2020 was an unprecedented year, with rapid and drastic changes in human mobility due to the COVID-19 pandemic. To understand the variation in commuting patterns among the Chinese population across stable and unstable periods, we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020, ranging from the Lunar New Year travel season (chunyun), to the exceptional calm of COVID-19 lockdowns, and then to the recovery periods. We observed that cross-city movements, which increased substantially in chunyun and then dropped sharply during the lockdown, are primarily dependent on travel distance and the socio-economic development of cities. Following the Lunar New Year holiday, national mobility remained low until mid-February, and COVID-19 interventions delayed more than 72.89 million people returning to large cities. Mobility network analysis revealed clusters of highly connected cities, conforming to the social-economic division of urban agglomerations in China. While the mass migration back to large cities was delayed, smaller cities connected more densely to form new clusters. During the recovery period after travel restrictions were lifted, the net flows of over 55% city pairs reversed in direction compared to before the lockdown. These findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public health emergency response, transportation planning, and regional economic development, among others.

Hidden populations, such as injecting drug users (IDUs), sex workers (SWs) and men who have sex with men (MSM), are considered at high risk of contracting and transmitting infectious diseases such as AIDS, gonorrhea, syphilis etc. However, public health interventions to such groups are prohibited due to strong privacy concerns and lack of global information, which is a necessity for traditional strategies such as targeted immunization and acquaintance immunization. In this study, we introduce an innovative intervention strategy to be used in combination with a sampling approach that is widely used for hidden populations, Respondent-driven Sampling (RDS). The RDS strategy is implemented in two steps: First, RDS is used to estimate the average degree (personal network size) and degree distribution of the target population with sample data. Second, a cut-off threshold is calculated and used to screen the respondents to be immunized. Simulations on model networks and real-world networks reveal that the efficiency of the RDS strategy is close to that of the targeted strategy. As the new strategy can be implemented with the RDS sampling process, it provides a cost-efficient and feasible approach for disease intervention and control for hidden populations.

Aviation transportation systems have developed rapidly in recent years and have become a focus for research on the modeling of epidemics. However, despite the number of studies on aggregated topological structures and their effects on the spread of disease, the temporal sequence of flights that connect different airports have not been examined. In this study, to analyze the temporal pattern of the Chinese Aviation Network (CAN), we obtain a time series of topological statistics through sliding the temporal CAN with an hourly time window. In addition, we build two types of Susceptible-Infectious (SI) spreading models to study the effects of linking sequence and temporal duration on the spread of diseases. The results reveal that the absence of links formed by flights without alternatives at dawn and night causes a significant decrease in the centralization of the network. The temporal sparsity of linking sequence slows down the spread of disease on CAN, and the duration of flights intensifies the sensitiveness of CAN to targeted infection. The results are of great significance for further understanding of the aviation network and the dynamic process, such as the propagation of delay.

Misreporting is a common source of bias in population surveys involving sensitive topics such as sexual behaviours, abortion or criminal activity. To protect their privacy due to stigmatized or illegal behaviour, respondents tend to avoid fully disclosure of personal information deemed sensitive. This attitude however may compromise the results of survey studies. To circumvent this limitation, this article proposes a novel ego-centric sampling method (ECM) based on the respondent’s peer networks to make indirect inferences on sensitive traits anonymously. Other than asking the respondents to report directly on their own behaviour, ECM takes into account the knowledge the respondents have about their social contacts in the target population. By using various scenarios and sensitive analysis on model and real populations, we show the high performance, that is low biases, that can be achieved using our method and the novel estimator. The method is also applied on a real-world survey to study traits of college students. This real-world exercise illustrates that the method is easy-to-implement, requiring few amendments to standard sampling protocols, and provides a high level of confidence on privacy among respondents. The exercise revealed that students tend to under-report their own sensitive and stigmatized traits, such as their sexual orientation. Little or no difference was observed in reporting non-sensitive traits. Altogether, our results indicate that ECM is a promising method able to encourage survey participation and reduce bias due to misreporting of sensitive traits through indirect and anonymous data collection.

We define metrics to quantify the level of overall delay and propose an agent-based data-driven model with four factors, including aircraft rotation, flight connectivity, scheduling process, and disturbance, to build a simulator for reproducing the delay propagation in aviation networks. We then measure the impact on the propagation by the delay at each airport and analyze the relevance to its temporal characteristics. When delay occurs, airline schedule planning may become infeasible, and rescheduling of flights is usually required to maintain the function of the system, so we then develop an improved genetic algorithm (GA) to reschedule flights and to relax the root delay. Results indicate that priority-based strategy rather than First-Come-First-Serve can achieve minimum overall delay when congestion occurs, and aircraft rotation is the most important internal factor contributing to delay propagation. Furthermore, the reschedule generated by the improved GA can decrease delay propagation more significantly compared to the agent-based model.