1. Introduction
The novel coronavirus (COVID-19), which broke out at the end of 2019, spread rapidly around the world in a remarkable form of human-to-human transmission, posing a serious threat to global public health. Under the leadership of the government, the prevention and control of the COVID-19 epidemic has achieved outstanding results. With the weekly deaths from COVID-19 down to the lowest since March 2020, the head of WHO said on 14 September 2022, that the end of the pandemic is now in sight. However, during the pandemic, as a result of the continuous mutation of the virus, its transmission speed and infectiveness are rapidly increasing. Currently, it is found that a single person infected with the Omicron virus may infect an average of 9.5 people without any protection, and the virus cannot be easily detected [
1]. There is no doubt that epidemic prevention and control around the world are facing greater challenges, and, therefore, related strategies require further attention and investigation.
In COVID-19 prevention and control, government guidance and supervision play a critical role, but the actual effectiveness of the governance also depends on the implementation by the public. During the pandemic, it can be found that there are still some people who do not follow the epidemic-related regulations, such as wearing a mask when in public places, which to some extent adds difficulties to the epidemic governance. Furthermore, between the government and the public, Internet media also plays an important role in the transmission of the epidemic-related information and regulations. Internet media is a double-edged sword, which means that positive online comments can help enhance the effectiveness of prevention and control policies, whereas negative online comments may reduce it. In this context, the paper aims to study the collaborative prevention and control of public health emergencies among the government, Internet media, and the public.
In the existing literature regarding public health emergencies, the epidemic transmission, and epidemic prevention and control were widely studied. Several simulation models were introduced to study the problems. Taking H1N1 as an example, evolutionary game theory was used to build the epidemic situation diffusion function, in order to forecast the peak value under different evolutionary scenarios [
2]. Moreover, in the literature, epidemic logistics models [
3] and SEIR models [
4,
5,
6] were applied to study the transmission and diffusion mechanisms of public health emergencies. Focusing on epidemic prevention and control, the existing literature mainly studied the effects of government intervention mechanisms, media perceptions of role, and public behavioral responses. Firstly, from the perspective of government intervention, Chinazzi et al. [
7] indicated that travel restrictions on residents could contain the outbreak. The importance of government interventions was demonstrated by Wessel [
8] and Mandal et al. [
9]. Then, Jiao et al. [
10] and Hallewell et al. [
11] pointed out that strict isolation policies by the government can effectively contain the spread of infectious diseases. Huang et al. [
12] believed that strengthening the emergency management performance assessment of relevant government departments is conducive to improving the emergency management efficiency of the public health emergency management system. Secondly, from the perspective of media perception of the role, during the emergency response and recovery stages of public health emergencies, social media could not only quickly convey official policies and event progress, but also served as a reference for leadership decision-making [
13]. Hagen et al. [
14] studied how social media can be used in crisis communication during public health emergencies. Thirdly, from the perspective of public behavioral responses, Sega et al. [
15] argued that the public levels of risk perception had an important effect on the evolution of the epidemic. Klaczkowski et al. [
16] investigated the effect of social distancing driven by individual’s risk attitudes on epidemics, and found that voluntarily enforcing social distancing can effectively contain the outbreak. Some scholars also believed that maintaining social distancing [
17,
18] and consciously wearing masks [
19,
20] can effectively reduce the spread of the epidemic. Overall, aiming at the prevention and control of public health emergencies, the above studies mainly focused on strategies from the perspective of only one single subject, such as the government, the media, or the public, but pays less attention to collaborative prevention and control of public health emergencies among them.
