Restricting human mobility is an effective strategy used to control disease spread. However, whether mobility restriction is a proportional response to control the ongoing COVID-19 pandemic is unclear. We aimed to develop a model that can quantify the potential effects of various intracity mobility restrictions on the spread of COVID-19. In this modelling study, we used anonymous and aggregated mobile phone sightings data to build a susceptible–exposed–infectious–recovered transmission model for COVID-19 based on the city of Shenzhen, China. We simulated how disease spread changed when we varied the type and magnitude of mobility restrictions in different transmission scenarios, with variables such as the basic reproductive number (R0), length of infectious period, and the number of initial cases. 331 COVID-19 cases distributed across the ten regions of Shenzhen were reported on Feb 7, 2020. In our basic scenario (R0 of 2·68), mobility reduction of 20–60% within the city had a notable effect on controlling COVID-19 spread: a flattening of the peak number of cases by 33% (95% UI 21–42) and delay to the peak number by 2 weeks with a 20% restriction, 66% (48–75) reduction and 4 week delay with a 40% restriction, and 91% (79–95) reduction and 14 week delay with a 60% restriction. The effects of mobility restriction were increased when combined with reductions of 25% or 50% in transmissibility of the virus. In specific analyses of mobility restrictions for individuals with symptomatic infections and for high-risk regions, these measures also had substantial effects on reducing the spread of COVID-19. For example, the peak of the epidemic was delayed by 2 weeks if the proportion of individuals with symptomatic infections who could move freely was maintained at 20%, and by 4 weeks if two high-risk regions were locked down. The simulation results were also affected by various transmission parameters. Our model shows the effects of various types and magnitudes of mobility restrictions on controlling COVID-19 outbreaks at the city level in Shenzhen, China. The model could help policy makers to establish the optimal combinations of mobility restrictions during the COVID-19 pandemic, especially to assess the potential positive effects of mobility restriction on public health in view of the potential negative economic and societal effects. Guangdong Medical Science Fund, and National Natural Science Foundation of China.

Ensuring equitable access to health services is crucial for public welfare and social equity, and is a key objective of the United Nations’ Sustainable Development Goals (SDGs). However, existing datasets often define hospital accessibility using travel time to hospitals in geographic dimension only, without considering the supply (hospital capacity) and demand (population distribution) dynamics. To overcome this limitation, we developed and validated a national-scale 1 km map of both hospital travel time and hospital accessibility in China. We used the Gaussian two-step floating catchment area (Ga2SFCA) model to calculate hospital accessibility, incorporating hospital capacity and service population. Various file types and statistical indicators are provided, making the dataset highly accessible for non-specialists. The dataset fills the gap in publicly available nationwide hospital accessibility data for China and can serve as a critical tool in optimizing resource allocation and developing targeted strategies to improve healthcare equity.

Road traffic congestion has a large impact on travel. The accurate prediction of traffic congestion has become a hot topic in intelligent transportation systems (ITS). Recently, a variety of traffic congestion prediction methods have been proposed. However, most approaches focus on floating car data, and the prediction accuracy is often unstable due to large fluctuations in floating speed. Targeting these challenges, we propose a method of traffic congestion prediction based on bus driving time (TCP-DT) using long short-term memory (LSTM) technology. Firstly, we collected a total of 66,228 bus driving records from 50 buses for 66 working days in Guangzhou, China. Secondly, the actual and standard bus driving times were calculated by processing the buses’ GPS trajectories and bus station data. Congestion time is defined as the interval between actual and standard driving time. Thirdly, congestion time prediction based on LSTM (T-LSTM) was adopted to predict future bus congestion times. Finally, the congestion index and classification (CI-C) model was used to calculate the congestion indices and classify the level of congestion into five categories according to three classification methods. Our experimental results show that the T-LSTM model can effectively predict the congestion time of six road sections at different time periods, and the average mean absolute percentage error ( $\overline{MAPE}$ ) and root mean square error ( $\overline{RMSE}$ ) of prediction are 11.25% and 14.91 in the morning peak, and 12.3% and 14.57 in the evening peak, respectively. The TCP-DT method can effectively predict traffic congestion status and provide a driving route with the least congestion time for vehicles.

Inequalities in exposure to PM2.5 reflect disproportionate distribution of health risks across populations. This study assesses global PM2.5 exposure inequalities and associated trends since 2000. Results show that 118 countries reduced intra-country inequalities, but global disparities intensified due to widening gaps between developing and developed regions. By identifying pathways and country-specific contributions to global inequalities, we show that environmental policy shifts in major economies can rapidly reshape global inequality patterns.

The bus stop layout and route deployment may influence the efficiency of bus services. Evaluating the supply of bus service requires the consideration of demand from various urban activities, such as residential and job-related activities. Although various evaluation methods have been explored from different perspectives, it remains a challenging issue. This study proposes a spatial statistical approach by comparing the density of the potential demand and supply of bus services at bus stops. The potential demand takes jobs-housing locations into account, and the supply of bus services considers bus stops and their associated total number of daily bus arrivals. The kernel density estimation (KDE) and spatial autocorrelation analyses are employed to investigate the coupling relationship between the demand and supply densities at global and local scales. A coupling degree index (CDI) is constructed to standardize the measurement of demand-supply balance. A case study in Wuhan, China demonstrated that: (1) the spatial distribution of bus stops is reasonable at global level, (2) Seriously unbalanced locations for bus services have been detected at several stops. Related adjustments that can improve these defects are highly recommended.

PM 2.5 pollution poses significant health risks in China, with over one million premature deaths annually. Although major air quality regulations were enacted in 2013 and substantial progress has been made, the equitable distribution of health benefits remains uncertain. This study uses epidemiological modeling and inequality decomposition to assess PM 2.5-related health burden disparities in China from 2000 to 2019. Results show that premature deaths decreased by 85,500 (95% CI: 81,300-89,700) after 2013, with population growth, mortality rates, and aging contributing increases of 5.0%, 7.2%, and 8.3%, respectively, while PM 2.5 reduction led to a 25.1% decrease. However, the Gini coefficient for health burden disparities rose by 19.7%, indicating an increasing concentration of health risks in certain regions and populations. Provincial and urban disparities contributed 78.9% and 88.3% of this inequality. Targeted policy interventions are urgently needed to ensure that future air quality improvements yield more equitable health outcomes across China.