# Political Will and the Impact Assessment of the New Crown Epidemic on Economic and Social Development and Countermeasures

Authors: Hongyan Wang; Min Lin
Journal: Journal of Environmental and Public Health
 (2022)

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## Abstract

Purpose. This paper aimed to study how to analyze and study economic and social development under the new crown epidemic based on the neural network and described the BP neural network. Methodology. Economic forecasts are affected by multiple influencing factors, the relationships between these factors are complex, and it is a nonlinear system with a high degree of uncertainty. The use of traditional forecasting methods has many limitations, and neural network methods can overcome these limitations and achieve good nonlinear forecasting. Research Findings. Through the analysis and statistics of the impact of the SARS epidemic and the new crown epidemic on the economy, by 2021, the economic contribution of final consumption expenditure, total capital formation, and net exports will be 65.4%, 13.7%, and 20.9%, respectively, and the impact of the current new crown virus epidemic on the economy will be greater than that of the SARS epidemic in 2003. Research Implications. The model applied to economic forecasting based on the BP network can achieve good forecasting effect, and scientific and reasonable forecasting methods depend on the in-depth understanding of economic activities and dominance of familiarity with economic theory. Practical Implications. Through the analysis of the economy in the context of political will and the new crown epidemic, it will give more reference to more and more complex emergencies in the future.


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## Body

## 1. Introduction

At the beginning of the new year, the sudden outbreak of the new crown epidemic in 2019 brought severe challenges to China’s economic development. The arrival of the epidemic coincides with the Spring Festival, which has had a huge impact on the service industry and business. However, over the past 40 years of reform and opening up, the Chinese government has accumulated rich experience in responding to emergencies and disasters. The fight against the SARS epidemic in 2003 brought the Chinese economy out of the fog. At present, with the rapid response to the epidemic, rich experience, greater prevention and control efforts, and more effective results, the China’s epidemic has been effectively controlled, and the new coronavirus pneumonia virus is currently spreading around the world. China’s companies began to restart production. This paper made beneficial improvements on the basis of the standard BP algorithm and analyzed the economic growth level data of SARS and the new crown epidemic, thereby improving the generalization ability of the network. The document showed that China’s economic activity has returned to normal as the China’s economy prioritizes emerging from the outbreak.In recent years, with the continuous changes in society, more and more studies have been conducted on economic and social development. De Neve et al. observed that proportions of abstract prosperity were over two times as delicate to negative monetary development as sure financial development. He utilized Gallup World Poll information from in excess of 150 nations, BRFSS information from 2.3 million US respondents, and Eurobarometer information covering over forty years of business cycles [1]. The reason for the Destek Asian review was to research the overall presentation of sustainable and non-environmentally friendly power utilization and financial development in 17 arising economies. To this end, yearly information from 1980 to 2012 was inspected utilizing a directed board causality that considers cross-sectional conditions among nations and country-explicit heterogeneity [2]. Zhang et al. revealed the relationship between electricity consumption and economic growth to achieve the goal of reducing energy consumption while improving the level of economic development. This work showed that the nature of China’s relations should and could be explored from a broader perspective, by developing a suitable comprehensive methodological framework [3]. Aneja et al. concentrated on analyzing the connection between energy utilization and financial development in the BRICS nations inside a multivariate board system from 1990 to 2012. At last, a board mistake revision system was applied to uncover the one-way causality from monetary development to inexhaustible and non-environmentally friendly power utilization [4]. Kahia et al. inspected the connection between energy use and financial development by decaying energy use into two kinds of energy, inexhaustible and non-environmentally friendly power use. Exact discoveries gave proof to short- and long-haul bidirectional causal relationships between inexhaustible and non-environmentally friendly power use, which showed substitutability and association between the two energy sources [5]. However, studies that incorporate current developments are only a minority.As a significant technique for AI, BP neural network has been effectively applied in man-made consciousness, design acknowledgment, picture handling, and different fields. In order to determine the heat transfer coefficient in the range of supercritical water pressure, Ma et al. collected 14 sets of experimental data, and a BP prediction model for determining the heat transfer coefficient of supercritical water was established based on it [6]. Li et al. proposed a new method combining chaotic algorithm and genetic algorithm, aiming at the shortcomings that BP is easy to fall into local minima and slow convergence speed in gesture recognition [7]. Pan et al. proposed a complex input feature importance calculation method based on the BP neural network for multiple input attributes of multiclassification output results according to the correlation and importance issues [8]. Zhang et al. optimized the BP network weights and limited selection process to improve the inversion results and then obtained a better network model, which was used for the inversion of the density interface model [9]. Huichun et al. extracted the total value of the nasal electronic response signal as a characteristic parameter and used a BP neural network to build a prediction model for zearalenone and aflatoxin in different grades of corn mold samples [10]. These algorithms provide solutions to problems in the research field to a certain extent, but the accuracy needs to be further improved.Through the analysis of the economic impact of the 2003 epidemic and the new crown epidemic, this epidemic will promote technological changes and promote the rapid development of new economies such as e-commerce and smart medical care could be predicted, which will bring a series of important new opportunities for China’s industrial restructuring. The innovation of this paper is to combine the economic and social development with BP neural network and introduced the principle and related methods of BP in detail.