Based on utility maximization theory, the evolutionary game model is introduced in this paper, in order to study the collaborative prevention and control problem and to obtain the optimal strategies for the subjects. In fact, under the assumption of boundedly rational, the behavioral evolutionary process of participants has been widely applied in the fields of computer science [
21], electronic equipment recycling management [
22], engineering safety [
23] and supply demand analysis [
24]. Moreover, in a series of studies on public health emergencies, evolutionary game theory was also used to study the decision-making process of participants. Fan et al. [
25] combined evolutionary game theory with system dynamics to analyze the interactions of behavioral strategies of the government, communities, and residents, and introduced a dynamic reward–penalty mechanism, which proved to be effective in suppressing the fluctuations in the evolutionary game process. Xiao et al. [
26] applied a regional evolutionary game model to study the collaborative governance of multiple regions in public health emergencies. Xu et al. [
27] analyzed the effects of the different factors on the decision-making of participants for public health emergencies by using a tripartite evolutionary game model, involving the local government, enterprises, and the public. Jia et al. [
28] used the stochastic evolutionary game model to explore the public’s epidemic prevention and control strategies in an uncertain environment. Yuan et al. [
29] studied the influencing factors of medical supply distribution in public health emergencies by constructing a two-sided game model between government-owned nonprofit organizations and hospitals. Liu et al. [
30] explored the interaction and influence mechanisms among government rescue teams, social emergency organizations, and government support institutions based on a tripartite evolutionary game model. Therefore, according to the existing literature, there were few systematic analyses of response strategies among various subjects, and most of the research explored the effects of behavioral evolution strategies among the central government, local governments, enterprises, and the public. However, there is no quantitative analysis regarding the interactions between government agencies, the Internet media, and the general public.
Different from the above-mentioned studies, this paper aims to examine the collaborative prevention and control of public health emergencies among the government, Internet media, and the public, and to explore the evolution process of participants’ behavioral strategies. Taking COVID-19 as an example, the research shows that there are significant differences in the tripartite equilibrium strategies during the early stage, outbreak stage and resumption stage of epidemic. Based on the evolutionary game theory, a trilateral evolutionary game model for collaborative prevention and control is constructed, and the evolutionary paths of three stages of the epidemic are simulated. In our context, the government agencies are mainly responsible for making policies and supervising the behaviors of Internet media and the public through a reward–penalty mechanism, then the government’s optional strategies are set to be “strong supervision” and “weak supervision”. Additionally, the Internet media, as the main promoters and information guiders of online public opinion, are supposed to convey the government’s policies and suppress negative public sentiment. However, the Internet media can also choose to pursue maximum utility by promoting public opinion. Then, the optional strategies for Internet media are “promoting” and “no promoting”. As for the public, they need to strictly follow the prevention and control policies and voluntarily self-isolate if needed. Therefore, the public’s optional strategies are set to be “voluntary isolation” and “free flowing”. Therefore, government agencies, Internet media, and the general public, as important subjects, play crucial roles in the collaborative prevention and control of the epidemic. How the strategies and mechanisms for epidemic prevention and control evolve is a topic worthy of in-depth study.
Moreover, a system dynamics approach is further adopted to simulate the heterogeneous effects of stochastic factors on the evolutionary of strategies. Changes in initial strategy probabilities of the game subjects are found not to affect the equilibrium results of the system, but to affect the convergence speed. Additionally, the higher the initial willingness of government agencies to supervise, the shorter the time for the Internet media and the general public to reach a steady state. However, when the initial willingness of the Internet media to promote the online public opinion is higher, the faster the public will converge to a stable state, and the slower government agencies will converge to a stable state. Furthermore, for these three game subjects, changes in the probabilities of epidemic spread are found to have the greatest impact on the evolution of government agency strategies.
Our analysis contributes to the literature on epidemic prevention and control. Different from the existing literature, the study considers the collaborative prevention and control among government agencies, the Internet media, and the general public, and emphasizes the important role played by the Internet media. Our second contribution is to introduce the evolutionary game theory to obtain the tripartite equilibrium strategies of the subjects. Moreover, the heterogeneous effects of stochastic factors on the evolutionary results are further studied, which sed light on the underlying evolutionary mechanisms. Our results provide decision-making support and policy recommendations for scientific and quantitative prevention and control of epidemics.
This study is arranged as follows. In
Section 2, a trilateral evolutionary game model is constructed, and the evolutionary paths of three stages of the epidemic development are simulated. In
Section 3, a system dynamics approach is further adopted to simulate the heterogeneous effect of stochastic factors on the evolutionary of strategies. In
Section 4, conclusions and recommendations are summarized.
