Background Pandemics, caused by unknown high-consequence pathogens, referred to as Disease “X”, pose extreme yet plausible threats to public health security. Their inherent unpredictability challenges conventional “predict-and-respond” paradigms, requiring more resilient, adaptive approaches to preparedness. This study proposes a shift toward a Scenario-and-Respond framework to enhance systemic resilience in the face of deep uncertainty. Methods We developed an interdisciplinary Scenario-and-Respond framework integrating scenario construction, grounded theory, systematic literature review, expert elicitation, and epidemiological modeling. The framework was retrospectively applied to the 2009–2010 H1N1 influenza pandemic in Beijing, Guangdong and Sichuan to validate its validity, robustness, and utility for anticipatory governance. Results This study generated four plausible, high-impact transmission scenarios for Disease “X”, each driven by distinct combinations of disease transmissibility, disease severity, transmission contribution from asymptomatic cases, epidemiological features of infectious disease outbreaks and medical resource demand. These scenarios enabled dynamic risk assessment under uncertainty and were validated against empirical H1N1 data. These results demonstrate that scenario-based planning proposes a shift from the traditional “predict-and-respond” paradigm to a more adaptive “Scenario-and-Respond” decision-making framework. Conclusion By replacing rigid prediction with flexible foresight, the Scenario-and-Respond framework offers a practical, resilience-oriented approach to pandemic preparedness. It provides actionable intelligence to strengthen adaptive capacity, reduce systemic vulnerability, and support safety-critical decisions in the face of emerging Disease “X” threats.

Traditional epidemic intelligence relies heavily on human epidemiologists for data interpretation and reporting, which makes it resource intensive, slow to respond, and vulnerable to variability in professional expertise. To overcome these limitations, we propose an expanded conceptual epidemic intelligence quadripartite framework that extends the classical trinity of (1) surveillance, (2) risk evaluation, and (3) early warning with a fourth pillar, (4) decision support and intervention optimization through AI agents. Acting as 24/7 digital epidemiologists, multiagent systems can integrate heterogeneous signals from multisource surveillance systems, conduct contextual risk evaluation and adaptive forecasting, generate tailored early warnings, and provide actionable recommendations for targeted control-closing the loop between detection and response. Embedding interpretability and mandatory human-in-the-loop oversight enhances trust and accountability. Nonetheless, real-world deployment requires addressing context-specific challenges of data quality, interoperability, robustness, governance, circular reporting, and equity. If designed with transparency, inclusiveness, and resilience, AI agents have the potential to transform epidemic intelligence into a continuously adaptive and globally connected system.

Acute respiratory infectious diseases (ARIDs) spread rapidly across diverse settings, necessitating accurate identification of high-risk scenarios for timely intervention. This study develops a quantitative, setting-specific risk assessment framework integrating a Delphi–AHP-based indicator system with a dual-track Bayesian calibration model. Through literature review, two-round Delphi consultation, and AHP weighting, a hierarchical indicator system comprising 4 primary, 9 secondary, and 28 tertiary indicators was constructed, with infection source characteristics receiving the highest weight. The system demonstrated high internal consistency (Cronbach’s α = 0.970–0.998), acceptable structural validity (KMO = 0.808), and substantial expert consensus. Using data from 14 outbreak settings, Bayesian inference was performed via MCMC sampling and conjugate updating under both weakly informative and informative priors. The weighted risk scores showed a positive association with secondary attack rates (r = 0.622, p = 0.0176), and parameter estimates remained stable across prior specifications, indicating preliminary robustness under limited sample conditions. This integrated Delphi–AHP and Bayesian framework supports scenario-based risk differentiation for ARIDs and provides a potential foundation for future AI-assisted prediction of emerging infectious diseases, pending further validation.