## 2. Economic Forecasting Method Based on Neural Network

### 2.1. Artificial Neural Networks and Economic Forecasting

Artificial neural network (ANN) is a physical model that simulates and reflects the structure, principle, and function of the human brain neural network. It consists of a large number of neurons in a specific way to form a complex dynamic network, as shown in Figure1. The main difference between nerve cells and other cells in the body is that nerve cells can generate, process, and transmit signals. It is mathematically proven that artificial neural networks can approximate any function that characterizes the regularity of sample data, no matter what form those functions take. The good nonlinear approximation, fault tolerance, generalization ability, and self-learning characteristics of the artificial neural network make it have a place in various fields such as pattern recognition, speech recognition, and signal processing [11, 12].Figure 1
Neuron model.The accuracy of economic forecasts depends not only on understanding the history and current situation of the region and the accuracy of the initial data obtained but also on the scientific and advanced nature of forecasting methods. Scientific and reasonable forecasting methods mainly depend on the in-depth understanding of economic activities and the dominance of familiarity with economic theory. By and large, financial gauging strategies can be generally isolated into two classifications: subjective estimating techniques and quantitative determining techniques [13, 14].The commonly used qualitative methods mainly include AHP, market research, and subjective probability method. Quantitative forecasting methods are mainly based on historical statistics and data, and scientific methods must be used to build specific mathematical models. The analysis and forecasting results of economic phenomena are usually expressed as specific numerical values. At this stage, quantitative analysis is an important method for regional economic forecasting. According to the understanding of the economic growth phenomenon, quantitative forecasting methods can be divided into deterministic model methods and uncertainty model methods. Identify relevant analysis models, including dynamic programming, regression analysis, time series analysis, input-output analysis, and econometric model methods. In general, the relationship between them is difficult to describe by traditional forecasting methods, so the uncertainty forecasting method has its unique advantages in solving forecasting problems, and therefore, it has achieved huge growth in recent years. Some intelligent prediction methods have been developed recently, such as artificial neural networks, gray prediction analysis, and fuzzy analysis [15]. The artificial network method is the main method used in this paper. Common methods of economic forecasting are shown in Figure 2.Figure 2
Common methods of economic forecasting.