2. Model Construction
In the process of collaborative prevention and control for public health emergencies, there is a complex game relationship among government agencies, the Internet media and the general public. Therefore, in this section, a trilateral evolutionary game model is constructed with government agencies, the Internet media and the general public as the game subjects. Additionally, the replicator dynamics equation is applied to obtain the stable equilibrium point of the game subjects. Finally, the evolutionary paths of the early stage, the outbreak stage, and the resumption stage of the epidemic’s development are simulated by MATLAB.
As a convenience, it is assumed that government agencies, the Internet media, and the general public will all be involved in the process of COVID-19 epidemic prevention and control.
(1) Government agencies. Government agencies include both the central government and local governments, which are the primary leaders in the process of epidemic prevention and control. Several prevention and control policies have been issued by the government in order to effectively contain the outbreak, and reward–penalty mechanisms have also been developed to restrain the decision-making behavior of the general public and the Internet media.
(2) The Internet media. The Internet media refer to the emerging online media and social network platforms that disseminate various types of information through the Internet. The Internet media are the main promoters and information guiders of online public opinion, and their decisions are aimed at maximizing their own profits.
(3) The general public. People who live in an affected area are defined as the general public. On the basis of ensuring their own lives and health, the general public make decisions with the goal of minimizing their own losses.
2.1. Model Assumption
To objectively analyze the decision-making behavior and interactions among government agencies, the Internet media, and the general public in the process of epidemic prevention and control, the following assumptions are introduced:
Hypothesis 1 (H1). The game subjects, including government agencies, the Internet media, and the general public, are all bounded rationality.
Hypothesis 2 (H2). There are two main strategies for government agencies to adopt in order to contain the outbreak. One is to take the most comprehensive, rigorous and thorough measures (referred to as “strong supervision”) for enterprises, the media, and the public, and so on. It means that, in medium-risk or high-risk regions of the epidemic, government agencies will make every possible effort to curb the spread of the disease by implementing closed-loop management, shutting down production, a nationwide nucleic acid test, reward–penalty mechanisms, and other mandatory measures. The probability of government agencies applying strong supervision is denoted by(). The other is to relax regulatory policies for enterprises, the media, and the public (called “weak supervision”), which means that in low-risk regions, government agencies relax regulatory policies to minimize the impact of epidemic prevention and control measures on production and normal life and to maximize overall coordination epidemic prevention and control with economic development. The probability of weak supervision is set to be.
Hypothesis 3 (H3). For the Internet media, there are two strategies for preventing and controlling epidemic. Firstly, online public opinion regarding the epidemic is not promoted by the Internet media (called “no promoting”), which implies that the Internet media have given full play to the role of information dissemination, policy publicity, and public opinion supervision, as well as appeasing and channeling public sentiment by timely reporting the COVID-19 situation, conveying relevant government policies, etc. The probability is denoted by(). Secondly, online public opinion on major epidemics is promoted by the Internet media (called “promoting”), which refers to the fact that government policies and related information about COVID-19 are not reported in a timely manner. Furthermore, the epidemic is also used to generate revenue from topics of online rumors. The probability of this case is set to be.
Hypothesis 4 (H4). There are two strategies that can be chosen by the general public in the process of epidemic prevention and control. One is to voluntarily enforce social distancing with others (called “voluntary isolation”). It refers to how the public actively collaborate with government agencies by initiatively reporting health conditions, and voluntarily self-isolating. The probability of this strategy is set to be(). The other is to go outside as one pleases by public transport (called “free flowing”). In other words, confirmed cases, asymptomatic patients, suspected cases, close contacts and the public from medium-risk or high-risk regions ignore laws and regulations for epidemic prevention and control, and take public transport such as buses and subways to move freely. This results in the spread of outbreak. The probability of this strategy is denoted by .