ObjectiveTo analyze the epidemiological investigation scenario patterns of respiratory infectious disease outbreaks and design intelligent data collection strategies for different epidemiological investigation data collection objectives. MethodsWe systematically reviewed and sorted the occurrence scenarios of typical respiratory infectious disease outbreaks in China. Using literature research, focus group interviews, and expert consultations, we summarized the structural and characteristic patterns of epidemiological investigation scenarios for respiratory infectious diseases. Combined with currently used intelligent data collection technologies and tools, we designed intelligent data collection strategies for epidemiological investigations of respiratory infectious disease outbreaks, established intelligent data collection modules for epidemiological investigations, and analyzed and organized the specific collection content and modes of each module. ResultsEpidemiological investigation scenario patterns of respiratory infectious disease outbreaks include case-based epidemiological investigations and epidemiological investigations of affected sites. Intelligent collection strategies were determined based on the data collection objectives of the epidemiological investigations, including detailed and precise case-based epidemiological investigation data collection for a small number of specific high-risk populations, simple and rapid case-based epidemiological investigation data collection for a large number of unknown risk populations, and epidemiological investigation data collection for affected sites. Seven intelligent data collection modules for epidemiological investigations were established, and a total of 1 012 epidemiological investigation collection variables were organized. ConclusionsBased on the transmission scenarios of respiratory infectious disease outbreaks and the data collection objectives of epidemiological investigations under different transmission scenarios, this study proposes targeted intelligent data collection strategies for epidemiological investigations of respiratory infectious disease outbreaks, establishes intelligent collection modules, and determines the collection content and modes, laying the foundation for the design and development of intelligent epidemiological investigation systems.

What is already known about this topic?:Since May 2022, a global outbreak of mpox has emerged in more than 100 non-endemic countries. As of December 2023, over 90,000 cases had been reported. The outbreak has predominantly affected men who have sex with men (MSM), with sexual contact identified as the principal mode of transmission. What is added by this report?:Since June 2023, China has faced an occurrence of mpox, predominantly affecting the MSM population. Approximately 90% of those affected reported engaging in homosexual behavior within 21 days prior to symptom onset, a trend that aligns with the global outbreak pattern. The prompt identification of cases, diligent tracing of close contacts, and the implementation of appropriate management strategies have successfully mitigated the spread of mpox virus in China. What are the implications for public health practice?:We propose that mpox is transmitted locally within China. Drawing from our experiences in controlling the virus spread, it is crucial to investigate and formulate effective surveillance and educational strategies. Importantly, we must encourage high-risk populations to promptly seek medical care upon the onset of symptoms.

R The reproduction number ( ) serves as a fundamental metric in the examination of infectious disease outbreaks, epidemics, and pandemics. Despite an array of available methods for estimating , both newcomers and established public health professionals often encounter difficulties in comprehending the circumstances for their use and their constrictions. Consequently, this review intends to offer elementary guidance on ’s selection and estimation approaches. To facilitate our review, we executed an extensive search on PubMed and Web of Science applying the following search approach: [“Basic Reproduction Number/classification”(Mesh)] AND [“Basic Reproduction Number/prevention and control” (Mesh)] OR [“Basic Reproduction Number/statistics and numerical data”(Mesh)]. Our search parameters were restricted to articles published from January 2013 to January 2023. This search rendered a total of 7,094 articles, of which we selected 60 that met our inclusion standards for further analysis.

Mathematical models have played an important role in the management of the coronavirus disease 2019 (COVID-19) pandemic. The aim of this review is to describe the use of COVID-19 mathematical models, their classification, and the advantages and disadvantages of different types of models. We conducted subject heading searches of PubMed and China National Knowledge Infrastructure with the terms "COVID-19," "Mathematical Statistical Model," "Model," "Modeling," "Agent-based Model," and "Ordinary Differential Equation Model" and classified and analyzed the scientific literature retrieved in the search. We categorized the models as data-driven or mechanism-driven. Data-driven models are mainly used for predicting epidemics, and have the advantage of rapid assessment of disease instances. However, their ability to determine transmission mechanisms is limited. Mechanism-driven models include ordinary differential equation (ODE) and agent-based models. ODE models are used to estimate transmissibility and evaluate impact of interventions. Although ODE models are good at determining pathogen transmission characteristics, they are less suitable for simulation of early epidemic stages and rely heavily on availability of first-hand field data. Agent-based models consider influences of individual differences, but they require large amounts of data and can take a long time to develop fully. Many COVID-19 mathematical modeling studies have been conducted, and these have been used for predicting trends, evaluating interventions, and calculating pathogen transmissibility. Successful infectious disease modeling requires comprehensive considerations of data, applications, and purposes.

