Mosquitoes are the deadliest animal in the word, transmitting a variety of insect-borne infectious diseases, such as malaria, dengue fever, yellow fever, and Zika, causing more deaths than any other vector-borne pathogen. Moreover, in the absence of effective drugs and vaccines to prevent and treat insect-borne diseases, mosquito control is particularly important as the primary measure. In recent decades, due to the gradual increase in mosquito resistance, increasing attention has fallen on the mechanisms and effects associated with pathogen infection. This review provides an overview of mosquito innate immune mechanisms in terms of physical and physiological barriers, pattern recognition receptors, signalling pathways, and cellular and humoral immunity, as well as the antipathogenic effects of mosquito symbiotic bacteria. This review contributes to an in-depth understanding of the interaction process between mosquitoes and pathogens and provides a theoretical basis for biological defence strategies against mosquito-borne infectious diseases. Graphical Abstract

The ongoing emergence of SARS-CoV-2 variants poses challenges to the immunity induced by infections and vaccination. We conduct a 6-month longitudinal evaluation of antibody binding and neutralization of sera from individuals with six different combinations of vaccination and infection against BA.5, XBB.1.5, EG.5.1, and BA.2.86. We find that most individuals produce spike-binding IgG or neutralizing antibodies against BA.5, XBB.1.5, EG.5.1, and BA.2.86 2 months after infection or vaccination. However, compared to ancestral strain and BA.5 variant, XBB.1.5, EG.5.1, and BA.2.86 exhibit comparable but significant immune evasion. The spike-binding IgG and neutralizing antibody titers decrease in individuals without additional antigen exposure, and <50% of individuals neutralize XBB.1.5, EG.5.1, and BA.2.86 during the 6-month follow-up. Approximately 57% of the 107 followed up individuals experienced an additional infection, leading to improved binding IgG and neutralizing antibody levels against these variants. These findings provide insights into the impact of SARS-CoV-2 variants on immunity following repeated exposure.

Background: Predicting vaccination behaviors accurately could provide insights for health care professionals to develop targeted interventions. Objective: The aim of this study was to develop predictive models for influenza vaccination behavior among children in China. Methods: We obtained data from a prospective observational study in Wuxi, eastern China. The predicted outcome was individual-level vaccine uptake and covariates included sociodemographics of the child and parent, parental vaccine hesitancy, perceptions of convenience to the clinic, satisfaction with clinic services, and willingness to vaccinate. Bayesian networks, logistic regression, least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), naive Bayes (NB), random forest (RF), and decision tree classifiers were used to construct prediction models. Various performance metrics, including area under the receiver operating characteristic curve (AUC), were used to evaluate the predictive performance of the different models. Receiver operating characteristic curves and calibration plots were used to assess model performance. Results: A total of 2383 participants were included in the study; 83.2% of these children (n=1982) were <5 years old and 6.6% (n=158) had previously received an influenza vaccine. More than half (1356/2383, 56.9%) the parents indicated a willingness to vaccinate their child against influenza. Among the 2383 children, 26.3% (n=627) received influenza vaccination during the 2020-2021 season. Within the training set, the RF model showed the best performance across all metrics. In the validation set, the logistic regression model and NB model had the highest AUC values; the SVM model had the highest precision; the NB model had the highest recall; and the logistic regression model had the highest accuracy, F1 score, and Cohen κ value. The LASSO and logistic regression models were well-calibrated. Conclusions: The developed prediction model can be used to quantify the uptake of seasonal influenza vaccination for children in China. The stepwise logistic regression model may be better suited for prediction purposes.

BACKGROUND Predicting vaccination behaviors accurately could provide insights for health care professionals to develop targeted interventions. OBJECTIVE The aim of this study was to develop predictive models for influenza vaccination behavior among children in China. METHODS We obtained data from a prospective observational study in Wuxi, eastern China. The predicted outcome was individual-level vaccine uptake and covariates included sociodemographics of the child and parent, parental vaccine hesitancy, perceptions of convenience to the clinic, satisfaction with clinic services, and willingness to vaccinate. Bayesian networks, logistic regression, least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), naive Bayes (NB), random forest (RF), and decision tree classifiers were used to construct prediction models. Various performance metrics, including area under the receiver operating characteristic curve (AUC), were used to evaluate the predictive performance of the different models. Receiver operating characteristic curves and calibration plots were used to assess model performance. RESULTS A total of 2383 participants were included in the study; 83.2% of these children (n=1982) were <5 years old and 6.6% (n=158) had previously received an influenza vaccine. More than half (1356/2383, 56.9%) the parents indicated a willingness to vaccinate their child against influenza. Among the 2383 children, 26.3% (n=627) received influenza vaccination during the 2020-2021 season. Within the training set, the RF model showed the best performance across all metrics. In the validation set, the logistic regression model and NB model had the highest AUC values; the SVM model had the highest precision; the NB model had the highest recall; and the logistic regression model had the highest accuracy, F1 score, and Cohen κ value. The LASSO and logistic regression models were well-calibrated. CONCLUSIONS The developed prediction model can be used to quantify the uptake of seasonal influenza vaccination for children in China. The stepwise logistic regression model may be better suited for prediction purposes.