It is of great significance to analyze the configuration of programs funded by the National Natural Science Foundation of China using funding data in the field of preventive medicine from 2007 to 2021. The analysis mainly focused on the funding status of the program, funding amount, funded institutions and personnel. A total of 5 349 programs in the discipline of preventive medicine were funded over the last 15 years. The funding amount in this discipline accounted for a relatively low proportion in the total funding amount of the Department of Medical Sciences and this proportion also showed a decreasing trend. Non-infectious disease epidemiology, human nutrition, and health toxicology were the top three subdisciplines of all funded programs in preventive medicine. The proportion of programs led by young scholars was gradually rising over the years, and young scholars were playing an increasingly influential role in scientific research. The funding status among each subdiscipline and institution also varied. The results of this study reflected the thriving of preventive medicine. Meanwhile, some problems and dilemmas were also revealed in its current development. Hopefully, this study could provide valuable information for institutions and preventive medicine researchers to apply for National Natural Science Foundation, and promote the long-term development of preventive medicine in the future.

It is of great significance to analyze the configuration of programs funded by the National Natural Science Foundation of China using funding data in the field of preventive medicine from 2007 to 2021. The analysis mainly focused on the funding status of the program, funding amount, funded institutions and personnel. A total of 5 349 programs in the discipline of preventive medicine were funded over the last 15 years. The funding amount in this discipline accounted for a relatively low proportion in the total funding amount of the Department of Medical Sciences and this proportion also showed a decreasing trend. Non-infectious disease epidemiology, human nutrition, and health toxicology were the top three subdisciplines of all funded programs in preventive medicine. The proportion of programs led by young scholars was gradually rising over the years, and young scholars were playing an increasingly influential role in scientific research. The funding status among each subdiscipline and institution also varied. The results of this study reflected the thriving of preventive medicine. Meanwhile, some problems and dilemmas were also revealed in its current development. Hopefully, this study could provide valuable information for institutions and preventive medicine researchers to apply for National Natural Science Foundation, and promote the long-term development of preventive medicine in the future.

COVID-19 has brought a significant impact to the global health system, and also opportunities and challenges to epidemiological researches. Theoretical epidemiological models can simulate the process of epidemic in scenarios under different conditions. Therefore, modeling researches can analyze the epidemical trend of COVID-19, predict epidemical risks, and evaluate effects of different control measures and vaccine policies. Theoretical epidemiological modeling researches provide scientific advice for the prevention and control of infectious diseases, and play a crucial role in containing COVID-19 over the past year. In this study, we review the theoretical epidemiological modeling researches on COVID-19 and summarize the role of theoretical epidemiological models in the prevention and control of COVID-19, in order to provide reference for the combination of mathematical modeling and epidemic control.
COVID-19 ; Communicable Diseases/epidemiology* ; Humans ; Models, Theoretical ; SARS-CoV-2

Objective:In the context of COVID-19 pandemic, the epidemic severities, non-pharmaceutical intervention intensities, individual behavior patterns and vaccination coverage vary with countries in the world. China has experienced a long period without indigenous cases, unfortunately, multi local outbreaks caused by imported cases and other factors have been reported, posing great challenges to COVID-19 prevention and control in China. Thus it is necessary to explore the mechanisms of the re-emerged COVID-19 epidemics and their differences.Methods:Based on susceptible exposed infectious recovered (SEIR) epidemic dynamics model, we developed a set of novel evolution equations which can describe the dynamic processes of integrated influence of interventions, vaccination coverage and individual behavior changes on the re-emergency of COVID-19 epidemic. We developed methods to calculate the optimal intervention intensity and vaccination rate at which the size of susceptible population can be reduced to less than threshold for the re-emergency of COVID-19 epidemic.Results:If strong interventions or super interventions are lifted too early, even a small cause can lead to the re-emergence of COVID-19 epidemic at different degrees. Moreover, the stronger the early control measures lifted are, the more severe the epidemic is. The individual behavior changes for the susceptibility to the epidemic and the enhancement or lifting of prevention and control measures are key factors to influence the incidence the multi outbreaks of COVID-19. The optimist early intervention measures and timely optimization of vaccination can not only prevent the re-emergency of COVID-19 epidemic, but also effectively lower the peak of the first wave of the epidemic and delay its arrival.Conclusion:The study revealed that factors for the re-emergence of COVID-19 epidemics included the intensity and lifting of interventions, the change of individual behavior to the response of the epidemic, external incentives and the transmissibility of COVID-19.

