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The crayfish optimization algorithm (COA), proposed in 2023, is a metaheuristic optimization algorithm that is based on crayfish's summer escape behavior, competitive behavior, and foraging behavior. COA has a good optimization performance, but it still suffers from the problems of slow convergence speed and sensitivity to the local optimum. To solve these problems, an improved multi-strategy crayfish optimization algorithm for solving numerical optimization problems, called IMCOA, is proposed to address the shortcomings of the original crayfish optimization algorithm for each behavioral strategy. Aiming at the imbalance between local exploitation and global exploration in the summer heat avoidance and competition phases, this paper proposes a cave candidacy strategy and a fitness–distance balanced competition strategy, respectively, so that these two behaviors can better coordinate the global and local optimization capabilities and escape from falling into the local optimum prematurely. The directly foraging formula is modified during the foraging phase. The food covariance learning strategy is utilized to enhance the population diversity and improve the convergence accuracy and convergence speed. Finally, the introduction of an optimal non-monopoly search strategy to perturb the optimal solution for updates improves the algorithm's ability to obtain a global best solution. We evaluated the effectiveness of IMCOA using the CEC2017 and CEC2022 test suites and compared it with eight algorithms. Experiments were conducted using different dimensions of CEC2017 and CEC2022 by performing numerical analyses, convergence analyses, stability analyses, Wilcoxon rank–sum tests and Friedman tests. Experiments on the CEC2017 and CEC2022 test suites show that IMCOA can strike a good balance between exploration and exploitation and outperforms the traditional COA and other optimization algorithms in terms of its convergence speed, optimization accuracy, and ability to avoid premature convergence. Statistical analysis shows that there is a significant difference between the performance of the IMCOA algorithm and other algorithms. Additionally, three engineering design optimization problems confirm the practicality of IMCOA and its potential to solve real-world problems. [ABSTRACT FROM AUTHOR]

COVID-19 infections are returning to many countries because of the emergence of variants or declining antibody levels provided by vaccines. An additional dose of vaccination is recommended to be a considerable supplementary intervention. We aim to explore public acceptance of the third dose of the COVID-19 vaccine and related influencing factors in China. This nationwide cross-sectional study was conducted in the general population among 31 provinces in November, 2021. We collected information on basic characteristics, vaccination knowledge and attitudes, and vaccine-related health beliefs of the participants. Univariable and multivariable logistic regression models were used to assess factors associated with the acceptance of a third COVID-19 vaccine. A total of 93.7% (95% CI: 92.9–94.6%) of 3119 Chinese residents were willing to receive a third dose of the COVID-19 vaccine. Individuals with low level of perceived susceptibility, perceived benefit, cues to action cues, and high level of perceived barriers, old age, low educational level, low monthly household income, and low knowledge score on COVID-19 were less likely to have the acceptance of a third dose of COVID-19 (all p < 0.05). In the multivariable logistic regression model, acceptance of the third dose of COVID-19 vaccine was mainly related to previous vaccination history [Sinopharm BBIP (aOR = 6.55, 95% CI 3.30–12.98), Sinovac (aOR = 5.22, 95% CI:2.72–10.02), Convidecia (aOR = 5.80, 95% CI: 2.04–16.48)], high level of perceived susceptibility (aOR = 2.48, 95% CI: 1.48–4.31) and high level of action cues (aOR = 23.66, 95% CI: 9.97–56.23). Overall, residents in China showed a high willingness to accept the third dose of COVID-19 vaccines, which can help vaccine manufacturers in China to manage the vaccine production and distribution for the huge domestic and international vaccine demand. Relevant institutions could increase people's willingness to booster shots by increasing initial COVID-19 vaccination rates, public's perception of COVID-19 susceptibility and cues to action through various strategies and channels. Meanwhile, it also has certain reference significance for other countries to formulate vaccine promotion strategies. [ABSTRACT FROM AUTHOR]

Influenza could circulate in parallel with COVID-19. In the context of COVID-19, some studies observed inverse associations between influenza vaccination and SARS-CoV-2 infection and clinical outcomes, while others did not. We conducted a meta-analysis to assess the association between influenza vaccination and SARS-CoV-2 infection and clinical outcomes, aiming to provide evidence for COVID-19 prevention and vaccination promotion. We searched four databases from inception to 10 March, 2021. Random effects and fixed effects models were used to pool odds ratios (ORs) and adjusted estimates with 95% confidence intervals (CIs). We used funnel plots to evaluate the publication bias, I2 statistics to evaluate the heterogeneity, and conducted subgroup analyses. Sixteen observational studies involving 290,327 participants were included. Influenza vaccination was associated with a lower risk of SARS-CoV-2 infection (pooled adjusted OR: 0.86, 95%CI: 0.81–0.91), while not significantly associated with adverse outcomes (intensive care: adjusted OR 0.63, 95%CI: 0.22–1.81; hospitalization: adjusted OR 0.74, 95%CI: 0.51–1.06; mortality: adjusted OR 0.89, 95%CI: 0.73–1.09). Our findings suggest that influenza vaccination is associated with a lower risk of SARS-CoV-2 infection. It is crucial for policy makers to implement strategies on influenza vaccination, for it may also have benefits for COVID-19 prevention. [ABSTRACT FROM AUTHOR]