Background Recently, despite the steady decline in the tuberculosis (TB) epidemic globally, school TB outbreaks have been frequently reported in China. This study aimed to quantify the transmissibility of Mycobacterium tuberculosis (MTB) among students and non-students using a mathematical model to determine characteristics of TB transmission. Methods We constructed a dataset of reported TB cases from four regions (Jilin Province, Xiamen City, Chuxiong Prefecture, and Wuhan City) in China from 2005 to 2019. We classified the population and the reported cases under student and non-student groups, and developed two mathematical models [nonseasonal model (Model A) and seasonal model (Model B)] based on the natural history and transmission features of TB. The effective reproduction number ( R eff ) of TB between groups were calculated using the collected data. Results During the study period, data on 456,423 TB cases were collected from four regions: students accounted for 6.1% of cases. The goodness-of-fit analysis showed that Model A had a better fitting effect ( P < 0.001). The average R eff of TB estimated from Model A was 1.68 [interquartile range (IQR): 1.20–1.96] in Chuxiong Prefecture, 1.67 (IQR: 1.40–1.93) in Xiamen City, 1.75 (IQR: 1.37–2.02) in Jilin Province, and 1.79 (IQR: 1.56–2.02) in Wuhan City. The average R eff of TB in the non-student population was 23.30 times (1.65/0.07) higher than that in the student population. Conclusions The transmissibility of MTB remains high in the non-student population of the areas studied, which is still dominant in the spread of TB. TB transmissibility from the non-student-to-student-population had a strong influence on students. Specific interventions, such as TB screening, should be applied rigorously to control and to prevent TB transmission among students. Graphical Abstract

Objective:To set up a structured or semi-structured database of public health emergencies, to integrate, extract, summarize and speculate effectively based on multi-source information, and to provide informatization and digitization support for emergency decision-making.Methods:Qualitative research methods, such as focus group and nominal group technique, in-depth interviews, were used to collect data and professional expertise of cases focusing on case categories, inclusion and exclusion principle, compiling framework and main influence factors. Structured model was constructed based on analyzing and summarizing emergency case essentials.Results:Twenty-five public health emergency cases were selected and included in this study. Followed by the 7-step compiling framework constructed in this study, a description-solution-conclusion scheme was proposed, composed of 9 parts, including structured case file, overview, chronicles, prevention and preparation, response and rescue, risk communication, restoration and reconstruction, after action report, references and attachments.Conclusion:A structured model was designed from the scenario-to-response perspective, with a database constructed following the standardized procedures, to retrospect the whole process from various aspects for the quick and impersonal reference of policy makers, which helping to overcome several obstacles for decision-making, for example, urgency, complexity, information uncertainty and accessibility, during public health emergency response, as well as further inspire the artificial intelligence utilization of historical experiences.

Abstract Evaluations of the pandemic to endemic phase are a great concern, especially in Zero-COVID-19 countries. Herein, we developed a mathematical model to simulate future scenarios for the variants of concern (VOCs) in the condition of several immune barriers and controlling measures. The results demonstrated that the Omicron variant would lead to 592.0 (mean ± standard deviation (SD): 433.9–750.0) million symptomatic, 24.3 (mean ± SD: 17.4–312.8) million hospital admission, 9.6 (mean ± SD:7.0–12.3) million ICU admission, and 5.4 (mean ± SD:3.7–7.5) million death cases after simulation with 1,000 days. At the endemic phase, there were nearly 500 death cases per day attributed to reinfection (66% [range: 62–70%]), infection from birth (18% [range: 16–21%]), and infection from migration (16% [range: 14–17%]). Actively treating more than 80% of cases could effectively reduce disease severity and death rates. It is feasible to transmit pandemic to endemic with Omicron variant and other milder VOCs. We recommend that the successful transition strategy is to improve medical resource allocation and enhance the prevention and control capabilities of health agencies.