### 2.2. BP Neural Network Algorithm

Economic forecasting is a very important and complex task, which can provide a scientific basis for the government, enterprises, and other relevant departments to understand the future economic operation, evaluate growth opportunities, and formulate development strategies.The BP algorithm uses the gradient steepest descent method to adjust the connection weights of the hidden layer and the output layer to obtain the smallest error. The specific error indicator generally uses the error mean square value. Usually, after the neural network is calculated in the forward direction, the error will be propagated and used as the input to correct the weight parameter. The whole learning process is that the error is diffused again and the weights of each layer are corrected.The whole process of forming a BP neural network can be summarized as follows: after the input variables were processed on each layer of neurons, the corresponding output was received at the output stage, which is the forward propagation stage. The output obtained after forward propagation was compared with the expected output to obtain the mean squared value of the error as a variable for inverse correction of the weight factors of the hidden and output layers. The ultimate goal is to bring the mean squared error to the desired minimum value, thereby minimizing the error signal for the entire system [16].In the forward transmission stage, when the result signal spread to the secret layer and the result layer, the sign strength was enhanced or debilitated by the association loads of each layer, and the contribution of the secret layer is the weighted amount of the association loads of every hub and the info layer. The secret layer standardized the spotless contribution to a scope of sign qualities through an enactment capability as the contribution to the result layer [17]. For the information layer, the section of a hub is the exit of the layer. The construction is displayed in Figure 3.Figure 3
BPNN structure diagram.BPNN has been broadly utilized in different fields, and its precise expectation and straightforward activity make BPNN lean toward by numerous researchers. BPNN has the accompanying attributes:(1)
It can solve the problem of complex internal mechanism. The samples are continuously learned and trained through the guidance of the BPNN, which can continuously improve the learning ability and storage ability.(2)
It has a powerful self-learning function. The network is trained to provide reasonable output to the new dataset, showing a more logical mapping in any scenario.(3)
The pattern can be summarized in the sample. By discovering and solving practical problems, it continuously strengthens its own capabilities, optimizes memory and solving algorithms, and quickly completes nonlinear mapping operations from the input layer to the output layer.(4)
It has the ability to process complex information collaboratively and without interfering with each other. Each neuron part in the BP network has the functions of receiving, processing, and outputting information independently [18]. That is to say, neurons in the same layer can calculate and process information at the same time and then output to the next layer of the network for processing, enhancing the real-time performance of the network.(5)
It has strong fault tolerance. It can also be processed and calculated when the input samples have relatively large errors.The specific option designed in this paper is a three-level neural network. The three nodes of the output layer are used to control the digital PID controllerZP, ZP, ZD. At the same time, since the parameters cannot be negative, the output layer activation function selects a nonnegative sigmoid function. There is no such requirement for the output of the hidden layer; therefore, the hidden layer activation function selects positive and negative symmetric sigmoid functions [19, 20].The BP algorithm consists of two processes as follows:(1)
Forward propagation of information: the input vector was sent to the hidden layer through the input layer, and then the output layer outputted, generating the output vector. During the transmission process, the weights of the neural network remained unchanged. If the expected output value was not reached, the weights would be adjusted through error backpropagation.(2)
Backpropagation of the error: the result layer sent the mistake between the genuine result of the organization and the normal result to the secret layer and afterward to the info layer. The blunder input component would constantly change and right the loads of the brain organization and iterated over and again to make the organization yield equivalent to the normal result.BPNN learning process is shown in Figure4.Figure 4
BPNN learning process.The particular result of the BPNN calculation is a managed learning calculation. The essential logical reason for its foundation is the angle plummet technique. The angle plummet strategy expects that the initiation elements of the secret layer and the result layer are ceaseless and scaled down. Simultaneously, the blunder esteem was determined right now of the forward spread stage, and the mean square worth of the mistake got by computing the result and the normal result was utilized as the coefficient for changing the association loads of each layer in the back engendering stage. A definitive objective is to carry the mean squared mistake to the ideal least worth, subsequently limiting the blunder signal for the whole framework.The gradient descent method was used in the correction of the connection weights, which requires the activation function to have continuous differentiability, and the sigmoid function satisfied this condition. At the same time, the derivative could be obtained by using the original function, which greatly reduced the difficulty of the operation. The specific function of the sigmoid function is as follows:(1)fx=11+e−x.Derive it as follows:(2)f′x=e−x1+e−x2=fx1−fx.The extraordinary learning and preparation of BPNN calculation are for the most part separated into three phases: forward propagation stage, backward error propagation stage, and weight update stage. It was assumed that Figure3 is a three-layer BP organization, including 3 information hubs, 5 secret layer hubs, and 3 result hubs. According to these three stages, the BP algorithm was analyzed concretely.The result of the organization input layer is as follows:(3)Qa1xa,a=1,2,3.The information and result calculation of the secret layer of the organization is as follows:(4)netb2=∑b=13vab2Qa1,(5)Qa1a=netb2z,b=1,2,…,5.In formulas (4) and (5), the weight factor of the hidden layer was denoted as vab2, and in the superscript, (1) was denoted as the input layer, (2) was denoted as the hidden layer, and (3) was denoted as the output layer. Specifically, f referred to the function that activates the hidden layer neurons. The input and output of the network output stage are Formula (6) and Formula (7), respectively:(6)neti3=∑i=15vib3Qb2,(7)Qi3z=gneti3z,(8)Q13z=ZP,Q23z=ZI,Q33z=ZD.In formula (8), the weight factor of the output level was denoted as vib3, and the function of the activation output level was denoted as g. Since ZP, ZI, ZD cannot be negative, g has a nonnegative sigmoid function here.After the forward propagation phase was completed, the specific error function means square value was calculated as follows:(9)B=12rz+1−yz+12.The blunder back-spread stage is to perform a negative angle remedy on the weight coefficients of the secret layer and the result layer with the mean square worth of the mistake determined above, in order to acquire the addition of the weight coefficients of each layer:(10)vib3z+1=−μαBαvib3+βvib3z.In formula (10), μ is the learning rate, which took a value between 0 and 1, such as μ = 0.2∼0.5; β is the inertia coefficient, which was mainly used to speed up the convergence speed. Usually, a constant between 0 and 1 could be obtained. In BP neural network, the learning speed has a great influence. In the initial stage of neural network training, when the value of β was large, the effect of rapid convergence could be obtained. However, when approaching the optimal point, the value of β must be relatively small to prevent the neural network from entering a nonconvergence state.After the blunder was back-engendered, the weight coefficients of the result layer and the secret layer would be changed by the mistake, that is to say, the weight coefficients of the secret layer and the result layer would be changed by the negative angle heading, and the targeting ability is the main reason for the completion of the brain organization.(11)vib3z+1=−μαBαvib3=−μαBαvib3Qb2.Definition 1.
(12)φi3=−μαBαnetib3=ez+1×Qi3×1−Qi3.
Thereby, the weight coefficient correction amount of the output layer was obtained as follows:(13)Δvib3z+1=μ×ez+1×Qi3×1−Qi3×Qi2.
Among them,μ is the learning rate; Qi2 represents the output of the hidden layer node; ez+1 represents the deviation value of the input of the controller at the z + 1th sampling time; Qi3 represents the output of the output layer pole.
Thus, the incremental calculation of the weighted coefficient correction of the output layer could be obtained as follows:(14)vib3z+1=vib3z=Δvib3z+1.
Also, as indicated by the slope strategy, it very well may be realized that the change boundaries of the weight coefficient of the secret layer of the BPNN were as follows:(15)Δvib2z+1=−μαBαvb2Qa1.Definition 2.
(16)φi2=−αBαnetb2=−αBαQb2×Qb2×1−Qb2.
The node output of the hidden layer would directly change the output of all nodes in the output plane connected to it, that is,(17)−αBαQb2=−∑i=13αBαnetb3×αnetb3αQb2,=−∑i=13αBαnetb3×ααQb2∑i=15vib3Qb2,=−∑i=13−αBαnetb3×vib3,=−∑i=13φi3×vib3.
Therefore, the modulation parameters of the hidden layer weight coefficients were obtained as follows:(18)Δvab2z+1=μ×φb2×φi3,=μ∑i=13φb3×vib3×Qb21−Qb3.
The incremental calculation for the weighting coefficient correction of the output layer was as follows:(19)vab2z+1=vab2z+Δvab2z+1.


