The global dynamic transmissibility of COVID-19 and its influencing factors: an analysis of control measures from 176 countries.

OBJECTIVE: To summarise the dynamic characteristics of COVID-19 transmissibility; To analyse and quantify the effect of control measures on controlling the transmissibility of COVID-19; To predict and compare the effectiveness of different control measures. METHODS: We used the basic reproduction number ([Formula: see text]) to measure the transmissibility of COVID-19, the transmissibility of COVID-19 and control measures of 176 countries and regions from January 1, 2020 to May 14, 2022 were included in the study. The dynamic characteristics of COVID-19 transmissibility were summarised through descriptive research and a Dynamic Bayesian Network (DBN) model was constructed to quantify the effect of control measures on controlling the transmissibility of COVID-19. RESULTS: The results show that the spatial transmissibility of COVID-19 is high in Asia, Europe and Africa, the temporal transmissibility of COVID-19 increases with the epidemic of Beta and Omicron strains. Dynamic Bayesian Network (DBN) model shows that the transmissibility of COVID-19 is negatively correlated with control measures. Restricting population mobility has the strongest effect, nucleic acid testing (NAT) has a strong effect, and vaccination has the weakest effect. CONCLUSION: Strict control measures are essential for controlling the COVID-19 outbreak; Restricting population mobility and nucleic acid testing (NAT) have significant impacts on controlling the COVID-19 transmissibility, while vaccination has no significant impact. In light of these findings, future control measures may include the widespread use of new NAT technology and the promotion of booster immunization.

The Temporal Lagged Relationship Between Meteorological Factors and Scrub Typhus With the Distributed Lag Non-linear Model in Rural Southwest China.

Background: Meteorological factors can affect the emergence of scrub typhus for a period lasting days to weeks after their occurrence. Furthermore, the relationship between meteorological factors and scrub typhus is complicated because of lagged and non-linear patterns. Investigating the lagged correlation patterns between meteorological variables and scrub typhus may promote an understanding of this association and be beneficial for preventing disease outbreaks. Methods: We extracted data on scrub typhus cases in rural areas of Panzhihua in Southwest China every week from 2008 to 2017 from the China Information System for Disease Control and Prevention. The distributed lag non-linear model (DLNM) was used to study the temporal lagged correlation between weekly meteorological factors and weekly scrub typhus. Results: There were obvious lagged associations between some weather factors (rainfall, relative humidity, and air temperature) and scrub typhus with the same overall effect trend, an inverse-U shape; moreover, different meteorological factors had different significant delayed contributions compared with reference values in many cases. In addition, at the same lag time, the relative risk increased with the increase of exposure level for all weather variables when presenting a positive association. Conclusions: The results found that different meteorological factors have different patterns and magnitudes for the lagged correlation between weather factors and scrub typhus. The lag shape and association for meteorological information is applicable for developing an early warning system for scrub typhus.

Influence of Workload, Personality, and Psychological Flexibility on Occupational Stress Among Medical Staff: A Fuzzy-Set Qualitative Comparative Analysis.

Objective: During the COVID-19 pandemic, the occupational stress of medical staff has been a major issue. This study aimed to suggest a new strategy to identify high-risk factor sets of occupational stress in medical staff using fuzzy-set qualitative comparative analysis (fs-QCA) and provide ideas for the prevention and intervention of occupational stress. Methods: A total of 1,928 medical staff members were surveyed and tested using the Acceptance and Action Questionnaire-II (AAQ-II), Occupational Stress Inventory-Revised edition (OSI-R), and Eysenck Personality Questionnaire-Revised Short Scale (EPQ-RSC). The fs-QCA was used to explore the high-risk factors for occupational stress among medical staff. Results: The psychological strain (PSY) score of the medical staff was 26.8 ± 7.13, and the physical strain (PHS) score was 24.3 ± 6.50. Low psychological flexibility score-introversion-high role overload, introversion-neuroticism-high role overload, and low psychological flexibility score-neuroticism were high-risk factor sets for PSY. Low psychological flexibility score-introversion-high role overload, low psychological flexibility score-introversion-neuroticism, low psychological flexibility score-neuroticism-high role overload, low psychological flexibility score-psychoticism-neuroticism, and psychoticism-neuroticism-high role overload were high-risk factor sets for PHS. Conclusion: There are different combinations of high-risk factors for occupational stress among the medical staff. For occupational stress intervention and psychological counseling, targeted and individualized health intervention measures should be implemented according to specific characteristic combinations of different individuals.

Virus-virus interactions of febrile respiratory syndrome among patients in China based on surveillance data from February 2011 to December 2020.

The burden of acute respiratory infections is still considerable, and virus-virus interactions may affect their epidemics, but previous evidence is inconclusive. To quantitatively investigate the interactions among respiratory viruses at both the population and individual levels, we use data from the pathogen surveillance for febrile respiratory syndrome (FRS) in China from February 2011 to December 2020. Cases tested for influenza virus (IV), respiratory syncytial virus (RSV), human parainfluenza virus (PIV), human Adenovirus (AdV), human coronavirus (CoV), human bocavirus (BoV), and rhinovirus (RV) were collected. We used spearman's rank correlation coefficients and binary logistic regression models to analyze the interactions between any two of the viruses at the population and individual levels, respectively. Among 120 237 cases, 4.5% were coinfected with two or more viruses. Correlation coefficients showed seven virus pairs were positively correlated, namely: IV and RSV, PIV and AdV, PIV and CoV, PIV and BoV, PIV and RV, AdV and BoV, and CoV and RV. Regression models showed positive interactions for all virus pairs, except for the negative interaction between IV and RV (odds ratio = 0.70, 95% confidence interval: 0.61-0.81). Most of the respiratory viruses interact positively, while IV and RV interact negatively.

Identifying the risk features for occupational stress in medical workers: a cross-sectional study.

OBJECTIVE: Occupational stress is considered a worldwide epidemic experienced by a large proportion of the working population. The identification of characteristics that place people at high risk for occupational stress is the basis of managing and intervening in this condition. In this study, we aimed to identify and validate the risk features for occupational stress among medical workers using a risk model and nomogram. METHODS: This cross-sectional study included 1988 eligible participants from Henan Province in China. Occupational stress and worker-occupation fit were measured with the Depression, Anxiety and Stress Scales (DASS-21) and Worker-Occupation Fit Inventory (WOFI). The identification of risk features was achieved through constructing multiple logistic regression model, and the risk features were used to develop the risk model and nomogram. Receiver operating characteristic (ROC) curves and calibration plots were generated to assess the effectiveness and calibration of the risk model. RESULTS: Among 1988 participants in our study, there were 42.5% (845/1988) medical workers experienced occupational stress. The risk features for occupational stress included poor work-occupation fit (WOF score < 25, expected risk: 77.3%), nurse population (expected risk: 63.1%), male sex (expected risk: 67.2%), work experience duration of 11-19 years (expected risk: 54.5%), experience of a traumatic event (expected risk: 65.3%) and the lack of a regular exercise habit (expected risk: 60.2%). For medical workers who have these risk features, the expected risk probability of occupational stress would be 90.2%. CONCLUSION: The current data can be used to identify medical workers at risk of developing occupational stress. Identifying risk features for occupational stress and the work-occupation fit can support hierarchical stress management in hospitals.