Background Hand-Foot-and-Mouth-Disease (HFMD) has been widely spread in Asia, and has result in a high disease burden for children in many countries. However, the dissemination characteristics intergroup and between different age groups are still not clear. In this study, we aim to analyze the differences in the transmissibility of HFMD, in the whole population and among age groups in Shenzhen city, by utilizing mathematical models. Methods A database that reports HFMD cases in Shenzhen city from January 2010 to December 2017 was collected. In the first stage, a Susceptive-Infected-Recovered (SIR) model was built to fit data of Shenzhen city and its districts, and Reff was used to assess transmissibility in each district. In the second stage, a cross-age groups SIR model was constructed to calculate the difference in transmissibility of reported cases among three age groups of EV71 virus: 0–3 years, 3–5 years, and over 5 years which was denoted as age group 1, 2, and 3, respectively. Results From 2010 to 2017, 345,807 cases of HFMD were reported in Shenzhen city, with peak incidence in spring and autumn in Shenzhen city and most of its districts each year. Analysis of the EV71 incidence data by age group revealed that age Group 1 have the highest incidence (3.13 ×10−7–2.31 ×10−4) while age group 3 had the lowest incidence (0–3.54 ×10−5). The differences in weekly incidence of EV71 between age groups were statistically significant (t12 = 7.563, P < 0.0001; t23 = 12.420, P < 0.0001; t13 = 16.996, P < 0.0001). The R2 of the SIR model Shenzhen city population-wide HFMD fit for each region was >0.5, and P < 0.001. Reff values were >1 for the vast majority of time and regions, indicating that the HFMD virus has the ability to spread in Shenzhen city over the long-term. Differences in Reff values between regions were judged by using analysis of variance (ANOVA) (F = 0.541, P = 0.744). SiIiRi-SjIjRj models between age groups had R2 over 0.7 for all age groups and P <0.001. The Reff values between groups show that the 0–2 years old group had the strongest transmissibility (median: 2.881, range: 0.017–9.897), followed by the over 5 years old group (median: 1.758, range: 1.005–5.279), while the 3–5 years old group (median: 1.300, range: 0.005–1.005) had the weakest transmissibility of the three groups. Intra-group transmissibility was strongest in the 0–2 years age group (median: 1.787, range: 0–9.146), followed by Group 1 to Group 2 (median: 0.287, range: 0–1.988) and finally Group 1 to Group 3 (median: 0.287, range: 0–1.988). Conclusion The incidence rate of HFMD is high in Shenzhen city. In the data on the incidence of EV71 in each age group, the highest incidence was in the 0–2 years age group, and the lowest incidence was in the over 5 years age group. The differences in weekly incidence rate of EV71 among age groups were statistically significant. Children with the age of 0–2 years had the highest transmissibility.

目的 通过对2007-2018吉林省肾综合征出血热病例进行流行特征描述,使用时间序列分析,预测吉林省未来的肾综合征出血热发病趋势.方法 通过建立ARIMA模型,对2007-2018年间吉林省肾综合征出血热发病数进行拟合,使用2019-2021年发病数与预测结果之间进行拟合优度检验来验证拟合效果.结果 2007-2018年间,吉林省共上报肾综合征出血热8 844例.总体来说吉林省肾综合征出血热发病率呈现缓慢下降趋势,时间序列分析结果为:ARIMA(1,0,0)(0,1,2)12较好地拟合和预测吉林省肾综合征出血热发病数(R2=0.66,P＜0.05).2019年至2021年预测肾综合征出血热发病数分别为510人、449人和513人.将2019-2021年HFRS实际病例数并与ARIMA模型预测数据进行比较,计算相关系数R2=0.85,P＜0.05.结论 ARIMA模型能较好地拟合和预测吉林省HFRS发病数.