## 2.1. Artificial Neural Networks and Economic Forecasting

Artificial neural network (ANN) is a physical model that simulates and reflects the structure, principle, and function of the human brain neural network. It consists of a large number of neurons in a specific way to form a complex dynamic network, as shown in Figure1. The main difference between nerve cells and other cells in the body is that nerve cells can generate, process, and transmit signals. It is mathematically proven that artificial neural networks can approximate any function that characterizes the regularity of sample data, no matter what form those functions take. The good nonlinear approximation, fault tolerance, generalization ability, and self-learning characteristics of the artificial neural network make it have a place in various fields such as pattern recognition, speech recognition, and signal processing [11, 12].Figure 1
Neuron model.The accuracy of economic forecasts depends not only on understanding the history and current situation of the region and the accuracy of the initial data obtained but also on the scientific and advanced nature of forecasting methods. Scientific and reasonable forecasting methods mainly depend on the in-depth understanding of economic activities and the dominance of familiarity with economic theory. By and large, financial gauging strategies can be generally isolated into two classifications: subjective estimating techniques and quantitative determining techniques [13, 14].The commonly used qualitative methods mainly include AHP, market research, and subjective probability method. Quantitative forecasting methods are mainly based on historical statistics and data, and scientific methods must be used to build specific mathematical models. The analysis and forecasting results of economic phenomena are usually expressed as specific numerical values. At this stage, quantitative analysis is an important method for regional economic forecasting. According to the understanding of the economic growth phenomenon, quantitative forecasting methods can be divided into deterministic model methods and uncertainty model methods. Identify relevant analysis models, including dynamic programming, regression analysis, time series analysis, input-output analysis, and econometric model methods. In general, the relationship between them is difficult to describe by traditional forecasting methods, so the uncertainty forecasting method has its unique advantages in solving forecasting problems, and therefore, it has achieved huge growth in recent years. Some intelligent prediction methods have been developed recently, such as artificial neural networks, gray prediction analysis, and fuzzy analysis [15]. The artificial network method is the main method used in this paper. Common methods of economic forecasting are shown in Figure 2.Figure 2
Common methods of economic forecasting.


