Effects of scheduled school breaks on the circulation of influenza in children, school-aged population, and adults in China: A spatio-temporal analysis.

OBJECTIVES: To investigate the effect of scheduled school break on the circulation of influenza in young children, school-aged population, and adults. METHODS: In a spatial-temporal analysis using influenza activity, school break dates, and meteorological covariates across mainland China during 2015-2018, we estimated age-specific, province-specific, and overall relative risk (RR) and effectiveness of school break on influenza. RESULTS: We included data in 24, 25, and 17 provinces for individuals aged 0-4 years, 5-19 years and 20+ years. We estimated a RR meta-estimate of 0.34 (95% confidence interval 0.29-0.40) and an effectiveness of 66% for school break in those aged 5-19 years. School break showed a lagged and smaller mitigation effect in those aged 0-4 years (RR meta-estimate: 0.73, 0.68-0.79) and 20+ years (RR meta-estimate: 0.89, 0.78-1.01) versus those aged 5-19 years. CONCLUSION: The results show heterogeneous effects of school break between population subgroups, a pattern likely to hold for other respiratory infectious diseases. Our study highlights the importance of anticipating age-specific effects of implementing school closure interventions and provides evidence for rational use of school closure interventions in future epidemics.

The prevalence of influenza bacterial co-infection and its role in disease severity: A systematic review and meta-analysis.

Background: Evidence suggests that influenza bacterial co-infection is associated with severe diseases, but this association has not been systematically assessed. We aimed to assess the prevalence of influenza bacterial co-infection and its role in disease severity. Methods: We searched PubMed and Web of Science for studies published between 1 January 2010 and 31 December 2021. We performed a generalised linear mixed effects model to estimate the prevalence of bacterial co-infection in influenza patients, and the odds ratios (OR) of death, intensive care unit (ICU) admission, and requirement of mechanical ventilation (MV) for influenza bacterial co-infection compared to influenza single-infection. Using the estimates of OR and prevalence, we estimated the proportion of influenza deaths attributable to bacterial co-infection. Results: We included 63 articles. The pooled prevalence of influenza bacterial co-infection was 20.3% (95% confidence interval (CI) = 16.0-25.4). Compared with influenza single-infection, bacterial co-infection increased the risk of death (OR = 2.55; 95% CI = 1.88-3.44), ICU admission (OR = 1.87; 95% CI = 1.04-3.38), and requirement for MV (OR = 1.78; 95% CI = 1.26-2.51). In the sensitivity analyses, we found broadly similar estimates between age groups, time periods, and health care settings. Likewise, while including studies with a low risk in confounding adjustment, the OR of death was 2.08 (95% CI = 1.44-3.00) for influenza bacterial co-infection. Based on these estimates, we found that approximately 23.8% (95% uncertainty range = 14.5-35.2) of influenza deaths were attributable to bacterial co-infection. Conclusions: We found that bacterial co-infection is associated with a higher risk of severe illnesses compared to influenza single-infection. Approximately one in four influenza deaths could be attributable to bacterial co-infection. These results should inform prevention, identification, and treatment for suspected bacterial co-infection in influenza patients. Registration: PROSPERO CRD42022314436.

COVID-19 spread prediction using socio-demographic and mobility-related data.

Studying the impacts of factors that may vary spatially and temporally as infectious disease progresses is critical for the prediction and intervention of COVID-19. This study aimed to quantitatively assess the spatiotemporal impacts of socio-demographic and mobility-related factors to predict the spread of COVID-19. We designed two different schemes that enhanced temporal and spatial features respectively, and both with the geographically and temporally weighted regression (GTWR) model adopted to consider the heterogeneity and non-stationarity problems, to reveal the spatiotemporal associations between the factors and the spread of COVID-19 pandemic. Results indicate that our two schemes are effective in facilitating the accuracy of predicting the spread of COVID-19. In particular, the temporally enhanced scheme quantifies the impacts of the factors on the temporal spreading trend of the epidemic at the city level. Simultaneously, the spatially enhanced scheme figures out how the spatial variances of the factors determine the spatial distribution of the COVID-19 cases among districts, particularly between the urban area and the surrounding suburbs. Findings provide potential policy implications in terms of dynamic and adaptive anti-epidemic.

Assessment of community vulnerability during the COVID-19 pandemic: Hong Kong as a case study.

The COVID-19 pandemic continues to threaten global public health. Reliable assessment of community vulnerability is therefore essential to fighting and mitigating the pandemic. This study presents a framework that considers the roles of internal and external factors, including the components of social vulnerability, exposure, and sensitivity, to comprehensively and accurately assess community vulnerability to the pandemic. With respect to internal factors, we summarized the inherent social characteristics of people groups using census data and explored the roles of both overall and four major thematic social vulnerabilities in shaping community infection by COVID-19. We then designed two external factors to characterize exposure and sensitivity and implemented an aggregation by multiplying them with the internal social vulnerability to achieve a comprehensive vulnerability assessment. The role of the estimated vulnerability in shaping community infection was evaluated by statistical and spatial analysis as well as by risk factor classification using defined rules. This case study of Hong Kong demonstrated the value of our framework in vulnerability assessment and revealed the role of vulnerability in shaping community infection by COVID-19.