ObjectivesThis study aims to explore the interaction of different pathogens in Hand, foot and mouth disease (HFMD) by using a mathematical epidemiological model and the reported data in five regions of China.MethodsA cross-regional dataset of reported HFMD cases was built from four provinces (Fujian Province, Jiangsu province, Hunan Province, and Jilin Province) and one municipality (Chongqing Municipality) in China. The subtypes of the pathogens of HFMD, including Coxsackievirus A16 (CV-A16), enteroviruses A71 (EV-A71), and other enteroviruses (Others), were included in the data. A mathematical model was developed to fit the data. The effective reproduction number (Reff) was calculated to quantify the transmissibility of the pathogens.ResultsIn total, 3,336,482 HFMD cases were collected in the five regions. In Fujian Province, the Reff between CV-A16 and EV-A71&CV-A16, and between CV-A16 and CV-A16&Others showed statistically significant differences (P < 0.05). In Jiangsu Province, there was a significant difference in Reff (P < 0.05) between the CV-A16 and Total. In Hunan Province, the Reff between CV-A16 and EV-A71&CV-A16, between CV-A16 and Total were significant (P < 0.05). In Chongqing Municipality, we found significant differences of the Reff (P < 0.05) between CV-A16 and CV-A16&Others, and between Others and CV-A16&Others. In Jilin Province, significant differences of the Reff (P < 0.05) were found between EV-A71 and Total, and between Others and Total.ConclusionThe major pathogens of HFMD have changed annually, and the incidence of HFMD caused by others and CV-A16 has surpassed that of EV-A71 in recent years. Cross-regional differences were observed in the interactions between the pathogens.

Background The epidemiological characteristics and transmissibility of Coronavirus Disease 2019 (COVID-19) may undergo changes due to the mutation of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) strains. The purpose of this study is to compare the differences in the outbreaks of the different strains with regards to aspects such as epidemiological characteristics, transmissibility, and difficulties in prevention and control. Methods COVID-19 data from outbreaks of pre-Delta strains, the Delta variant and Omicron variant, were obtained from the Chinese Center for Disease Control and Prevention (CDC). Case data were collected from China's direct-reporting system, and the data concerning outbreaks were collected by on-site epidemiological investigators and collated by the authors of this paper. Indicators such as the effective reproduction number (Reff), time-dependent reproduction number (Rt), rate of decrease in transmissibility (RDT), and duration from the illness onset date to the diagnosed date (DID)/reported date (DIR) were used to compare differences in transmissibility between pre-Delta strains, Delta variants and Omicron variants. Non-parametric tests (namely the Kruskal-Wallis H and Mean-Whitney U tests) were used to compare differences in epidemiological characteristics and transmissibility between outbreaks of different strains. P < 0.05 indicated that the difference was statistically significant. Results Mainland China has maintained a “dynamic zero-out strategy” since the first case was reported, and clusters of outbreaks have occurred intermittently. The strains causing outbreaks in mainland China have gone through three stages: the outbreak of pre-Delta strains, the outbreak of the Delta variant, and outbreaks involving the superposition of Delta and Omicron variant strains. Each outbreak of pre-Delta strains went through two stages: a rising stage and a falling stage, Each outbreak of the Delta variant and Omicron variant went through three stages: a rising stage, a platform stage and a falling stage. The maximum Reff value of Omicron variant outbreaks was highest (median: 6.7; ranged from 5.3 to 8.0) and the differences were statistically significant. The RDT value of outbreaks involving pre-Delta strains was smallest (median: 91.4%; [IQR]: 87.30–94.27%), and the differences were statistically significant. The DID and DIR for all strains was mostly in a range of 0–2 days, with more than 75%. The range of duration for outbreaks of pre-Delta strains was the largest (median: 20 days, ranging from 1 to 61 days), and the differences were statistically significant. Conclusion With the evolution of the virus, the transmissibility of the variants has increased. The transmissibility of the Omicron variant is higher than that of both the pre-Delta strains and the Delta variant, and is more difficult to suppress. These findings provide us with get a more clear and precise picture of the transmissibility of the different variants in the real world, in accordance with the findings of previous studies. Reff is more suitable than Rt for assessing the transmissibility of the disease during an epidemic outbreak.

