[Advance on theoretical epidemiology models research of prevention and control of COVID-19].

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.

[Analysis on dynamical mechanism of multi outbreaks of COVID-19].

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.

[Prediction of severe outcomes of patients with COVID-19].

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.

[Construction of urban scale-free network model and its epidemiological significance in the prevention and control of 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.