## 2.2. BP Neural Network Algorithm

Economic forecasting is a very important and complex task, which can provide a scientific basis for the government, enterprises, and other relevant departments to understand the future economic operation, evaluate growth opportunities, and formulate development strategies.The BP algorithm uses the gradient steepest descent method to adjust the connection weights of the hidden layer and the output layer to obtain the smallest error. The specific error indicator generally uses the error mean square value. Usually, after the neural network is calculated in the forward direction, the error will be propagated and used as the input to correct the weight parameter. The whole learning process is that the error is diffused again and the weights of each layer are corrected.The whole process of forming a BP neural network can be summarized as follows: after the input variables were processed on each layer of neurons, the corresponding output was received at the output stage, which is the forward propagation stage. The output obtained after forward propagation was compared with the expected output to obtain the mean squared value of the error as a variable for inverse correction of the weight factors of the hidden and output layers. The ultimate goal is to bring the mean squared error to the desired minimum value, thereby minimizing the error signal for the entire system [16].In the forward transmission stage, when the result signal spread to the secret layer and the result layer, the sign strength was enhanced or debilitated by the association loads of each layer, and the contribution of the secret layer is the weighted amount of the association loads of every hub and the info layer. The secret layer standardized the spotless contribution to a scope of sign qualities through an enactment capability as the contribution to the result layer [17]. For the information layer, the section of a hub is the exit of the layer. The construction is displayed in Figure 3.Figure 3
BPNN structure diagram.BPNN has been broadly utilized in different fields, and its precise expectation and straightforward activity make BPNN lean toward by numerous researchers. BPNN has the accompanying attributes:(1)
It can solve the problem of complex internal mechanism. The samples are continuously learned and trained through the guidance of the BPNN, which can continuously improve the learning ability and storage ability.(2)
It has a powerful self-learning function. The network is trained to provide reasonable output to the new dataset, showing a more logical mapping in any scenario.(3)
The pattern can be summarized in the sample. By discovering and solving practical problems, it continuously strengthens its own capabilities, optimizes memory and solving algorithms, and quickly completes nonlinear mapping operations from the input layer to the output layer.(4)
It has the ability to process complex information collaboratively and without interfering with each other. Each neuron part in the BP network has the functions of receiving, processing, and outputting information independently [18]. That is to say, neurons in the same layer can calculate and process information at the same time and then output to the next layer of the network for processing, enhancing the real-time performance of the network.(5)
It has strong fault tolerance. It can also be processed and calculated when the input samples have relatively large errors.The specific option designed in this paper is a three-level neural network. The three nodes of the output layer are used to control the digital PID controllerZP, ZP, ZD. At the same time, since the parameters cannot be negative, the output layer activation function selects a nonnegative sigmoid function. There is no such requirement for the output of the hidden layer; therefore, the hidden layer activation function selects positive and negative symmetric sigmoid functions [19, 20].The BP algorithm consists of two processes as follows:(1)
Forward propagation of information: the input vector was sent to the hidden layer through the input layer, and then the output layer outputted, generating the output vector. During the transmission process, the weights of the neural network remained unchanged. If the expected output value was not reached, the weights would be adjusted through error backpropagation.(2)
Backpropagation of the error: the result layer sent the mistake between the genuine result of the organization and the normal result to the secret layer and afterward to the info layer. The blunder input component would constantly change and right the loads of the brain organization and iterated over and again to make the organization yield equivalent to the normal result.BPNN learning process is shown in Figure4.Figure 4
BPNN learning process.The particular result of the BPNN calculation is a managed learning calculation. The essential logical reason for its foundation is the angle plummet technique. The angle plummet strategy expects that the initiation elements of the secret layer and the result layer are ceaseless and scaled down. Simultaneously, the blunder esteem was determined right now of the forward spread stage, and the mean square worth of the mistake got by computing the result and the normal result was utilized as the coefficient for changing the association loads of each layer in the back engendering stage. A definitive objective is to carry the mean squared mistake to the ideal least worth, subsequently limiting the blunder signal for the whole framework.The gradient descent method was used in the correction of the connection weights, which requires the activation function to have continuous differentiability, and the sigmoid function satisfied this condition. At the same time, the derivative could be obtained by using the original function, which greatly reduced the difficulty of the operation. The specific function of the sigmoid function is as follows:(1)fx=11+e−x.Derive it as follows:(2)f′x=e−x1+e−x2=fx1−fx.The extraordinary learning and preparation of BPNN calculation are for the most part separated into three phases: forward propagation stage, backward error propagation stage, and weight update stage. It was assumed that Figure3 is a three-layer BP organization, including 3 information hubs, 5 secret layer hubs, and 3 result hubs. According to these three stages, the BP algorithm was analyzed concretely.The result of the organization input layer is as follows:(3)Qa1xa,a=1,2,3.The information and result calculation of the secret layer of the organization is as follows:(4)netb2=∑b=13vab2Qa1,(5)Qa1a=netb2z,b=1,2,…,5.In formulas (4) and (5), the weight factor of the hidden layer was denoted as vab2, and in the superscript, (1) was denoted as the input layer, (2) was denoted as the hidden layer, and (3) was denoted as the output layer. Specifically, f referred to the function that activates the hidden layer neurons. The input and output of the network output stage are Formula (6) and Formula (7), respectively:(6)neti3=∑i=15vib3Qb2,(7)Qi3z=gneti3z,(8)Q13z=ZP,Q23z=ZI,Q33z=ZD.In formula (8), the weight factor of the output level was denoted as vib3, and the function of the activation output level was denoted as g. Since ZP, ZI, ZD cannot be negative, g has a nonnegative sigmoid function here.After the forward propagation phase was completed, the specific error function means square value was calculated as follows:(9)B=12rz+1−yz+12.The blunder back-spread stage is to perform a negative angle remedy on the weight coefficients of the secret layer and the result layer with the mean square worth of the mistake determined above, in order to acquire the addition of the weight coefficients of each layer:(10)vib3z+1=−μαBαvib3+βvib3z.In formula (10), μ is the learning rate, which took a value between 0 and 1, such as μ = 0.2∼0.5; β is the inertia coefficient, which was mainly used to speed up the convergence speed. Usually, a constant between 0 and 1 could be obtained. In BP neural network, the learning speed has a great influence. In the initial stage of neural network training, when the value of β was large, the effect of rapid convergence could be obtained. However, when approaching the optimal point, the value of β must be relatively small to prevent the neural network from entering a nonconvergence state.After the blunder was back-engendered, the weight coefficients of the result layer and the secret layer would be changed by the mistake, that is to say, the weight coefficients of the secret layer and the result layer would be changed by the negative angle heading, and the targeting ability is the main reason for the completion of the brain organization.(11)vib3z+1=−μαBαvib3=−μαBαvib3Qb2.Definition 1.
(12)φi3=−μαBαnetib3=ez+1×Qi3×1−Qi3.
Thereby, the weight coefficient correction amount of the output layer was obtained as follows:(13)Δvib3z+1=μ×ez+1×Qi3×1−Qi3×Qi2.
Among them,μ is the learning rate; Qi2 represents the output of the hidden layer node; ez+1 represents the deviation value of the input of the controller at the z + 1th sampling time; Qi3 represents the output of the output layer pole.
Thus, the incremental calculation of the weighted coefficient correction of the output layer could be obtained as follows:(14)vib3z+1=vib3z=Δvib3z+1.
Also, as indicated by the slope strategy, it very well may be realized that the change boundaries of the weight coefficient of the secret layer of the BPNN were as follows:(15)Δvib2z+1=−μαBαvb2Qa1.Definition 2.
(16)φi2=−αBαnetb2=−αBαQb2×Qb2×1−Qb2.
The node output of the hidden layer would directly change the output of all nodes in the output plane connected to it, that is,(17)−αBαQb2=−∑i=13αBαnetb3×αnetb3αQb2,=−∑i=13αBαnetb3×ααQb2∑i=15vib3Qb2,=−∑i=13−αBαnetb3×vib3,=−∑i=13φi3×vib3.
Therefore, the modulation parameters of the hidden layer weight coefficients were obtained as follows:(18)Δvab2z+1=μ×φb2×φi3,=μ∑i=13φb3×vib3×Qb21−Qb3.
The incremental calculation for the weighting coefficient correction of the output layer was as follows:(19)vab2z+1=vab2z+Δvab2z+1.


