Frontiers in Business, Economics and Management 
ISSN: 2766-824X | Vol. 18, No. 1, 2025 

 

124 

Risk Control in Complex Supply Chain Networks Under 
Sudden Public Emergencies 

Hao Meng * 

School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing 400065, China 

* Corresponding author: Hao Meng (Email: seiunsky2000@163.com) 

 

Abstract: After the occurrence of sudden public emergencies, risks propagate throughout the entire supply chain network, 
triggering a domino effect. Therefore, determining appropriate strategies to intervene in and control risks, thereby blocking  or 

mitigating their spread, becomes critically important. This study constructs a risk control model for complex supply chain 

networks under sudden public emergencies based on the SEIR system dynamics propagation model, incorporating risk 

immunization strategies and government participation in risk control. The proposed model adopts a target control approach to 

manage risk dissemination, and the effectiveness of this approach is verified through simulation experiments. The findings reveal 

that target control can effectively contain risk propagation, and enhancing the risk immunity of supply chain networks during 

sudden public emergencies significantly reduces the extent of risk diffusion while lowering the cost of government intervention. 

Keywords: Sudden public events, supply chain risk, system dynamics propagation model, target control. 

 

1. Introduction 

Under the context of economic globalization, supply chain 

networks have become increasingly complex. While the 

complexity of supply chain networks brings new markets and 

business opportunities to enterprises at various levels, it also 

raises concerns about supply chain vulnerability. When a risk 

event occurs at a node enterprise within the network, the risk 

spreads throughout the entire supply chain, triggering a 

domino effect that threatens the stability of the entire network. 

This issue has become particularly prominent in recent years 

due to the frequent occurrence of sudden public emergencies. 

For instance, during the COVID-19 pandemic, industries with 

long and complex supply chains, such as the automotive and 

aerospace sectors, experienced significant supply chain 

disruptions. Public reports indicate that more than 100 global 

automobile manufacturers and parts suppliers halted 

production, while Boeing, one of the world's leading 

commercial aircraft manufacturers, suspended operations at 

its plant in Washington State, USA. This, in turn, forced 

multiple Japanese suppliers of Boeing aircraft components to 

cease production as well. In response to these supply chain 

risk issues, the report of the 20th National Congress of the 

Communist Party of China emphasized the need to pursue 

high-quality development as the central theme, focusing on 

improving total factor productivity, enhancing the resilience 

and security of industrial and supply chains, and promoting 

substantive improvements in economic performance 

alongside reasonable quantitative growth. 

In existing research, numerous scholars have conducted a 

series of studies on the emergence and propagation of supply 

chain risks under sudden events. Sheffi et al. (2001) [1] 

proposed strategies for minimizing the impact of terrorism-

related events on supply chains. Chopra et al. (2004) [2] 

classified sudden risks into categories such as natural 

disasters, labor disputes, supplier bankruptcies, wars, and 

terrorism. Kleindorfer et al. (2005) [3], focusing on the 

outcomes of risks, categorized sudden event risks as those 

leading to increased supply chain costs or even collapse due 

to disruptions. Stephan et al. (2006) [4] examined the 

relationship between supply chain vulnerability and the 

occurrence of supply chain risks during sudden public events. 

Tomlin et al. (2009) [5] analyzed how to balance and select 

between various preventive and emergency measures in 

supply chain risk management during sudden events. Lu et al. 

(2021) [6] identified relevant indicators in the construction 

industry that can serve as early warnings for supply chain 

risks caused by sudden public events. Hohenstein et al. (2022) 

[7] specifically analyzed effective supply chain risk 

management measures during the COVID-19 pandemic, a 

significant public emergency. The aforementioned studies 

provide valuable insights and methodologies for 

understanding the propagation of supply chain risks and 

implementing measures to control the spread of such risks. 

