In this study, we analysed the relationship between meteorological factors and the number of patients with COVID-19. The study period was from April 12, 2020 to October 13, 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 ANOVA(Analysis of Variance), 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 R 0 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 to , and the average is , the range of R 0 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%).

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