## 3. Experiment on the Impact of the New Crown Epidemic on Economic and Social Development

### 3.1. Impact of the New Crown Epidemic on the Economy Will Exceed That of the SARS Epidemic

In terms of economic structure, the current share of the tertiary industry and consumption is much higher than in 2003, and the epidemic has had a greater impact on the service industry and consumption.In 2003, the essential, optional, and tertiary ventures represented 12.4%, 45.6%, and 42.0% of GDP at current costs, separately, and their commitment rates to monetary development were 3.1%, 57.9%, and 39.0% separately. The optional business was the foundation of the economy. In 2021, the essential, optional, and tertiary ventures represented 7.3%, 39.4%, and 53.3%, separately. The primary and secondary industries decreased by 5.1 and 6.2 percentage points, respectively, and the tertiary increased by 11.3 percentage units compared with 2003. The commitment paces of definite utilization consumption, gross capital arrangement, and net commodities of labor and products to monetary development were 5.3, 1.1, and 1.7 rate focuses separately, and the financial development rates were 65.4%, 13.7%, and 20.9% individually. The commitment pace of the tertiary business to financial development was 59.4%, an increment of 20.4 rate focuses north of 2003. In 2021, the financial commitment paces of definite utilization use, gross capital arrangement, and net commodities were 65.4%, 13.7%, and 20.9%, separately. The commitment pace of complete capital development was lower than that in 2003, at 52.7%. The economic impact of the current COVID-19 outbreak will be greater than the economic impact of the SARS outbreak in 2003 (see Figure5), which has a greater impact on services and consumption.Figure 5
Comparison of 2003 and 2021. (a) GDP comparison (b) Contribution rate comparison of economic growth.
(a)(b)From a national perspective, consumption has been significantly affected. In May 2003, total retail sales of consumer goods grew at an average annual rate of 4.3%, compared with 7.7% in April and 8.3% in June. Investments were not affected. In the whole year of 2003, fixed asset investment did not decline significantly, but increased significantly in April and May (see Table1). Imports and exports had little impact, which fluctuated only in April 2003. From March to May 2003, the growth rates of exports were 34.7%, 33.3%, and 37.3%, and imports were 45.1%, 34.4%, and 40.9%, respectively (see Table 2).Table 1
Total retail sales of consumer goods.
YearMonthTotal retail sales (%)200228.358.088.6118.9200328.254.7810.21110.12004210.3518.4813.91114.6Table 2
Import and export growth rate from March to May 2003.
YearMonthExport growth (%)Import growth (%)2003334.745.1433.334.4537.340.9


### 3.2. China’s GDP Growth under the Impact of the Epidemic

The SARS epidemic began in November 2002 and experienced a long incubation period. It did not spread quickly and did not peak until April and May 2003. Therefore, it had no obvious impact on economic growth in the first quarter of 2003. The annual GDP growth rate fell from 11.1% in the first quarter of 2003 to 9.1% in the second quarter, and the monthly growth rate fell from over 12% to 3.5%. The SARS epidemic was brought under control in the third quarter, and economic growth gradually recovered to 10% (Figure6). According to the experience of the SARS epidemic, it took about two months from the rapid outbreak of the epidemic to the control of the epidemic. Likewise, the timing of the economic impact of the coronavirus outbreak is mainly concentrated in the first quarter of 2021.Figure 6
The economic changes in 2003.China’s GDP in 2021 reached 114.37 trillion, a year-on-year increase of 8.1%. It was equivalent to 17.73 trillion US dollars, and the per capita GDP was 12, 551 US dollars. It was a stone’s throw away from the per capita threshold of US$12, 736 in high-income countries.In 2021, China’s GDP grew by 8.1% year-on-year, which was not only higher than the 6% growth level set by the government work report in March last year but also slightly higher than the 8% growth level predicted by international financial institutions and economic organizations.At the quarterly level, yearly development was 18.3% in the main quarter, 7.9% in the subsequent quarter, 4.9% in the third quarter from last quarter, and 4.0% in the final quarter. It was clear that in the four quarters of 2021, GDP growth would trend higher towards the end of the year. The situation appeared to be clearer while adding in the growth in the four quarters of 2020 (see Figure7).Figure 7
China’s GDP growth rate. (a) The year of 2020. (b) The year of 2020.
(a)(b)In Figure7, it very well may be seen that in the primary quarter of 2021, GDP expanded by 18.3% year-on-year, which was excessively quick. The explanation is that the effect of the scourge in 2020 was moderately low, and the quickest year-on-year development was normal. In any case, the descending tension on the large-scale economy has expanded essentially after the second from last quarter of 2021, with a year-on-year increment of 4.9% and 4.0% in the third and fourth quarters. A similar period in 2020 was contrasted, and the development rate has dialed back essentially, which was below the normal development pace of 5.1% in the two years.The year-on-year development pace of modern added esteem above assigned size and the development pace of all-out retail deals of customer products are displayed in Figure8(a), and the month-to-month development of all out import and commodity volume is displayed in Figure 8(b).Figure 8
Growth rate. (a) Year-on-year growth rate of industrial added value and growth rate of total retail sales of consumer goods. (b) Monthly growth of total imports and exports.
(a)(b)As can be seen from Figure8, the 30% year-on-year increase in total import and export volume exceeding expectations was the main contribution to GDP growth in 2021. The growth rate of investment in fixed assets accelerated, the growth rate of industrial output above the designated size accelerated, and the growth rate of total retail sales of consumer goods was slow.