Due to the similarities in the entities, mediums, and 

transmission methods between virus propagation and risk 

dissemination, many scholars argue that the patterns of risk 

propagation in supply chain networks exhibit similarities to 

the mechanisms of infectious diseases. In the early 20th 

century, Kermack and McKendrick introduced the renowned 

infectious disease model, known as the Kermack-

McKendrick model. This model is based on the number of 

susceptible (S), infected (I), and recovered (R) individuals in 

a population, and considers the transitions between these 

categories. Building on this, Zhang et al. (2003) [8] proposed 

the SEIR model, which includes exposed individuals (E) and 

demonstrated the threshold role of the basic reproduction 

number in infectious disease models. When intervening in 

infectious disease models, it is necessary to account for a 

variety of factors, making the identification of optimal control 

strategies critical. Vaccination has long been considered one 

of the most effective methods. Dietz and Schenzle et al. (1985) 

[9] noted that real-world infectious disease models must 

incorporate vaccination programs. Kribs-Zaleta and 

Martchevab (2002) [10] discussed a simple SIS vaccination 

model and proved that vaccination can successfully control 

disease spread when the basic reproduction number is reduced 

to less than 1. Meng et al. (2022) [11] demonstrated the 

control effect of vaccination on SEIR models with nonlinear 

infection rates. Furthermore, in the context of controlling and 



 

125 

assisting with infectious disease models, optimal control 

theory is often applied. Revell et al. (1967) [12] first 

introduced optimal control theory in the context of infectious 

disease control, focusing on minimizing the cost of control 

with optimal strategies. Bruno et al. (2011) [13] discussed 

how to use optimal control vaccination strategies in SEIR 

models to minimize both disease burden and intervention 

costs. Ahmad et al. (2016) [14] applied optimal control theory 

to the control of the Ebola outbreak using immunization 

strategies and developed the corresponding SEIR model. 

Olivares et al. (2021) [15] addressed the optimal control 

problem for COVID-19 based on evolving vaccination 

strategies. 

Unlike previous studies, this paper utilizes the similarities 

between supply chain network risk propagation and infectious 

disease transmission, applying the infectious disease system 

dynamics model and optimal control theory to the study of 

supply chain network risk propagation. The model is further 

improved to construct a theoretical framework for risk 

propagation and control in supply chain networks under 

sudden public events. An immunization-based risk control 

approach is proposed to reduce the propagation of risks within 

the supply chain network. Through simulation experiments, 

the paper models the risk propagation process under different 

control scenarios, recording changes in the number of various 

types of enterprises. The study adopts a combination of 

theoretical reasoning and system simulation to analyze the 

effects of different government control methods, and based on 

this analysis, suggests measures for preventing and 

controlling the propagation of risks in supply chain networks. 

This research aims to provide valuable reference for 

governmental and other functional departments. 

2. Model  

2.1. Hypothesis 

The complex supply chain network in this paper refers to a 

supply chain system composed of a large number of 

interconnected enterprises, forming a network structure. The 

complexity of this supply chain network is mainly reflected 

in two aspects: quantity and structure. Specifically, in terms 

of quantity, the network involves a vast number of enterprises, 

resulting in a large-scale network. In terms of structure, due 

to differences in enterprise size and connectivity, different 

node enterprises exhibit varying levels of resilience to risk, 

presenting heterogeneous characteristics. The parameters 

involved in the model are listed in Table 1. 

 

Table 1. Related parameters of SEIR model 

Parameter Description 

𝜇 
The entry rate of new enterprises and the exit 

rate of existing enterprises. 

𝛽1 
The contact infection rate of enterprises with 

strong risk resistance. 

𝛽2 
The contact infection rate of enterprises with 

weak risk resistance. 

𝑚 
The proportion of enterprises with strong risk 

resistance. 

𝛼 The infection transition rate. 

𝛾 The recovery transition rate. 

𝜌 The immunization control rate. 

 

Hypothesis 1. The complex supply chain network can be 

viewed as a relatively open system, where existing enterprises 

may exit, and new enterprises continually join, resulting in the 

replacement of some enterprises. It is assumed that the exit 

rate of existing enterprises is equal to the entry rate of new 

enterprises. As a result, the supply chain network exhibits a 

continuous updating characteristic. 

Hypothesis 2. The supply chain risk discussed in this paper 

is characterized by undirected propagation. When an 

enterprise in the network is affected by risk, this risk can 

simultaneously spread both upstream and downstream in the 

supply chain, infecting other enterprises connected to it. Thus, 

the risk can propagate in an undirected manner throughout the 

supply chain. 

Hypothesis 3. In order to study the risk propagation process, 

the SEIR system dynamics transmission model is employed. 

The enterprises in the supply chain network are divided into 

four categories: susceptible enterprises (S), exposed 

enterprises (E), infected enterprises (I), and immune 

enterprises (R). It is also assumed that susceptible enterprises 

do not possess risk immunity before the government 

implements an immunization strategy. The four categories of 

enterprises undergo state transitions according to certain 

probabilities. 

