Yongli Cai

Influenza is a global public health and economic burden. Its seasonality patterns differ considerably between geographic regions, but the factors underlying these differences are not well characterized. The data on influenza were obtained from 2019 to 2022 in Huaian. A descriptive study was used to describe the epidemiological characteristics.The DLNM(distributed lag nonlinear model) model was established to further analyze the relationship between influenza cases, meteorological factors and pollutants. In addition, the attribution risk analysis and the interaction analysis further explored the interaction between the attributable risk and meteorological factors of influenza in terms of meteorological factors. A total of 9205 cases of influenza were reported in Huaian City from 2019 to 2022, Jiangsu province, of which 4938 cases were males and 4267 cases were females.The DLNM results showed an inverted U-shaped relationship between PM_2.5(Fine Particulate Matter) and temperature and influenza.The low concentration of PM_2.5 and O₃(Ozone) showed decreased risks, and the maximum effect values appeared on the 8th day (RR(Relative Ris) = 0.35,95 %CI(Confidence Interval): 0.25–0.49) and the 2nd day (RR = 0.63,95 %CI: 0.52–0.77). At the high concentration, the cumulative RR values of PM_2.5 and O₃ reached their maximum on the 8th day (RR = 1.93,95 %CI: 1.47–2.54) and the 9th day (RR = 2.58,95 %CI: 1.63–4.09). The attribution analysis based on DLNM showed that the AF(attributable fraction) value of influenza attributable to the high concentration of PM_2.5 exposure was 15.90 %, equivalent to 1456 cases. AF of the high concentration of O₃ was 8.12 % (743 cases). The AF of low temperature effect was 30.91 % (2830 cases). The interaction analysis showed that high temperature reduced the influence of PM_2.5 on the onset of influenza, showing an antagonistic effect (RR = 0.31, 95 %CI: 0.15–0.65), IRR(interaction relative risk) and RERI(interaction relative risk) were 0.17 (95 %CI: 0.08–0.37) and −1.62 (95 %CI: 2.65∼-0.68), respectively. The results show that low temperature significantly increases the risk of influenza. At the low concentration of PM_2.5, the risk of influenza increases with increasing concentration but decreases at the high concentrations. At the high concentration of O₃, the risk of influenza increases rapidly. 15.90 % of influenza cases may be attributed to the high concentration of PM_2.5, equivalent to 1456 cases; temperature-induced cases mainly come from the low-temperature effect, with an AF value of 30.91 %, equivalent to 2830 cases. In addition, high temperature can effectively mitigate the impact of PM_2.5 on influenza incidence, and outdoor exposure time should be minimized in low temperature and high PM_2.5 weather.

Background: Although COVID-19 vaccines and their booster regimens protect against symptomatic infections and severe outcomes, there is limited evidence about their protection against asymptomatic and symptomatic infections in real-world settings, particularly when considering that the majority of SARS-CoV-2 Omicron infections were asymptomatic. We aimed to assess the effectiveness of the booster dose of inactivated vaccines in mainland China, i.e., Sinopharm (BBIBP-CorV) and Sinovac (CoronaVac), against Omicron infection in an Omicron BA.5 seeded epidemic.Methods: Based on an infection-naive but highly vaccinated population in Urumqi, China, the study cohort comprised all 37,628 adults who had a contact history with individuals having SARS-CoV-2 infections, i.e., close contacts, between August 1 and September 7, 2022. To actively detect SARS-CoV-2 infections, RT-PCR tests were performed by local authorities on a daily basis for all close contacts, and a testing-positive status was considered a laboratory-confirmed outcome. The cohort of close contacts was matched at a ratio of 1:5 with the fully vaccinated (i.e., 2 doses) and booster vaccinated groups (i.e., 3 doses) according to sex, age strata, calendar date, and contact settings. Multivariate conditional logistic regression models were adopted to estimate the marginal effectiveness of the booster dose against Omicron BA.5 infection after adjusting for confounding variables. Subgroup analyses were performed to assess vaccine effectiveness (VE) in different strata of sex, age, the time lag from the last vaccine dose to exposure, and the vaccination status of the source case. Kaplan–Meier curves were employed to visualize the follow-up process and testing outcomes among different subgroups of the matched cohort.Findings: Before matching, 37,099 adult close contacts were eligible for cohort enrolment. After matching, the 2-dose and 3-dose groups included 3317 and 16,051 contacts, and the proportions with Omicron infections were 1.03% and 0.62% among contacts in the 2-dose and 3-dose groups, respectively. We estimated that the adjusted effectiveness of the inactivated booster vaccine versus 2 doses against Omicron infection was 35.5% (95% CI 2.0, 57.5). The booster dose provided a higher level of protection, with an effectiveness of 60.2% (95% CI 22.8, 79.5) for 15–180 days after vaccination, but this VE decreased to 35.0% (95% CI 2.8, 56.5) after 180 days. Evidence for the protection of the booster dose was detected among young adults aged 18–39 years, but was not detected for those aged 40 years or older.Interpretation: The receipt of the inactivated vaccine booster dose was associated with a significantly lower Omicron infection risk, and our findings confirmed the vaccine effectiveness (VE) of booster doses against Omicron BA.5 variants. Given the rapid evolution of SARS-CoV-2, we highlight the importance of continuously monitoring the protective performance of vaccines against the genetic variants of SARS-CoV-2, regardless of existing vaccine coverage.

The novel coronavirus disease 2019 (COVID-19) outbreak on the Diamond Princess ship has caused over 634 cases as of February 20, 2020. We model the transmission process on the ship with a stochastic model and estimate the basic reproduction number at 2.2 (95%CI: 2.1-2.4). We estimate a large dispersion parameter than other coronaviruses, which implies that the virus is difficult to go extinction. The epidemic doubling time is at 4.6 days (95%CI: 3.0-9.3), and thus timely actions were crucial. The lesson learnt on the ship is generally applicable in other settings.