In this study, we analysed the relationship between meteorological factors and the number of patients with coronavirus disease 2019 (COVID-19). The study period was from 12 April 2020 to 13 October 2020, and daily meteorological data and the daily number of patients with COVID-19 in each state of the United States were collected. Based on the number of COVID-19 patients in each state of the United States, we selected four states (California, Florida, New York, Texas) for analysis. One-way analysis of variance ( ANOVA), scatter plot analysis, correlation analysis and distributed lag nonlinear model (DLNM) analysis were used to analyse the relationship between meteorological factors and the number of patients with COVID-19. We found that the significant influencing factors of the number of COVID-19 cases differed among the four states. Specifically, the number of COVID-19 confirmed cases in California and New York was negatively correlated with AWMD (P < 0.01) and positively correlated with AQI, PM2.5 and TAVG (P < 0.01) but not significantly correlated with other factors. Florida was significantly correlated with TAVG (positive) (P < 0.01) but not significantly correlated with other factors. The number of COVID-19 cases in Texas was only significantly negatively associated with AWND (P < 0.01). The influence of temperature and PM2.5 on the spread of COVID-19 is not obvious. This study shows that when the wind speed was 2 m/s, it had a significant positive correlation with COVID-19 cases. The impact of meteorological factors on COVID-19 may be very complicated. It is necessary to further explore the relationship between meteorological factors and COVID-19. By exploring the influence of meteorological factors on COVID-19, we can help people to establish a more accurate early warning system.

In this study, we quantify and evaluate the transmission capacity of different types of influenza, and evaluate the flu vaccination effect. Taking the influenza cases reported by the National Influenza Center of China from 2010 to 2019 as the research object (http://www.chinaivdc.cn/cnic), we established the SEIABR model to calculate the influenza infection rate and R₀ for each year from 2010 to 2019, and calculate the influenza A and B influenza infection rates. We further added vaccination measures to the SEIABR model, and analysis the impact of different vaccination rates on the spread of influenza. We find that the range of β(infection rate) is $6.03\times {10}^{-10}$ to $9.66\times {10}^{-10}$, and the average is $\left(7.95\pm 1.27\right)\times {10}^{-10}$, the range of R₀ is .98 to 1.47, and the average is 1.21. Simulation result suggest that vaccine coverage needed to reach 60%-80% to control the spread of influenza virus in China when the vaccine effectiveness was 20%-40%. When the vaccine effectiveness is 40%-60%, vaccine coverage needs to reach 40%-60% to control the spread of influenza virus in China. In China, the infection rate of influenza A is higher than influenza B, to better control the spread of the flu virus, we suggest that we also need to increase the number of people vaccinated or improve the efficiency of vaccines(the current vaccination coverage is probably less than 20%).

This study mainly uses simulation technology to simulate the COVID-19 epidemic in Changsha, Hunan Province, China, and analyze the impact of different prevention and control measures on the epidemic. we Collect the information of all COVID-19 patients in Changsha from January 21, 2020 to March 14, 2020 and relevant policies during the COVID-19 epidemic in Changsha. Established the SEIAR infectious disease dynamics model under natural conditions, and added isolation measures on this basis. Using Anylogic8.5, the COVID-19 epidemic in Changsha City was simulated under various conditions based on the established model.In this study we find that There were 242 COVID-19 patients in Changsha. including 121 males (50%) and 121 females (50%).Most cases occurred between February 6 and February 16. Through the calculation of the R_t during the epidemic in Changsha, it is found that it is reasonable to resume work on February 8, because the R_t value of Changsha dropped below 1 at this time.The simulation results show that reducing the contact rate of residents and reducing the success rate of virus transmission (wearing masks, disinfection, etc.) can effectively prevent the spread of COVID-19 and significantly reduce the number of peak patients.We believe that the disease is mainly spread by the respiratory tract. Therefore, the simulation results show that whether in the early or mid-stage of the epidemic, quarantining the names of residents or reducing the contact rate of residents is very effective in controlling the COVID-19 epidemic.