Hypothesis 4. Due to differences in the size and 

management capabilities of node enterprises, different 

enterprises in the supply chain network have varying abilities 

to withstand risks. Larger enterprises generally possess more 

resources and capabilities, enabling them to better resist risk 

shocks. In contrast, smaller enterprises are relatively more 

vulnerable and are more easily affected by the spread of risks. 

Therefore, susceptible enterprises (S) are further divided into 

two categories: those with strong risk resistance and those 

with weak risk resistance. The probability of infection differs 

when different risk-sensitive groups come into contact with 

infected enterprises. Thus, different infection rates are 

adopted to represent the infection levels of these two groups. 

Hypothesis 5. Considering the characteristics of sudden 

public events, which typically have a significant impact and 

widespread effects, it is assumed that after the occurrence of 

such an event, multiple infected enterprises coexist 

simultaneously within the supply chain network, leading to a 

scenario where risks propagate from multiple sources at the 

same time. This assumption more accurately reflects the 

impact of sudden public events on the supply chain network 

and provides a reasonable foundation for subsequent research 

and analysis. 

Hypothesis 6. To control the propagation of risks, the 

government implements immunization measures for certain 

risk-sensitive enterprises, providing financial and policy 

support to enhance the immunity of these enterprises against 

risks. The government incurs control costs, which, similar to 

previous studies, are positively correlated with the square of 

the immunization rate. This implies that as the immunization 

rate increases, the control costs rise disproportionately. 

Under the immunization strategy, the conceptual model of 

risk propagation in the complex supply chain network is 

shown in Figure 1. 

 
Figure 1. Risk propagation model of supply chain network 



 

126 

2.2. Model Construction 

Based on the above assumptions, the SEIR system 

dynamics risk propagation model for complex supply chain 

networks under sudden public events is constructed, as shown 

in Equation (1). Equation (1) represents the relationship 

between the number of enterprises in each risk state and the 

time t. 

 

 

1 2

1 2

(1 ) ( ) ( )S(t)
( ) ( ) ( )

( )

(1 ) ( ) ( )( )
( ) ( )

( )

( )
( ) ( ) ( )

( )
( ) ( ) ( )

m m S t I td
N t S t S t

dt N t

m m S t I tdE t
t E t

dt N t

dI t
E t I t t

dt

dR t
I t S t R t

dt

 
  

 
 

  

  

 + −
= − − −


 + −
 = − −




= − −



= + −


 (1) 

After the occurrence of a sudden public event, some node 

enterprises are affected and become risk-infected enterprises, 

i.e., the sources of risk infection. These enterprises then 

propagate the risk throughout the supply chain network, 

leading to its spread. The number of various types of 

enterprises evolves over time according to a system of 

nonlinear differential equations (referencing Equation (1)). 

Since solving nonlinear differential equations analytically is 

generally quite difficult and obtaining precise analytical 

expressions is challenging, the focus is placed on the steady-

state characteristics of the system over long time scales, rather 

than on precisely capturing each instantaneous state. In 

system dynamics propagation theory, the concepts of 

equilibrium points and their stability are commonly applied 

to study the trends and characteristics of the differential 

equation system. 

The following Equation (2) implies that 𝑑𝑆 𝑑𝑡⁄ =
0, 𝑑𝐸 𝑑𝑡⁄ = 0. 𝑑𝐼 𝑑𝑡⁄ = 0, 𝑑𝑅 𝑑𝑡⁄ = 0 , at which point the 
system is in a steady state. 

 

 

1 2

1 2

(1 ) ( ) ( )S(t)
( ) ( ) ( ) 0

( )

(1 ) ( ) ( )( )
( ) ( ) 0

( )

( )
( ) ( ) ( ) 0

( )
( ) ( ) ( ) 0

m m S t I td
N t S t S t

dt N t

m m S t I tdE t
t E t

dt N t

dI t
E t I t t

dt

dR t
I t S t R t

dt

 
  

 
 

  

  

 + −
= − − − =


 + −
 = − − =




= − − =



= + − =


   (2) 

If 𝐸 = 𝐼 = 0  at this point, it means that the risk in the 
system will have disappeared, and this equilibrium point is 

referred to as the zero equilibrium point. It can be easily 

derived that: 

0 (0) (0) (0( ) )
( , , , ( , 0, ,) 0 )X S E I R

N 

 +   
==

+

（0)
      (3) 

Conversely, if 𝐸 ≠ 0, 𝐼 ≠ 0 at this point, it indicates that 
the number of risk-infected enterprises in the system remains 

constant, and the risk does not disappear. In this case, a non-

zero equilibrium point 𝑋∗ = (𝑆∗, 𝐸∗, 𝐼∗, 𝑅∗) is obtained. 