Hypothesis 5 (H5). During the process of epidemic prevention and control, the costs of government agencies adopting the “strong supervision” strategy are denoted as. Under this strategy, reward measures ofare given to the Internet media who choose the “no promoting” strategy, and punishment measures ofare given to the Internet media who choose the “promoting” strategy and cause social panic. In addition, reward measures ofare given to the public who choose the “voluntary isolation” strategy, and administrative punishment measures ofare imposed on the public who choose the “free flowing” strategy and cause the spread of the epidemic. The spread probability is denoted as, and the corresponding socio-economic losses are denoted as, then the expected socio-economic losses is. Moreover, if government agencies adopt the “weak supervision” strategy and the public prefer the “free flowing” strategy, the large-scale spread probability is set to be(), and the expected socio-economic losses is. At the same time, the government must also bear trust losses of the public, which are.
Hypothesis 6 (H6). When the “no promoting” strategy is chosen by the Internet media, the costs of manpower and material resources are; at the same time, it confers additional public trust profits to the government, which are set to be. When the “promoting” strategy is chosen, revenues from the topic of online rumors are denoted as. However, the Internet media must bear trust losses of the public, which are.
Hypothesis 7 (H7). When the general public choose the “voluntary isolation” strategy, if the Internet media fail to push online public opinion regarding the epidemic, then a personal loss caused by social isolation is denoted as; if the Internet media prefer to push public opinion about the epidemic, the public will panic and the demand for supplies will increase, resulting in a surge in price. At this time, the personal loss will increase to(). When the general public choose the “free flowing” strategy, because of the negative externality of the large-scale spread of the epidemic, the public have to bear their share of the socio-economic losses, that is,or. The population of the affected area is denoted by.
To simplify the description of the methodology approach, a flowchart is shown in
Figure 1. The symbols of the relevant parameters are shown in
Table 1.
2.2. Construction of a Tripartite Game Model
Based on the above assumptions, the strategy combination and payoff matrix of the tripartite evolutionary game among government agencies, the Internet media, and the general public are obtained, as shown in
Table 2.
2.2.1. Evolutionary Path Analysis of Government Agencies
According to the game payoff matrix in
Table 2, the expected payoff of government agencies under different strategies is calculated, and then the replicator dynamics equation of the evolutionary game can be obtained. Let
be the expected payoff when government agencies adopt the “strong supervision” strategy, and the formula is as follows:
The expected payoff of “weak supervision” strategy for government agencies is
:
The average expected payoff of government decision-making behavior is
:
Therefore, substituting into Equations (1)–(3), the replicator dynamics equation for government agencies to choose the “strong supervision” strategy can be obtained as follows:
From stability theorems of the replicator dynamics equation, we know that:
(1) When , we obtain . Therefore, the game is in a stable state regardless of the value of ; that is, the strategy choice of government agencies does not change over time.
(2) When
, let
, we obtain
or
, and the game is in a stable state. Firstly, we calculate the derivative of
:
Then, based on Equation (5), the following two cases are discussed:
(i) When , , , at this time, is the stable equilibrium point in the evolution of government behavior; that is, it is stable for government agencies to choose “weak supervision” strategy in the process of epidemic prevention and control.
(ii) When , , , at this time, is the stable equilibrium point in the evolution of government behavior; that is, it is stable for government agencies to choose the “strong supervision” strategy in the process of epidemic prevention and control.
According to the above analysis, the above conclusions are expressed in the three-dimensional coordinate system, and the dynamic evolution trend of the government behavior is obtained, as shown in
Figure 2.
2.2.2. Evolutionary Path Analysis of the Internet Media
According to the game payoff matrix in
Table 2, the expected payoff of the Internet media under different strategies is calculated, and then the replicator dynamics equation of the evolutionary game can be obtained. Let
be the expected payoff when the Internet media adopt the “no promoting” strategy, and the formula is as follows:
The expected payoff of the “promoting” strategy is
:
The average expected payoff is
:
Therefore, substituting into Equations (6)–(8), the replicator dynamics equation for the Internet media to choose the “no promoting” strategy can be obtained as follows:
According to Equation (9), we have:
(1) When , we obtain . Therefore, the game is in a stable state regardless of the value of , that is, the strategy choice of the Internet media does not change over time.
