<p>Evidence-Based Framework and Implementation of China’s Strategy in Combating COVID-19</p>

The value of discharged case fatality rate in estimating the severity and epidemic trend of COVID-19 in China: a novel epidemiological study

Aim

The main objective of this study was to explore the value of the discharged case fatality rate (DCFR) in estimating the severity and epidemic trend of COVID-19 in China.

Subjects and methods

Epidemiological data on COVID-19 in China and Hubei Province were obtained from the National Health Commission of the People's Republic of China from January 20, 2020, to March 31, 2020. The number of daily new confirmed cases, daily confirmed deaths, daily recovered cases, the proportion of daily deaths and total deaths of discharged cases were collected, and the total discharge case fatality rate (tDCFR), daily discharge case fatality rate (dDCFR), and stage-discharge case fatality rate (sDCFR) were calculated. We used the R software (version 3.6.3, R core team) to apply a trimmed exact linear time method to search for changes in the mean and variance of dDCFR in order to estimate the pandemic phase from dDCFR.

Results

The tDCFR of COVID-19 in China was 4.16% until March 31, 2020. According to the pattern of dDCFR, the pandemic was divided into four phases: the transmission phase (from January 20 to February 2), the epidemic phase (from February 3 to February 14), the decline phase (from February 15 to February 22), and the sporadic phase (from February 23 to March 31). The sDCFR for these four phases was 43.18% (CI 39.82-46.54%), 13.23% (CI 12.52-13.94%), 5.86% (CI 5.49-6.22%), and 1.61% (CI 1.50-1.72%), respectively.

Conclusion

DCFR has great value in assessing the severity and epidemic trend of COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10389-023-01895-4.

The Early Warning Mechanism of Public Health Emergencies Through Whistleblowing: A Perspective Based on Considering the Uncertainty of Risk Perception

Purpose

During the early warning period of public health emergencies, the information released by whistleblowers on the risk posed by the given event can reduce uncertainty in the public's risk perception and help governments take timely actions to contain the large-scale dissemination of risk. The purpose of this study is to give full play to whistleblowers and draw attention to the risk events, forming a pluralistic model of the risk governance during the early warning period of public health emergencies.

Methods

We construct an evolutionary game model of the early warning of public health emergencies through whistleblowing that involves the government, whistleblowers, and the public, discussing the mechanism of interaction between these subjects under the uncertainty of risk perception. Furthermore, we use numerical simulations to analyze the influence of changes in the relevant parameters on the evolutionary trajectory of the subjects' behaviors.

Results

The results of the research are obtained by numerical simulation of the evolutionary game model. The results show that the public's cooperation with the government encourages the latter to take a positive guidance strategy. Increasing the reward for whistleblowers within an acceptable cost, strengthening the propaganda of the mechanism and the higher level of risk perception of the government and whistleblowers will promote whistleblowers' vocalization actively. When the government's reward for whistleblowers is lower, the whistleblowers choose negative vocalization with the improvement of the public's risk perception. If there is no mandatory guidance from the government at this point, the public is prone to passively cooperating with the government owing to a lack of risk-related information.

Conclusion

Establishing an early warning mechanism through whistleblowing is important for containing risk in the early warning period of public health emergencies. Building the whistleblowing mechanism in daily work can improve the effectiveness of the mechanism and enhance the public's risk perception better when the public health emergencies arise.

A Pre-Generation of Emergency Reference Plan Model of Public Health Emergencies with Case-Based Reasoning

Background and Purpose

In the early 21st century, the coronavirus alone has ravaged the world three times. Public health emergencies have caused a tremendous negative impact on public health, daily life, and global economic development, for having the characteristics of complexity and great harm. To tackle these problems, a pre-generation of emergency reference plan model of public health emergencies is proposed to better deal with the outbreak and spread of public health events.

Methods

The method is divided into three stages. First, the modified SEIR model is used to predict the attribute values of the target case. Then, the similar case sets are extracted and filtered by calculating the similarity through the cross-efficiency evaluation method with the parallel system. Finally, the multi-stage emergency effect evaluation model is conducted so that the emergency plan with the best response effect at this stage can be made for reference.

Results

We collected 25 typical events of COVID-19 that occurred in 11 cities in China as historical case bases and target cases, respectively. The result of the experiment verified the feasibility and effectiveness of the proposed method.

Conclusion

This paper presents a new perspective on making a public health emergency plan, which could improve the decision-making accuracy and efficiency, maximize the emergency effect and save precious time for emergency response. This model can provide rapid decision supports for decision-making for public services such as government departments, centers for disease control, medical emergency centers and transport authorities, etc.

Dynamic Analysis of a COVID-19 Vaccination Model with a Positive Feedback Mechanism and Time-Delay

As the novel coronavirus pandemic has spread globally since 2019, most countries in the world are conducting vaccination campaigns. First, based on the traditional SIR infectious disease model, we introduce a positive feedback mechanism associated with the vaccination rate, and consider the time delay from antibody production to antibody disappearance after vaccination. We establish an UVaV model for COVID-19 vaccination with a positive feedback mechanism and time-delay. Next, we verify the existence of the equilibrium of the formulated model and analyze its stability. Then, we analyze the existence of the Hopf bifurcation, and use the multiple time scales method to derive the normal form of the Hopf bifurcation, further determining the direction of the Hopf bifurcation and the stability of the periodic solution of the bifurcation. Finally, we collect the parameter data of some countries and regions to determine the reasonable ranges of multiple parameters to ensure the authenticity of simulation results. Numerical simulations are carried out to verify the correctness of the theoretical results. We also give the critical time for controllable widespread antibody failure to provide a reference for strengthening vaccination time. Taking two groups of parameters as examples, the time of COVID-19 vaccine booster injection should be best controlled before 38.5 weeks and 35.3 weeks, respectively. In addition, study the impact of different expiration times on epidemic prevention and control effectiveness. We further explore the impact of changes in vaccination strategies on trends in epidemic prevention and control effectiveness. It could be concluded that, under the same epidemic vaccination strategy, the existence level of antibody is roughly the same, which is consistent with the reality.