The new crown pneumonia epidemic outbreak has had a severe impact on the economy and people's lives across China. Due to the mutation and contagious nature of the new crown pneumonia, economic recovery has been restricted. This article analyzes and predicts based on the data of eight provinces in China in the past six years (2015-2020), observes and discusses the degree of the economic impact of each province, and compares and analyzes the overall economic changes before, during, and after the epidemic. During the epidemic period, the eight provinces will be affected by the ten economic indicators. The eight provinces and China's economic recovery conditions will be put forward to achieve epidemic prevention and control. Implementation and recovery of economic development. In response to problem one of economic recovery forecasting, this article selects representative and relevant provinces from different regions of China for discussion. In order to avoid the concentration of selected provinces and the phenomenon of partial coverage, the Chinese regions are divided into Xinjiang and Qinghai from the northwest section, Sichuan and Yunnan from the southwest section, Hubei and Henan from the central China section, and from the northeast. Heilongjiang is selected for the lot, and Guangdong is selected from the South China lot. This article selects regional GDP, average real wage index, urban registered unemployment rate, and consumer price index as the consideration indicators. The four economic macro indicators are used to judge the goodness of fit through data fitting to establish a well-fitting correlation equation. Then, through the time series AR forecasting model, the forecast values of the four economic macro indicators for 2020 are calculated and compared with the actual values in the yearbook in 2020 to judge the overall economic changes in the middle of the epidemic. In addition, by comparing the year-on-year growth rate of economic development from January to March 2019-2021, the economic changes in the later epidemic period can be obtained. To obtain the degree of impact of the epidemic on each of the relevant economic indicators of the eight provinces, this paper uses the analytic hierarchy process to assign the degree of impact to the ten economic indicators to calculate the relevant weights, and conduct a consistency test by the consistency ratio. On this basis, this paper calculates the weighting influence of each province on each economic indicator, and then uses the total target weight as the basis for judging the severity of the impact of the new crown epidemic on each province, and compares and ranks it accordingly. Then, this article compares the weights of various economic indicators in each province to analyze the data results and find the objective reasons for the phenomena. In order to quantify the economic impact of each province, this paper uses the gray forecast GM (2,1) model to predict the economic error of GDP in 2021. Based on the first quarter of the GDP of each province in 2021, multiply it by 4 to get the theoretical annual GDP. Discuss the extent of the impact of the sporadic and small-scale outbreaks. Suppose the theoretical value is greater than the predicted value. In that case, it means that the province’s epidemic prevention and control has been done properly, the economy has recovered significantly, and currency circulation has increased. If the theoretical value is less than the predicted value, it means that the province is greatly affected by the repeated outbreaks and economic recovery is restricted. Regarding the economic impact of the entire country, we use GDP as a test indicator to predict the national GDP in 2021 through the gray forecast GM (2,1) model from GDP in 15-20 years. In the same way, based on the first and second quarters of 2021, the theoretical national GDP of 2021 is obtained by adding and multiplying by two, and comparing the GDP year-on-year growth rate with the predicted growth rate, the development trend and sporadic exposure to the epidemic can be obtained. The magnitude of the impact that occurred. Finally, this paper carries out error analysis and consistency check on the established model, and at the same time evaluates the advantages and disadvantages of the model.

Anka Dai, Ziqi Wang. Forecast of China's economic recovery in the context of the COVID-19 pandemic. Academic Journal of Business & Management (2021) Vol. 3, Issue 11: 83-91. https://doi.org/10.25236/AJBM.2021.031117.

[1] Hamzah F B, Lau C, Nazri H, et al. CoronaTracker: worldwide COVID-19 outbreak data analysis and prediction [J]. Bull World Health Organ, 2020, 1(32).

[2] Zhang H, Song H, Wen L, et al. Forecasting tourism recovery amid COVID-19 [J]. Annals of Tourism Research, 2021, 87: 103149.

[3] Li T. A SWOT analysis of China's air cargo sector in the context of COVID-19 pandemic [J]. Journal of air transport management, 2020, 88: 101875.

[4] Liu X, Liu Y, Yan Y. China macroeconomic report 2020: China’s macroeconomy is on the rebound under the impact of COVID-19 [J]. Economic and Political Studies, 2020, 8(4): 395-435.

[5] Yang Z, Zeng Z, Wang K, et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions [J]. Journal of thoracic disease, 2020, 12(3): 165.

[6] Song L, Zhou Y. The COVID-19 pandemic and its impact on the global economy: What does it take to turn crisis into opportunity? [J]. China & World Economy, 2020, 28(4): 1-25.

[7] Polyzos S, Samitas A, Spyridou A E. Tourism demand and the COVID-19 pandemic: An LSTM approach [J]. Tourism Recreation Research, 2020: 1-13.

[8] Barua S. Understanding Coronanomics: The economic implications of the coronavirus (COVID-19) pandemic [J]. Available at SSRN 3566477, 2020.

[9] Lau H, Khosrawipour V, Kocbach P, et al. The positive impact of lockdown in Wuhan on containing the COVID-19 outbreak in China [J]. Journal of travel medicine, 2020.

[10] Sharifi A, Khavarian-Garmsir A R. The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management [J]. Science of the Total Environment, 2020: 142391.