day 19. Furthermore, according to the predicted results, the current cumulative confirmed cases tend to be stable after day 70 ( Fig. 2A) .
Considering policy as a factor in the model, there was a wide gap in the number of cases in terms of the intervention policies applied. After the implementation of strong interventional policies, the current number of COVID-19 cases was and smaller than that without policies. Without the application of such policies, the number of current cases was found to reach at 157,721. Under the current This article is protected by copyright. All rights reserved.
stronginterventional policies, the predicted peak number of current cases was smaller than under alternative scenarios in Figure 2B . Table 2 shows the forecast results and the actual results.
The Policy-SEIR model results of Guangdong Province are shown in Figure 3 .
The numbers of predicted and actual current cases were close, indicating that the predicted results were in line with the actual situation. The cumulative number of current confirmed cases continued to increase in the first 11 days, peaking on day 12 at 1010 and then declining, according to predicted results. In the actual situation, the peakreached 1007 on day 12. The model results suggested that the number of COVID-19 cases tended to flatten from day 60 (Fig. 3A) .
Interventional policies had a significant effect on the predicted results of the model. The number of cases would have been 1542 on day 20 without using interventional policies, which was far more than that would have occurred using current policies. (Fig. 3B )
The Policy-SEIR model results of Henan Province are shown in Figures 4 and 5 .
The numbers of predicted and actual current cases were close, revealing that the predicted results were similar to the actual situation. The cumulative number of current cases continued to increase in the first 14 days, peaking at 901 on day 15, and then declining, whereas the predicted number peaked at day 11. The model results suggested that the number of COVID-19 cases would be stable after day 60 (Fig. 4A) . The effect of interventional policies was significant, with the number of patients shown to reach 2068 on day 21 without interventional policies, which was considerably more than the figure with policies (Fig. 5A) .
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The Policy-SEIR model results of Zhejiang Province are shown in Figures 4 and   5 . The numbers of predicted and actual current cases were close, revealing that the predicted results were similar to the actual situation. The cumulative number of current cases continued to increase in the first 9 days, peaking at 921 on day 10, and then declining, whereas the predicted number peaking at 895. The model results suggested that the number of COVID-19 cases tended to flatten from day 70 (Fig. 4B) . The predicted number of patients would exceed 1000 on day 19
without less interventional policies (Fig. 5B) .
The Policy-SEIR model results of Hunan Province are shown in Figures 4 and 5 .
There was a difference between the predicted results and the actual results. The cumulative number of current cases peaked on day 13 at 698, and then declined, whereas, in the predicted results, the peak was 702 on day 10. The model results suggested that the curve of current COVID-19 cases in Hunan Province tended to be flatten from day 60 (Fig. 4C) . Policy intervention had a significant effect on the prediction results of the model, as the number of cases was shown as 968 on day 23 without interventional policies, which was far more than the figure with such policies (Fig. 5C) .
The Policy-SEIR model results of Anhui Province are shown in Figures 4 and 5 .
The numbers of predicted and actual current cases were similar, indicating that the predicted results were consistent with the actual situation. The cumulative number of current cases continued to increase in the first 13 days, peaking at 777 on day day 60 (Fig. 4D) . Policy intervention had a particularly significant effect on the increase in current cases according to the predicted results of the model. The peak number of cases would have been close to 6189 on day 35 without interventional policies, which far exceeded the number of cases occurring when applying policies and the situation would also have not become stable until after 140 days (Fig. 5D ).
According The segregation policy for confirmed cases adopted in China was extremely strict.
In Wuhan, according to the severity of the disease, people diagnosed with COVID-19 were quarantined in different places. Severely affected patients were isolated in special hospitals such as Huoshenshan and Leishenshan Hospital 14 .
People with mild infections were isolated in specific locations, known as "Fangcang" hospitals, which comprised former public buildings such as gymnasiums and conference centers that have been converted for medical purposes 15 . Unaffected individuals were isolated at home and reduced the number to go out home. Each local community had to ensure that temperature tests were undertaken for people entering or leaving the community. A study showed that transmission of COVID-19 can be effectively controlled through efficient contact tracking and case isolation 16 Transition rate of exposed individuals to infected individuals but not isolated 0.1 0-1 γ 2 Transition rate of exposed individuals to isolated 0.1 0-1