*

1 2

N( + )( + + )

[m +(1-m) ]
S

    

  
=               (4) 

( )( )
 

* *

1 2

*
(1 )

S S
E

N

m m S

    

  

+ − −

+ −
=

*
          (5) 

 

* *
*

*

1 2

( )

(1 )

N N S S
I

Sm m

  

 

− −

+ −
=             (6) 

( )
 1

* * *

*

2

*

(1 )

S S S
R

N

m S

N

m

    

  

−
+

+ −
=

−
        (7) 

At this point, it indicates that once the system reaches the 

non-zero equilibrium point, the system is at the critical point 

of risk propagation. The number of infected enterprises 

remains stable, and the risk will neither spread further nor 

fade away. 

2.3. Target Control 

In target control, transforming susceptible enterprises into 

immune enterprises requires a cost, which is positively 

correlated with the square of the immunization control rate 

and is given by 𝐴𝜌2/2, where 𝐴 is the cost coefficient. On 
one hand, increasing the immunization control rate reduces 

the basic reproduction number, thereby reducing risk 

propagation; on the other hand, increasing the immunization 

control rate leads to a sharp rise in control costs. The optimal 

target control decision is to find the immunization control rate 

that minimizes the sum of control costs and the basic 

reproduction number. The objective function expression is as 

follows: 

2

0
2

A
J R= +                 (8) 

The latter part of the objective function represents the basic 

reproduction number 𝑅0 , and the magnitude of the cost 
coefficient 𝐴 reflects both the size of the control costs and 
the relative importance of control costs compared to the basic 

reproduction number. The larger the value of 𝐴, the higher 
the control costs, and at the same time, the more significant 

the control costs become relative to the basic reproduction 

number. This paper uses the next-generation matrix method 

to calculate the basic reproduction number, and the result for 

the basic reproduction number of the model is: 

 1 2
0

m (1 )

(

m
R

  

     

+ −
=

+ )( + )( + )
           (9) 

Equation (9) reflects the average number of enterprises that 

one infected enterprise in the system dynamics propagation 

model will infect. It is crucial in the risk propagation process, 

as whether the basic reproduction number is greater than 1 

determines whether the risk will continue to spread within the 

supply chain network or gradually dissipate. 

Property 1 In target control, the optimal immunization 

control rate 𝜌∗  satisfies 𝐴𝜌∗(𝜇 + 𝜌∗)2 = [𝑚𝛽1 + (1 −
𝑚)𝛽2]𝜇𝛼/[(𝛼 + 𝜇)(𝛾 + 𝜇)]. 

Proof: Taking the first derivative of Equation (8) with 

respect to 𝜌 gives: 

 1 2
2

m (1 )

(

mdJ
A

  


      

+ −
= −

 + ) ( + )( + )
       (10) 

Taking the second derivative with respect to 𝜌 gives: 

2

1 2

2 3

2( (1 ) )

( )

m md J
A

d

  

      

+ −
= +

+ ) ( + )( +
       (11) 

According to Equation (11), it can be concluded that the 



 

127 

second derivative is always greater than 0. Since 𝜌∗  is the 
point that satisfies 𝐽′(𝜌) = 0 , 𝜌∗  is a minimum point, and 
𝐽′(𝜌∗) = 0 represents the minimum value. 

This property indicates that under the target control strategy, 

as long as the government's assistance cost is within the 

critical value range, there will inevitably be an optimal control 

rate that minimizes the sum of the assistance cost and the risk 

diffusion degree. This provides a basis for the government to 

formulate the optimal target control assistance strategy. 

