Research On The Risk-early Warning And Control Models Of Large-scale Public Health Emergencies

In recent years,large-scale public health emergency,such as the novel coronavirus epidemic(COVID-19),has severely endangered the safety of world,country,and tens of millions of people around the world,and bring huge challenges to the emergency management system of the country and even the world.Adapting to the complex and ever-changing crisis situations and improving the efficiency of responding to large-scale public health emergency requires building a scientific and effective system of early warning and prevention and control.It is an inevitable requirement of emergency management.A hot topic of concern among scholars is how to scientifically and accurately predict,warn and control the risk of public health emergency.Therefore,based on China’s current development conditions,emergency management theory,optimal partition theory,optimal control theory,synergetics and evolutionary game theory,taking large-scale public health emergency as the research object and taking COVID-19 as an example,this paper deeply explores the risk prediction,early warning and control models of public health emergency from the two perspectives of micro and macro.The main methods used in this paper include literature research method,CART algorithm,Markov chain,Bayesian network model,Fisher’s optimal partition method,optimal control model,evolutionary game model,simulation,empirical analysis,etc.The main work and research conclusions of this study are as follows:(1)In this paper,a scientific and effective forward-looking prediction of public health emergency is presented,along with precise preventions and controls.Based on the risk characteristics of different development stages of public health emergencies,this paper comprehensively uses the literature method and the CART algorithm to build a risk early warning index system for large-scale public health emergencies.Then,based on the Markov chain and Bayesian network model,the EM algorithm is introduced to construct a risk level prediction model for large-scale public health emergencies.Finally,taking the novel coronavirus epidemic as an example,this paper predicts the risk level of the epidemic and compares it with the actual situation to verify the accuracy of the model.The study found that,first,by using the CART algorithm to screen the characteristic variables,this paper established the risk warning index system of largescale public health emergency in a scientific and quantitative way.Second,the risk level prediction model of large-scale public health emergency can effectively solve the problem of single index dimension in the Markov chain model,and the lack of data samples in the Bayesian network model when predicting the risk level,so as to more scientifically and accurately predict public health event risk level.Third,based on the historical data of Beijing from April 18,2020 to June 18,2020,this paper predicts the risk levels of Beijing on June 12,15,and 18,2020.The prediction results suggest that the risk of COVID-19 in Beijing near June 15 may increase significantly.The prediction results are basically consistent with the actual changes in the epidemic response level,which verifies the validity and accuracy of the model in this paper.(2)In the prevention and control of risk,early warning is the first line of defense against public health emergencies.The purpose of this paper is to scientifically estimate the risk early warning thresholds,partition the risk early warning intervals,and promptly prompt the warning situation.Based on the traditional Fisher’s optimal partition method,this paper comprehensively considers the dynamic function change relationship between multiple indicators and sample data,adopts multiple functions to fit and identify function features,and constructs an improved adaptive optimal partition model based on entropy method.Taking COVID-19 as an example,the epidemic risk warning interval is quantitatively divided,and then based on the actual development of the epidemic,it is compared with the partition results between the traditional model and our model to verify the scientific and accuracy of the model.It is found that the trend of public health emergency changes dynamically,while traditional partition model cannot identify the changes of data characteristics in different epidemic periods.The model in this paper uses eight kinds of fitting functions to reduce the sum of squares of deviations when partitioning,so as to more accurately identify the development trends of epidemic in different periods.In addition,considering the diversity of epidemic indicators,this paper uses the entropy method to assign the weight of each indicator,which not only considers the relationship between multiple indicators and sample data,but also can better avoid possible deviations caused by manual selection.Third,in order to verify the effectiveness of the model,this paper use the data of daily COVID-19 cases worldwide from January 20,2020 to March 31,2020,and gives the simulation results of risk early warning intervals.It is found that our partition results obtained by improved adaptive optimal partition model are basically consistent with the WHO statements about the epidemic development stages.What’s more,comparing with traditional partition model,the results in this paper are more accurate and the risk early warning thresholds are more advanced.Therefore,the study has guiding value for enhancing the accuracy of risk early warning of public health emergencies,and provides a theoretical reference for the construction of emergency management system,(3)Population mobility is the main dissemination path of virus,and strict control measures on population mobility are key to controlling the spread of the epidemic.In order to effectively cut off the dissemination chain,this paper explores the impact of population mobility on the risk propagation mechanism of public health emergency at the micro level.In this paper,based on the SEIR dynamics model and optimal control theory,we take COVID-19 as an example and construct an optimal control model for security risk based on population migration by using the screening population migration strategy and nucleic acid test strategy as control variables.And then,the software of MATLAB to is applied to simulate and analyze the heterogeneous effects of the two control strategies on the spread of the epidemic,which verifies the validity and scientific of the model in this paper.The results show that two or more control strategies need to be implemented together to more efficiently prevent and control the spread of the epidemic during outbreak prevention and control.This is because the control effect of the nucleic acid test strategy is significantly better than the control effect of the population migration strategy.Based on the cost minimization principle in the optimal control model,the strategy of nucleic acid test strength needs to be implemented in conjunction with other strategies to maximize the control of virus transmission.The control effect of both strategies together is better than the control effect of only one of the strategies:screening incoming population strategy and nucleic acid test strategy.The results of this paper are of great significance for improving the risk prevention and control system of public health emergency.(4)In the context of the COVID-19 pandemic,strengthening collaborative prevention and control of public health emergencies has become an important element of social governance.In the process of collaborative prevention and control of public health emergencies,there is a complex game relationship among government agencies,the Internet media and the general public.In order to explore the evolution process of participants’ behavioral strategies at the macro level,a trilateral evolutionary game model among government agencies,the Internet media and the general public is constructed,and the evolutionary path and stable strategies of game subjects are analyzed.And then,a system dynamics approach is further adopted to simulate the heterogeneous effect of different initial strategies and epidemic spread probability on the evolution of strategies.The results show that 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.Secondly,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.Finally,when the epidemic spread probability p2 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.The main contribution of the study is to systematically analyze the research on risk early warning and prevention and control models of major public health emergency from the micro and macro perspectives,and form a set of models and method systems for accurate risk prediction,early warning,and control of large-scale public health emergency.Specifically,a risk level prediction model for public health emergency has constructed to more accurately locate major risks.A risk early warning model of largescale public health emergency has constructed to more scientifically and accurately partition the risk early warning intervals for large-scale public health emergency.From the micro perspective,a risk optimization control model for large-scale public health emergency based on population migration is constructed to provide decision-making support for the systematic construction of a risk prevention and control system.The evolutionary game model of the government,the Internet media,and the public is constructed from the macro perspective.Based on the evolutionary game model and risk control model,a systematic and coordinated system for the risk prevention and control in major public health emergency is constructed.In general,the whole process of risk early warning and control of large-scale public health emergency is progressive,forming a rigorous logical structure,which has with strong novelty and provides theoretical and methodological support for the construction a system of risk early warning and prevention and control for public health emergency.