Since December 2019, the outbreak of COVID-19 in Wuhan has spread rapidly due to population movement during the Spring Festival holidays. Since January 23rd, 2020, the strategies of containment and contact tracing followed by quarantine and isolation has been implemented extensively in mainland China, and the rates of detection and confirmation have been continuously increased, which have effectively suppressed the rapid spread of the epidemic. In the early stage of the outbreak of COVID-19, it is of great practical significance to analyze the transmission risk of the epidemic and evaluate the effectiveness and timeliness of prevention and control strategies by using mathematical models and combining with a small amount of real-time updated multi-source data. On the basis of our previous research, we systematically introduce how to establish the transmission dynamic models in line with current Chinese prevention and control strategies step by step, according to the different epidemic stages and the improvement of the data. By summarized our modelling and assessing ideas, the model formulations vary from autonomous to non-autonomous dynamic systems, the risk assessment index changes from the basic regeneration number to the effective regeneration number, and the epidemic development and assessment evolve from the early SEIHR transmission model-based dynamics to the recent dynamics which are mainly associated with the variation of the isolated and suspected population sizes.

Since the implementation of antiretroviral therapy (ART), it has achieved remarkable results in the field of HIV/AIDS treatment. However, when the treatment is applied to the population-level, the actual impact of ART on the HIV epidemic becomes a hot topic in the field. This paper will summarize the research on ART and HIV epidemic in recent years, and discuss the impact of ART on the trend of HIV epidemic, so as to provide scientific support and suggestions for the role of treatment is prevention.

Shortcomings have been inherited in the traditional influenza early warning system, often expressed through the scope, accuracy on prediction and real-time performance of the monitor related programs. With the new round of scientific and technological revolution and the increasingly maturity of modern information system, related technology on influenza early warning has become the focus of research in this field, based on big data analysis technology. Using the traditional influenza surveillance and early warning system as reference, this paper summarizes the progress of influenza early warning research, based on the Internet, influencing factors, time and space trends, and risk assessment etc., to summarize the trends on the advantages, shortcomings and future development of big data, used in the early warning system on influenza.

Objective:To establish a new model for the prediction of severe outcomes of COVID-19 patients and provide more comprehensive, accurate and timely indicators for the early identification of severe COVID-19 patients.Methods:Based on the patients’ admission detection indicators, mild or severe status of COVID-19, and dynamic changes in admission indicators (the differences between indicators of two measurements) and other input variables, XGBoost method was applied to establish a prediction model to evaluate the risk of severe outcomes of the COVID-19 patients after admission. Follow up was done for the selected patients from admission to discharge, and their outcomes were observed to evaluate the predicted results of this model.Results:In the training set of 100 COVID-19 patients, six predictors with higher scores were screened and a prediction model was established. The high-risk range of the predictor variables was calculated as: blood oxygen saturation <94 %, peripheral white blood cells count >8.0×10 9, change in systolic blood pressure <-2.5 mmHg, heart rate >90 beats/min, multiple small patchy shadows, age >30 years, and change in heart rate <12.5 beats/min. The prediction sensitivity of the model based on the training set was 61.7 %, and the missed diagnosis rate was 38.3 %. The prediction sensitivity of the model based on the test set was 75.0 %, and the missed diagnosis rate was 25.0 %. Conclusions:Compared with the traditional prediction (i.e. using indicators from the first test at admission and the critical admission conditions to assess whether patients are in mild or severe status), the new model’s prediction additionally takes into account of the baseline physiological indicators and dynamic changes of COVID-19 patients, so it can predict the risk of severe outcomes in COVID-19 patients more comprehensively and accurately to reduce the missed diagnosis of severe COVID-19.

COVID-19 is a public health emergency currently. In this study, a scale-free network model is established based on the Spring Migration data in 2020.The cities is clustered into three different modules. The epidemic of the cities in the black module was the most serious, followed by the red and the cyan. The black module contains 9 cities in Zhejiang province and 8 cities in Guangdong province, most of them located in the southeast coastal economic belt. These cities should be the key cities for epidemic prevention and control.

COVID-19 is a public health emergency currently. In this study, a scale-free network model is established based on the Spring Migration data in 2020.The cities is clustered into three different modules. The epidemic of the cities in the black module was the most serious, followed by the red and the cyan. The black module contains 9 cities in Zhejiang province and 8 cities in Guangdong province, most of them located in the southeast coastal economic belt. These cities should be the key cities for epidemic prevention and control.