3. Simulation Experiment 

In order to thoroughly investigate the effectiveness of 

different control methods on supply chain risk propagation, 

simulation experiments were conducted using MATLAB 

software based on relevant data. The trends of enterprise 

states over time under three scenarios—no intervention, target 

control, and process control—were analyzed to assess the 

degree of risk development and to examine the impact of 

government intervention measures on risk propagation within 

the supply chain network. The focus of the analysis was on 

the relative values of parameters and results, rather than their 

absolute values, to highlight the practical significance of the 

simulation outcomes. In the simulation experiment, the key 

parameters for the supply chain network were set as follows: 

the entry rate of new enterprises and the exit rate of existing 

enterprises μ=0.02, the latent conversion rate for enterprises 

with high risk resistance β1=0.3, the latent conversion rate for 

enterprises with low risk resistance β2=0.4, the proportion of 

enterprises with high risk resistance m=0.5, the infection 

transition rate α=0.45, and the recovery rate γ=0.15. The cost 

coefficient for target control was set to A=20. The number of 

enterprises in the supply chain network was set to N=1200. In 

the initial stage after the occurrence of the sudden public 

event, 150 enterprises were infected by risk and became risk-

infected enterprises, and the risk then began to spread from 

these enterprises to other associated enterprises. 

In the simulation experiment, the supply chain network was 

simulated under three different control methods: no control, 

target control, and process control. The control rates and basic 

reproduction numbers corresponding to each control method 

are shown in Table 2.  

In the case of no control, the basic reproduction number of 

the supply chain network is greater than 1, indicating that in 

the absence of government intervention, the risk within the 

supply chain network will spread and eventually stabilize at a 

certain level. However, if the target control method is applied 

to intervene in the supply chain network, the optimal control 

rate can be calculated, which shows that the government 

should continuously provide assistance to approximately 11% 

of the risk-sensitive enterprises, transforming them directly 

into risk-immune enterprises. Under this scenario, the basic 

reproduction number of the supply chain network with target 

control is less than 1, and the risk within the network will 

gradually dissipate due to the intervention. 

 

Table 2. Optimal control rate and basic reproduction number under 
different control strategies 

Risk Control Methods 𝜌 𝑅0 

No Control 0 1.971 

Target Control 0.11 0.303 

3.1. Risk Enterprises under Different Control 
Schemes 

Risk-sensitive enterprises are the main target of 

government assistance. The government adopts a series of 

measures to directly convert a portion of the risk-sensitive 

enterprises into risk-immune enterprises. By reducing the 

number of risk-sensitive enterprises, the number of risk-

exposed enterprises generated from contact with risk-infected 

enterprises is indirectly reduced, which in turn lowers the 

number of risk-infected enterprises and ultimately eliminates 

the risk. Therefore, the changes in the number of risk-

sensitive enterprises reflect the effectiveness of different 

control strategies. The specific situation is shown in Figure 2. 

Under the no-control scenario, the number of risk-sensitive 

enterprises rapidly decreases due to the continuous spread and 

expansion of risk in the network, reaching a minimum of 420 

enterprises by period 34. Afterward, the number fluctuates 

slowly and eventually stabilizes at around 608 enterprises by 

period 300. In the case of target control, a fixed proportion of 

risk-sensitive enterprises are directly converted into risk-

immune enterprises. As a result, the decrease in the number 

of risk-sensitive enterprises under target control occurs more 

rapidly compared to the no-control scenario. By period 60, the 

number stabilizes at around 180 enterprises, with a shorter 

time required to reach the stable state. 

 
Figure 2. Risk-Sensitive Enterprises under Different Control 

Scenarios 

The generation of risk-exposed enterprises occurs because 

of the interactions between risk-sensitive enterprises and risk-

infected enterprises, where the risk spreads to a portion of the 

risk-sensitive enterprises, converting them into risk-exposed 

enterprises. At this point, although these enterprises have 

been infected and will eventually turn into risk-infected 

enterprises, risk-exposed enterprises do not infect risk-

sensitive enterprises. The number of risk-exposed enterprises 

determines the number of risk-infected enterprises in 

subsequent periods and, to some extent, represents the level 

of risk in the network. The changes in the number of risk-

exposed enterprises under different control scenarios are 

shown in Figure 3. In the case of no control, with the initial 

spread of risk, the number of risk-exposed enterprises reaches 

its peak in period 11, at 75 enterprises. Following this, as the 

number of risk-sensitive enterprises decreases, the number of 

risk-exposed enterprises begins to rapidly decline, dropping 

to 16 enterprises by period 76. As risk continues to spread 

through the supply chain network, the number fluctuates, 

ultimately reaching a stable state in period 300, with around 

24 risk-exposed enterprises remaining. When target control is 



 

128 

applied, the number of risk-exposed enterprises reaches its 

peak in period 4, at 50 enterprises. After reaching this peak, 

the number of risk-exposed enterprises starts to decrease, 

eventually reaching zero by period 60. 