### 3.3. New Crown Epidemic Brings New Opportunities

There is often a turning point behind a crisis, and the outbreak has also drawn attention to life, health, and smart cities. It will promote technological change, promote the rapid development of new water quality of economies such as e-commerce and smart healthcare, and provide a series of important new opportunities for China’s industrial restructuring (see Figure9).Figure 9
New opportunities for China’s industrial restructuring.The first is the accelerated development of the mass health industry. The outbreak of the epidemic has greatly increased people’s awareness of life and health and greatly increased the demand for medical equipment such as gas masks and related raw materials.The second is to further accelerate the construction of smart cities. The SARS epidemic in 2003 led to the rapid development of Alipay and Taobao. There is no doubt that the epidemic will change the way people go from online shopping to online shopping and will deepen the development of industries such as e-commerce, modern logistics, and the new economy.The third is the rapid rise of the “home economy”. Homestay economy is a new concept that emerged with the advent of the Internet, which mainly refers to working from home or engaging in professional work such as online entertainment, e-education, and learning, including using the Internet at home. Office and consumption patterns have changed dramatically, moving offices from one unit to another. The “home economy” was formed spontaneously in the past, but this time, due to the need to prevent and control the epidemic, some provincial and municipal governments have given support and encouragement. The rapid rise of the home economy will promote the development of the Internet celebrity economy, community economy, and platform economy.The fourth is the green buildings are gradually developing. The rapid construction and live broadcast of Huoshenshan Hospital and Leishenshan Hospital created a miracle in Wuhan, demonstrated the great potential of green buildings and intelligent buildings in Hubei Province, and showed the world the speed and strength of Chinese architecture. Therefore, prefabricated buildings, intelligent buildings, and energy-saving and environmentally friendly buildings will achieve huge growth, and Chinese construction will go overseas.The fifth is to further develop emergency industrial and military-political integration. In the fight against the epidemic, the People’s Liberation Army has provided strong emergency support in various aspects including medical treatment, transportation, and chemical prevention. The newly built Huoshenshan Hospital has been handed over to all the people’s soldiers, creating living conditions for military-civilian unity and introducing new associations for the further development of military-political integration.The new business form and the new model triggered by the epidemic will continue to promote the development of the industry in the direction of green, digital, networked, intelligent, and coordinated development, which is completely consistent with the trend of industrial transformation and upgrading and brings new opportunities to industrial development. It will help accelerate the transformation of new and old power, promote high-quality economic growth, and cultivate new economic growth points.


## 3.1. Impact of the New Crown Epidemic on the Economy Will Exceed That of the SARS Epidemic

In terms of economic structure, the current share of the tertiary industry and consumption is much higher than in 2003, and the epidemic has had a greater impact on the service industry and consumption.In 2003, the essential, optional, and tertiary ventures represented 12.4%, 45.6%, and 42.0% of GDP at current costs, separately, and their commitment rates to monetary development were 3.1%, 57.9%, and 39.0% separately. The optional business was the foundation of the economy. In 2021, the essential, optional, and tertiary ventures represented 7.3%, 39.4%, and 53.3%, separately. The primary and secondary industries decreased by 5.1 and 6.2 percentage points, respectively, and the tertiary increased by 11.3 percentage units compared with 2003. The commitment paces of definite utilization consumption, gross capital arrangement, and net commodities of labor and products to monetary development were 5.3, 1.1, and 1.7 rate focuses separately, and the financial development rates were 65.4%, 13.7%, and 20.9% individually. The commitment pace of the tertiary business to financial development was 59.4%, an increment of 20.4 rate focuses north of 2003. In 2021, the financial commitment paces of definite utilization use, gross capital arrangement, and net commodities were 65.4%, 13.7%, and 20.9%, separately. The commitment pace of complete capital development was lower than that in 2003, at 52.7%. The economic impact of the current COVID-19 outbreak will be greater than the economic impact of the SARS outbreak in 2003 (see Figure5), which has a greater impact on services and consumption.Figure 5
Comparison of 2003 and 2021. (a) GDP comparison (b) Contribution rate comparison of economic growth.
(a)(b)From a national perspective, consumption has been significantly affected. In May 2003, total retail sales of consumer goods grew at an average annual rate of 4.3%, compared with 7.7% in April and 8.3% in June. Investments were not affected. In the whole year of 2003, fixed asset investment did not decline significantly, but increased significantly in April and May (see Table1). Imports and exports had little impact, which fluctuated only in April 2003. From March to May 2003, the growth rates of exports were 34.7%, 33.3%, and 37.3%, and imports were 45.1%, 34.4%, and 40.9%, respectively (see Table 2).Table 1
Total retail sales of consumer goods.
YearMonthTotal retail sales (%)200228.358.088.6118.9200328.254.7810.21110.12004210.3518.4813.91114.6Table 2
Import and export growth rate from March to May 2003.
YearMonthExport growth (%)Import growth (%)2003334.745.1433.334.4537.340.9