(2) When
, let
, we obtain
or
, and the game is in a stable state. We obtain the derivative of
:
Based on Equation (10), two cases are discussed as follows:
(i) When , , , at this time, is the stable equilibrium point in the evolution of the Internet media behavior, that is, it is stable for the Internet media to adopt “promoting” strategy in the process of epidemic prevention and control.
(ii) When , , , at this time, is the stable equilibrium point in the evolution, that is, it is stable for the Internet media to choose a “no promoting” strategy in the process of epidemic prevention and control.
According to the above analysis, the above conclusions are expressed in the three-dimensional coordinate system, and the dynamic evolution trend of the Internet media behavior is obtained, as shown in
Figure 3.
2.2.3. Evolutionary Path Analysis of the General Public
Similarly, let
be the expected payoff when the general public adopt “voluntary isolation” strategy, let
be the expected payoff when the general public choose the “free flowing” strategy, and
denotes the average expected payoff. The formulas are as follows:
Therefore, substituting into Equations (11)–(13), the replicator dynamics equation for the general public can be obtained as follows:
According to Equation (14), we have:
(1) When , . Therefore, the game is in a stable state regardless of the value of , that is, the strategy choice of the general public does not change over time.
(2) When
, let
, we obtain
or
, and the game is in a stable state. We obtain the derivative of
:
Then, based on Equation (15), two following cases are discussed:
(i) When , , , at this time, is the stable equilibrium point in the evolution of the general public behavior, that is, it is stable for the general public to adopt “free flowing” strategy in the process of epidemic prevention and control.
(ii) When , , , at this time, is the stable equilibrium point in the evolution, that is, it is stable for the general public to choose “voluntary isolation” strategy in the process of epidemic prevention and control.
According to the above analysis, the above conclusions are expressed in the three-dimensional coordinate system, and the dynamic evolution trend of the general public’s behavior is obtained, as shown in
Figure 4.
2.3. Stability Analysis of Model
Based on the above analysis, the three-dimensional dynamic system of the evolutionary game is obtained as follows:
Let
; there are eight local equilibrium points of this evolutionary system: (0,0,0), (0,0,1), (0,1,0), (1,0,0), (1,1,0), (1,0,1), (0,1,1), (1,1,1). According to the idea proposed by Friedman [
31], the stability of each equilibrium point can be analyzed by using eigenvalues of the Jacobian matrix. If the eigenvalues of an equilibrium point are all negative numbers, the equilibrium point is the evolutionary stable strategy (ESS). The Jacobian matrix of this system is as follows:
which is
,
.
From Equation (17), the eigenvalues are obtained corresponding to each equilibrium point in the evolutionary system, and then the stability is analyzed, as shown in
Table 3 and
Table 4.
From the stability conditions in
Table 4, it is evident that the difference between profits and costs determines the choice of three subjects. Considering the uncertainty of COVID-19, game subjects have different decision-making behaviors at different stages of the epidemic’s development. Therefore, we divide the development process of COVID-19 into three stages [
32]: the early stage of the epidemic, the outbreak stage, and the resumption stage, and analyze the stability of equilibrium points at each stage.
(1) The early stage of the epidemic. At this time, COVID-19 is a new unknown virus, and there are few confirmed cases and deaths. Considering the needs of economic development, government agencies tend to choose the “weak supervision” strategy. To maximize their own interests, the Internet media prefer the “promoting” strategy to obtain revenues from hot topics regarding the epidemic. The “free flowing” strategy is adopted since the general public have limited knowledge about the epidemic and less awareness of prevention. Therefore, this stage corresponds to the equilibrium point (0,0,0). From
Table 4, to achieve the equilibrium point (0,0,0) as a stable point, two conditions are supposed to be satisfied: (i)
, that is, if the costs of strong supervision by the government are greater than their profits, then the “weak supervision” strategy is preferred by government agencies; (ii)
, that is, if personal loss from the “voluntary isolation” strategy exceeds the socio-economic losses of the “free flowing” strategy, then the “free flowing” strategy is chosen by the general public. The system evolution path is shown in
Figure 5.