 
Figure 3. Risk- Exposed Enterprises under Different Control 

Scenarios 

The number of risk-infected enterprises is the most 

intuitive reflection of the risk propagation situation within the 

supply chain network. After the occurrence of a sudden public 

event, risk-infected enterprises begin to exist, initially 

numbering 150. These risk-infected enterprises will infect 

risk-sensitive enterprises, and a portion of the transformed 

risk-sensitive enterprises will turn into risk-infected 

enterprises. The number of risk-infected enterprises 

represents the extent of risk diffusion. The changes in the 

number of risk-infected enterprises under different control 

scenarios are shown in Figure 4. In the case of no control, the 

number of risk-infected enterprises in the supply chain 

network will briefly decrease, then rapidly rise to the highest 

point. When risk propagation starts, the number of newly 

infected risk-sensitive enterprises is initially low, resulting in 

fewer risk-sensitive enterprises being converted into risk-

infected enterprises. As the number of risk-sensitive 

enterprises increases, the number of risk-infected enterprises 

increases as well, reaching a peak of 201 enterprises in period 

16. After this point, the number of risk-infected enterprises 

follows a pattern similar to that of risk-sensitive enterprises, 

first decreasing, then fluctuating, and finally stabilizing at a 

non-zero equilibrium point around period 300, with 67 risk-

infected enterprises remaining. Under target control, the 

number of risk-infected enterprises shows a brief, rapid 

decline, followed by a slowdown in the decline rate and then 

another rapid decrease. Ultimately, under target control, the 

risk dissipates, and the number of risk-infected enterprises 

reaches zero in period 70, remaining stable thereafter with no 

further fluctuations. 

 
Figure 4. Risk- Infected Enterprises under Different Control 

Scenarios 

Risk-recovered enterprises consist of two parts: one part 

recovers by itself after being infected, while the other part is 

directly converted into risk-immune enterprises through 

government assistance from risk-sensitive enterprises. The 

changes in the number of risk-recovered enterprises reflect 

the recovery of risk-infected enterprises and the extent of 

government aid. The variations in the number of risk-immune 

enterprises under different control scenarios are shown in 

Figure 5. Under the no-control scenario, risk-recovered 

enterprises rely entirely on self-recovery, and their number 

changes are directly related to the number of risk-infected 

enterprises. Both exhibit a pattern of initially rising to a peak, 

followed by a decline and fluctuation, eventually stabilizing 

near a non-zero equilibrium point. When the system reaches 

the non-zero equilibrium point, the number of risk-immune 

enterprises stabilizes at around 500, with a time span of 300 

periods. Under the target control scenario, risk-immune 

enterprises consist of both self-recovered and government-

aided types. In the early stages, since both risk-infected 

enterprises recover and government assistance is provided, 

the number of risk-immune enterprises rises rapidly. It peaks 

at 1,018 enterprises in period 60 and then stabilizes. 

 
Figure 5. Risk- Recovered Enterprises under Different Control 

Scenarios 

Considering the four types of risk enterprises mentioned 

above, under the no-control scenario, the supply chain 

network reaches a non-zero equilibrium point around period 

300. In this state, the risk within the supply chain network still 

exists and remains stable. However, under the target control 

scenario, the supply chain network reaches a disease-free 

equilibrium point around period 60, and the risk within the 

supply chain network no longer exists. 



 

129 

4. Conclusion  

Through the simulation experiments and sensitivity 

analysis of the supply chain network models under different 

aid strategies in response to sudden public events, it is clear 

that when the government provides assistance to risk-

sensitive enterprises, target control proves to be effective in 

suppressing risk propagation. Therefore, enhancing the 

overall risk resilience of the supply chain network before the 

occurrence of a sudden public event can effectively reduce 

both the spread of risk and the level of government 

intervention required, thus lowering costs. This approach not 

only strengthens the recovery capacity of the supply chain 

network but also reduces the cost of intervention after the 

event occurs, allowing for greater government intervention. 

Consequently, establishing an aid mechanism with low costs 

and high benefits is essential to minimize control costs and 

more effectively manage the spread of risks within the supply 

chain network. 

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