Hand, foot, and mouth disease (HFMD) is a serious disease burden in the Asia-Pacific region, including China. This study calculated the transmissibility of HFMD at county levels in Jiangsu Province, China, analyzed the differences of transmissibility and explored the possible influencing factors of its transmissibility. We built a mathematical model for seasonal characteristics of HFMD, estimated the effective reproduction number (R-eff), and compared the incidence rate and transmissibility in different counties using non-parametric tests, rapid cluster analysis and rank-sum ratio in 97 counties in Jiangsu Province from 2015 to 2020. The average daily incidence rate was between 0 and 4 per 100,000 people in Jiangsu Province from 2015-2020. The Quartile of R-eff in Jiangsu Province from 2015 to 2020 was 1.54 (0.49, 2.50). Rugao District and Jianhu District had the highest transmissibility according to the rank-sum ratio. R-eff generally decreased in 2017 and increased in 2018 in most counties, and the median level of R-eff was the lowest in 2017 (P<0.05). The transmissibility was different in 97 counties in Jiangsu Province. The reasons for the differences may be related to the climate, demographic characteristics, virus subtypes, vaccination, hygiene and other infectious diseases.

Public health decision-making may have great uncertainty especially in dealing with emerging infectious diseases, so it is necessary to establish a collaborative mechanism among modelers, epidemiologists, and public health decision-makers to reduce the uncertainty as much as possible. We searched the relevant studies on transmission dynamics modeling of infectious diseases, SARS, MERS, and COVID-19 as of March 1, 2021 based on PubMed. We compared the key health decision-making time points of SARS, MERS, and COVID-19 prevention and control, and the publication time points of modeling research, to reveal the collaboration between infectious disease modeling and public health decision-making in the context of the COVID-19 pandemic. Searching with infectious disease and mathematical model as keywords, there were 166, 81 and 1 289 studies on the modeling of infectious disease transmission dynamics of SARS, MERS, and COVID-19 were retrieved respectively. Based on the modeling application framework of public health practice proposed in the current study, the collaboration among modelers, epidemiologists and public health decision-makers should be strengthened in the future.

目的 对吉林省肾综合征出血热病例进行流行特征描述,分析影响肾综合征出血热发病的流行病学因素.对吉林省气象因素与发病数据的关系进行讨论,探究影响发病的气象因素.方法 采用卡方检验对2007-2019年间吉林省不同月份、不同地区、不同性别、不同年龄组以及不同职业的发病比例进行差异比较.使用类泊松回归对2007-2017年间气象数据与发病数进行拟合,得到气象因素对发病数的影响.结果 2007-2019年间,吉林省共上报肾综合征出血热9317例.吉林省肾综合征出血热呈现流行特征为:发病率呈现缓慢下降趋势,每年5-6月以及11月会出现两个发病高峰,病例以男性农业人口为主,经济相对落后地区发病率高.气象因素分析发现,气温、降水与日照的改变会影响肾综合征出血热发病数(P＜0.05),回归系数分别为1.002、1.000、0.999.结论 可针对吉林省肾综合征出血热高发地区和高发人群进行重点防控,并在发病高峰来临前做好防治准备.