(2) The outbreak stage of the epidemic. At this time, the epidemic is widespread. Due to the high infection rate and widespread impact of COVID-19, the number of confirmed cases and deaths increases rapidly at this time. In order to effectively contain the outbreak, government agencies tend to switch from a “weak supervision” strategy to a “strong supervision” strategy. Under the influence of gradually strengthening supervision by government agencies, the Internet media prefer the “no promoting” strategy for their own profits. To protect themselves and avoid the punishment from government agencies, the public will choose the “voluntary isolation” strategy. Therefore, this stage corresponds to the equilibrium point (1,1,1).
Table 4 shows that to achieve the equilibrium point (1,1,1) as a stable point, three conditions are supposed to be satisfied: (i)
, that is, if trust losses from the “weak supervision” strategy by government agencies are greater than costs of the “strong supervision” strategy, then the “strong supervision” strategy is adopted by government agencies; (ii)
, that is, if the payoff of the “no promoting” strategy by the Internet media exceeds the payoff of the “promoting” strategy, then the “no promoting” strategy is preferred by the Internet media; (iii)
, that is, if the payoff of the “voluntary isolation” strategy are greater than the payoff of the “free flowing” strategy, the “voluntary isolation” strategy is chosen by the general public. The system evolution path is shown in
Figure 6.
(3) The resumption stage of the epidemic. Under the leadership of government agencies, a significant progress has been achieved in the prevention and control of COVID-19, and the resumption of work and production for enterprises will be orderly promoted. If government agencies continue to implement extremely strict public health control measures, it will disrupt normal life, production, and economic development. Additionally, considering the fiscal expenditure, government agencies prefer the “weak supervision” strategy. As the public become fully aware of the epidemic, it is difficult for the Internet media to benefit from topics about the epidemic, so the Internet media will adopt the “no promoting” strategy to maximize their profits. Additionally, the general public tend to apply the “voluntary isolation” strategy to promote business recovery and reduce personal loss. Therefore, this stage corresponds to the equilibrium point (0,1,1).
Table 4 shows that to achieve the equilibrium point (0,1,1) as a stable point, three conditions are supposed to be satisfied: (i)
, that is, if costs of prevention and control caused by “strong supervision” are greater than trust losses from the “weak supervision” strategy, then the “weak supervision” strategy will be adopted by government agencies; (ii)
, that is, if trust losses caused by “no promoting” strategy by the Internet media exceed the revenues from the “promoting” strategy, then the “no promoting” strategy is chosen by the Internet media; (iii)
, that is, if socio-economic losses of the “free flowing” strategy exceed the personal loss of the “voluntary isolation” strategy, then the “voluntary isolation” strategy will be preferred by the general public. The system evolution path is shown in
Figure 7.
4. Conclusions and Recommendations
The Internet media, as the main promoters and information guiders of online public opinion, plays a significant role in the process of epidemic prevention and control. However, there are many conflicts of interest among government agencies, the Internet media, and the general public. Therefore, this paper considers the Internet media as a game subject. Based on dynamic game theory, a trilateral evolutionary game model among government agencies, the Internet media and the general public is constructed to explore the sustainable collaborative prevention and control for public health emergencies. This paper links the theoretical study with the realistic prevention and control of COVID-19 to provide decision-making support and policy recommendations for scientific prevention.
4.1. Research Conclusions
In this paper, a trilateral evolutionary game model among government agencies, the Internet media and the general public is constructed, and the evolutionary stable strategies of game subjects are analyzed. Moreover, based on system dynamics theory, the impact of initial strategies and key factors on equilibrium strategies are further simulated. The main results are as follows:
(1) At the early stage, the outbreak stage, and the resumption stage of the epidemic, the tripartite equilibrium strategies of government agencies, the Internet media and the general public are significantly different. In order to maximize their own profits, the “weak supervision”, “promoting” and “free flowing” strategies will be adopted at the early stage; the “strong supervision”, “no promoting” and “voluntary isolation” strategies will be used at the outbreak stage; and the “weak supervision”, “no promoting” and “voluntary isolation” strategies will be applied at the resumption of work and production stage.
