Social contagion is a key mechanism that shapes health behaviors, but few studies have applied this approach at the regional level to examine how vaccination beliefs and rates vary and diffuse across geographic areas. Through modifying the traditional SIR model, this paper addresses this gap by applying social network theory to a new compartmental model to simulate regional contagion in COVID-19 vaccination rates in England, using panel data of new and accumulated vaccination numbers from December 2020 to June 2022. The model estimates each region's initial and changing vaccination beliefs and their mutual influence on each other. The results reveal that Southeastern regions in England had higher initial vaccination beliefs and stronger spillover effects on other regions than northwestern regions. The paper suggests that policies to increase vaccination rates should consider the heterogeneity and peer effects among regions and other factors that may affect vaccination beliefs. The paper also discusses the limitations of the network model and directions for future research.

The disruption of China's care systems during and immediately post-pandemic is not unique in the global context. Most countries continue to experience significant health systems challenges. However, the size of China's population and its ageing demographic, makes it a particular health system challenge from which important global lessons might be learned. China's case evidences the consequences of lack of integration of primary care practitioners into high-level decision-making, and the lack of investment in primary healthcare. The government must allow for transparent and meaningful integration of evidence and knowledge from researchers, healthcare providers, and practitioners at different tiers in the healthcare system when designing policies. In this way, the government can better allocate scarce medical resources for testing, medical treatment, and vaccination such that the healthcare system's capacity to face the healthcare challenges of COVID-19 and future pandemics can be improved. Payment systems should encourage the use of cost-effective primary care and primary care funds must be protected from the risk of reallocation into secondary and tertiary care - even in times of crisis. There is an urgent need for the Chinese government to rethink the payment mechanism and the allocation of medical resources, thus avoiding overcrowding at tertiary hospitals, enhancing primary healthcare capacity, and utilising the perspectives of grassroots-level health workers and researchers who better understand the needs of the population.

Despite the prevalence of smoking cessation programs and public health campaigns, individuals with long-term illness, disability, and infirmity have been found to smoke more often than those without such conditions, leading to worsening health. However, the available literature has mainly focused on the association between long-term illness and smoking, which might suffer from the possible bidirectional influence, while few studies have examined the potential causal effect of long-term illness on smoking. This gap in knowledge can be addressed using an instrumental variable analysis that uses a third variable as an instrument between the endogenous independent and dependent variable and allows the identification of the direction of causality under the discussed assumptions. Our study analyses the UK General Household Survey in 2006, covering a nationally representative 13585 households. We exploited the number of vehicles as the instrumental variable for long-term illness, disability, and infirmity as vehicle numbers may be related to illness based on the notion that these individuals are less likely to drive, but that vehicle number may have no relationship to the likelihood of smoking. Our results suggested that chronic illness status causes a significantly 98% higher probability of smoking. The findings have wide implications for public health policymakers to design a more accessible campaign around smoking and for psychologists and doctors to take targeted care for the welfare of individuals with long-term illnesses.--- Awarded the 2022 Inter-University Big Data Challenge Finalist & Winner for "Research Solution Award ($600)"

Given the prevalence of depressive mental health symptoms among Chinese adults of grandpar-enting age in recent decades, a better understanding of how the depression and life satisfaction among mid-aged and older adults in China are affected by their role as grandparents is called for. This study examines the relationship between grandparenting and depression and life satisfaction among Chinese adults using multilevel regression models based on a multilevel matching dataset formulated from the 2018 China Health and Retirement Longitudinal Study (CHARLS) and the 2018 China City Statistical Yearbook. The results show that for adults who take care of their grandchildren, living with their children can significantly reduce depression. Meanwhile, whereas spending more time taking care of grandchildren can lower life satisfaction, taking care of more grandchildren is related to higher life satisfaction. The findings of this study should help policymakers improve the quality of life of Chinese adults through better-targeted approaches.

A year following the initial COVID-19 outbreak in China, many countries have approved emergency vaccines. Public-health practitioners and policymakers must understand the predicted populational willingness for vaccines and implement relevant stimulation measures. This study developed a framework for predicting vaccination uptake rate based on traditional clinical data - involving an autoregressive model with autoregressive integrated moving average (ARIMA) - and innovative web search queries - involving a linear regression with ordinary least squares/least absolute shrinkage and selection operator, and machine-learning with boost and random forest. For accuracy, we implemented a stacking regression for the clinical data and web search queries. The stacked regression of ARIMA (1,0,8) for clinical data and boost with support vector machine for web data formed the best model for forecasting vaccination speed in the US. The stacked regression provideda more accurate forecast. These results can help governments and policymakers predict vaccine demand and finance relevant programs.