## 3.2. China’s GDP Growth under the Impact of the Epidemic

The SARS epidemic began in November 2002 and experienced a long incubation period. It did not spread quickly and did not peak until April and May 2003. Therefore, it had no obvious impact on economic growth in the first quarter of 2003. The annual GDP growth rate fell from 11.1% in the first quarter of 2003 to 9.1% in the second quarter, and the monthly growth rate fell from over 12% to 3.5%. The SARS epidemic was brought under control in the third quarter, and economic growth gradually recovered to 10% (Figure6). According to the experience of the SARS epidemic, it took about two months from the rapid outbreak of the epidemic to the control of the epidemic. Likewise, the timing of the economic impact of the coronavirus outbreak is mainly concentrated in the first quarter of 2021.Figure 6
The economic changes in 2003.China’s GDP in 2021 reached 114.37 trillion, a year-on-year increase of 8.1%. It was equivalent to 17.73 trillion US dollars, and the per capita GDP was 12, 551 US dollars. It was a stone’s throw away from the per capita threshold of US$12, 736 in high-income countries.In 2021, China’s GDP grew by 8.1% year-on-year, which was not only higher than the 6% growth level set by the government work report in March last year but also slightly higher than the 8% growth level predicted by international financial institutions and economic organizations.At the quarterly level, yearly development was 18.3% in the main quarter, 7.9% in the subsequent quarter, 4.9% in the third quarter from last quarter, and 4.0% in the final quarter. It was clear that in the four quarters of 2021, GDP growth would trend higher towards the end of the year. The situation appeared to be clearer while adding in the growth in the four quarters of 2020 (see Figure7).Figure 7
China’s GDP growth rate. (a) The year of 2020. (b) The year of 2020.
(a)(b)In Figure7, it very well may be seen that in the primary quarter of 2021, GDP expanded by 18.3% year-on-year, which was excessively quick. The explanation is that the effect of the scourge in 2020 was moderately low, and the quickest year-on-year development was normal. In any case, the descending tension on the large-scale economy has expanded essentially after the second from last quarter of 2021, with a year-on-year increment of 4.9% and 4.0% in the third and fourth quarters. A similar period in 2020 was contrasted, and the development rate has dialed back essentially, which was below the normal development pace of 5.1% in the two years.The year-on-year development pace of modern added esteem above assigned size and the development pace of all-out retail deals of customer products are displayed in Figure8(a), and the month-to-month development of all out import and commodity volume is displayed in Figure 8(b).Figure 8
Growth rate. (a) Year-on-year growth rate of industrial added value and growth rate of total retail sales of consumer goods. (b) Monthly growth of total imports and exports.
(a)(b)As can be seen from Figure8, the 30% year-on-year increase in total import and export volume exceeding expectations was the main contribution to GDP growth in 2021. The growth rate of investment in fixed assets accelerated, the growth rate of industrial output above the designated size accelerated, and the growth rate of total retail sales of consumer goods was slow.


## 3.3. New Crown Epidemic Brings New Opportunities

There is often a turning point behind a crisis, and the outbreak has also drawn attention to life, health, and smart cities. It will promote technological change, promote the rapid development of new water quality of economies such as e-commerce and smart healthcare, and provide a series of important new opportunities for China’s industrial restructuring (see Figure9).Figure 9
New opportunities for China’s industrial restructuring.The first is the accelerated development of the mass health industry. The outbreak of the epidemic has greatly increased people’s awareness of life and health and greatly increased the demand for medical equipment such as gas masks and related raw materials.The second is to further accelerate the construction of smart cities. The SARS epidemic in 2003 led to the rapid development of Alipay and Taobao. There is no doubt that the epidemic will change the way people go from online shopping to online shopping and will deepen the development of industries such as e-commerce, modern logistics, and the new economy.The third is the rapid rise of the “home economy”. Homestay economy is a new concept that emerged with the advent of the Internet, which mainly refers to working from home or engaging in professional work such as online entertainment, e-education, and learning, including using the Internet at home. Office and consumption patterns have changed dramatically, moving offices from one unit to another. The “home economy” was formed spontaneously in the past, but this time, due to the need to prevent and control the epidemic, some provincial and municipal governments have given support and encouragement. The rapid rise of the home economy will promote the development of the Internet celebrity economy, community economy, and platform economy.The fourth is the green buildings are gradually developing. The rapid construction and live broadcast of Huoshenshan Hospital and Leishenshan Hospital created a miracle in Wuhan, demonstrated the great potential of green buildings and intelligent buildings in Hubei Province, and showed the world the speed and strength of Chinese architecture. Therefore, prefabricated buildings, intelligent buildings, and energy-saving and environmentally friendly buildings will achieve huge growth, and Chinese construction will go overseas.The fifth is to further develop emergency industrial and military-political integration. In the fight against the epidemic, the People’s Liberation Army has provided strong emergency support in various aspects including medical treatment, transportation, and chemical prevention. The newly built Huoshenshan Hospital has been handed over to all the people’s soldiers, creating living conditions for military-civilian unity and introducing new associations for the further development of military-political integration.The new business form and the new model triggered by the epidemic will continue to promote the development of the industry in the direction of green, digital, networked, intelligent, and coordinated development, which is completely consistent with the trend of industrial transformation and upgrading and brings new opportunities to industrial development. It will help accelerate the transformation of new and old power, promote high-quality economic growth, and cultivate new economic growth points.


## 4. Conclusions

2020 is the year of building a moderately prosperous society in an all-around way and the end of the “13th Five-Year Plan”. It is crucial to do a good job in economic work. The sudden outbreak of COVID-19 has had significant economic and social impacts across the country, putting even more pressure on the province’s economic growth. However, based on experience with relevant events, this effect is short term. The economic and social development should be viewed from a comprehensive, dialectical, and long-term perspective, continuously enhance confidence, and turn pressure into motivation and crisis into opportunity. Governments at all levels must always put the safety and health of the people first and pay close attention to all kinds of prevention and control efforts. It is also necessary to further adjust macroeconomic policies, coordinate economic actions of various social development actions, and seek multiparty support. It is important to deepen military-civilian integration, promote the orderly recovery of employment and production, effectively support small and medium-sized enterprises and microenterprises, focus on the development of key industries, guide industrial transformation and upgrading, and strive to achieve the goals and tasks of the economic and social development. In the process of completing this paper, it was found that the model applied to economic forecasting based on the BP network can achieve good forecasting effect. However, the research in this paper is preliminary and the writing time is limited, and there are still some deficiencies, which are worth continuing to explore.



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*Source: 8173768-2022-08-22.xml*