(2) Changes in initial probabilities will not affect the final stable state of the system but will affect the time to reach it. Taking the equilibrium strategies at the resumption of work and production stage as an example, on the one hand, as the initial probability of government agencies adopting the “strong supervision” strategy increases, the time for the Internet media and the general public to reach a stable state will be shortened. On the other hand, as the initial probability of Internet media adopting the “no promoting” strategy increases, government agencies converge faster to the “weak regulation” strategy, whereas the general public converge slower to the “voluntary isolation” strategy.
(3) When the epidemic spread probability increases, its effects on the equilibrium strategies of government agencies, the Internet media and the general public are heterogeneous. Government agencies converge more slowly toward the “weak supervision” strategy, whereas the Internet media and the general public converge more quickly toward “no promoting” and “voluntary isolation” strategies.
4.2. Research Recommendations
Our results shed light on the evolutionary stable strategies of government agencies, Internet media and the public during the pandemic. Based on the above analysis, the following recommendations for decision maker are proposed for preventing and controlling epidemics:
(1) As the leader of epidemic prevention and control, government agencies should pay attention to the interests of the public and supervise the behavior of Internet media to promote the coordinated prevention and control of multiple subjects. From the above analysis, it can be found that the equilibrium strategy of the subjects changes continuously with the evolutionary trend of epidemic, and the stable point changes sequentially from (0,0,0), to (1,1,1), and then to (0,1,1). Thus, in the process of epidemic prevention and control, the government cannot blindly adopt extreme strategies to simply force the public to isolate at home, but should dynamically adjust the prevention and control strategies to fit the characteristics of the epidemic and the truly needs of the public. According to our model, in the resumption stage, the stable point is (0,1,1), which recommends that the prevention and control rely more on the Internet media and the public at the time, and the supervision from the government should be appropriately relaxed to ensure the orderly resumption of production and human life. In addition, the public should strengthen their awareness of self-protection to avoid threats to their own health.
(2) In the process of epidemic prevention and control, the Internet media play a crucial role as a bridge between government agencies and the public. According to our results, in both outbreak stage and resumption stage of the epidemic, the equilibrium strategies for the Internet media are showed to be “no promoting”. Specifically, in the outbreak stage of the epidemic, various information about the epidemic is growing rapidly. Therefore, the Internet media should actively cooperate with the government in terms of disseminating prevention and control information, publicizing official policies, supervising online public opinion, and appeasing public sentiment, which can help the government improve the efficiency of epidemic prevention and control. As the epidemic enters the resumption stage and the equilibrium strategy for government gradually changes from “strong supervision” to “weak supervision”, the Internet media turns to play a much more important role in prevention and control. The Internet media is supposed to moderately promote online public opinion by timely disseminating information about the affected areas and the trajectories of confirmed cases to maintain the public’s high attention to the epidemic and daily protection. Moreover, according to system simulation analysis results, as the probability of Internet media’s initial strategy preference of “no promoting” increases, the speed of government policy shift to “weak supervision” will also gradually increase, thus enabling the government to better coordinate epidemic prevention and control with economic and social development.
4.3. Future Perspectives
In the future, there are more research directions to be explored. Firstly, in the process of prevention and control of public health emergencies, there can be more subjects in the game, whereas this paper mainly focuses on the trilateral game problem. In the following research, more game subjects can be introduced to explore the collaborative prevention and control mechanism for public health emergencies, such as hospitals, GNPOs, etc., to build an evolutionary game model.
Secondly, to explore further, researchers can also combine machine learning with an evolutionary game model, where the improved model can automatically update the evolutionary paths of game subjects when their strategies change.
In addition, this paper mainly studies the impact of initial strategies and key factors in the resumption stage of the epidemic rather than the whole process of the event. In fact, research on the early warning in the early stage of the epidemic is equally important. In the future, it is also worth discussing that how to achieve effective prevention and control by tracking the dynamic changes of relevant indicators in the early stage of the epidemic.