Background: It is much valuable to evaluate the comparative effectiveness of the coronavirus disease 2019 (COVID-19) prevention and control in the non-pharmacological intervention phase of the pandemic across countries and identify useful experiences that could be generalized worldwide. Methods: In this study, we developed a susceptible–exposure–infectious–asymptomatic–removed (SEIAR) model to fit the daily reported COVID-19 cases in 160 countries. The time-varying reproduction number (Rt) that was estimated through fitting the mathematical model was adopted to quantify the transmissibility. We defined a synthetic index (IAC) based on the value of Rt to reflect the national capability to control COVID-19. Results: The goodness-of-fit tests showed that the SEIAR model fitted the data of the 160 countries well. At the beginning of the epidemic, the values of Rt of countries in the European region were generally higher than those in other regions. Among the 160 countries included in the study, all European countries had the ability to control the COVID-19 epidemic. The Western Pacific Region did best in continuous control of the epidemic, with a total of 73.76% of countries that can continuously control the COVID-19 epidemic, while only 43.63% of the countries in the European Region continuously controlled the epidemic, followed by the Region of Americas with 52.53% of countries, the Southeast Asian Region with 48% of countries, the African Region with 46.81% of countries, and the Eastern Mediterranean Region with 40.48% of countries. Conclusion: Large variations in controlling the COVID-19 epidemic existed across countries. The world could benefit from the experience of some countries that demonstrated the highest containment capabilities.

Background Globally, there have been 212,544,565 confirmed cases of Corona Virus disease 2019 (COVID-19), including 4,441,428 deaths by 24 August 2021, reported to WHO. Facing the global pandemic of COVID-19, countries and regions have implemented different policies and taken different non-pharmacological interventions (NPIs) according to their own circumstances. However, the quantitative assessment of national policies and local resilience capabilities is a huge challenge. Methods In order to assess interventions and improve local resilience from a comprehensive perspective, this study aims to establish a multi-dimensional and dynamic prevention and control system. The main body of the system is an index system. To make our evaluation system more scientific and useful, the comparative study with several widely used tools or lessons is conducted to report what they have done. Then analytic hierarchy process (AHP) is used to set up the framework under the concept of a multi-level strategy of public health management. Indicators in the system are determined by literature research and expert interviews. Results Emergency capability assessment includes building a well-established system, execution of the system, and measurement. The well-established system exhibits several characteristics: 1) considering indicators about whole-of-society involvement, including country-, city-, local community- and individual-level; 2) improving capability at multi-phases, from the preparedness ability to response ability; 3) at both policy level and implementation level. Categories of containment and closure, response in economic system, and response in public health system constitute the main body of the framework. The well-established system does not necessarily apply to all scenarios, and the actual situation should be taken into consideration in the process of implementation/execution. At the stage of measurement, the case of Wuhan/Hubei response is introduced to implement and test our system. Empirical researches will be conducted to verify the index system quantitatively in our future research. Conclusions Our index system can assess national policies and capabilities quantitatively. When enough data are available, it will become a tool to assess the local resilience capability for countries or regions.

Introduction: Vaccination booster shots are completely necessary for controlling breakthrough infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China. The study aims to estimate effectiveness of booster vaccines for high-risk populations (HRPs). Methods: A vaccinated Susceptible-Exposed-Symptomatic- Asymptomatic-Recovered/ Removed (SEIAR) model was developed to simulate scenarios of effective reproduction number (R-eff) from 4 to 6. Total number of infectious and asymptomatic cases were used to evaluated vaccination effectiveness. Results: Our model showed that we could not prevent outbreaks when covering 80% of HRPs with booster unless R-eff=4.0 or the booster vaccine had efficacy against infectivity and susceptibility of more than 90%. The results were consistent when the outcome index was confirmed cases or asymptomatic cases. Conclusions: An ideal coronavirus disease 2019 (COVID-19) booster vaccination strategy for HRPs would be expected to reach the initial goal to control the transmission of the Delta variant in China. Accordingly, the recommendation for the COVID-19 booster vaccine should be implemented in HRPs who are already vaccinated and could prevent transmission to other groups.