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Since the outbreak of the SARS epidemic in 2003, the Chinese government has increased inputs to bolster the health care system. However, the COVID-19 pandemic has exposed the geographic maldistribution of health resources in China. We examine the spatial and temporal variation of the SARS epidemic using a difference-in-differences strategy. Our empirical results show that, compared with cities without SARS case reports, exogenous health shocks significantly increased the affected cities' medical resources supply. We provide multiple robustness tests to examine the validity of the main findings. Further study shows that the mechanism is because the SARS event increased the financial autonomy of the epidemic-affected cities, thus providing an incentive for local governments to increase health resources. Meanwhile, health shocks have little influence on the regions with only imported cases than the infected area. These findings provide a possible explanation for the inequality in the distribution of health resources.

Trip planning/recommendation is an important task for a plethora of applications in urban settings (e.g., tourism, transportation, social outings), relying on services provided by Location-Based Social Networks (LBSN). To provide greater context-awareness in trajectory planning, LBSNs combine historical trajectories of users for generating various hand-crafted features—e.g., geo-tags of photos taken by tourists and textual characteristics derived from reviews. Those features are used to learn tourists’ preferences, which are then used to generate a travel plan recommendation. However, many such features are extracted based on prior knowledge or empirical analysis specific to particular datasets, rendering the corresponding solutions not to be generalizable to diverse data sources. Thus, one important question for managing mobility is how to learn an accurate tour planning model based solely on POI visits or user check-ins and without the efforts of hand-crafted feature engineering. Inspired by recent successes of deep learning in sequence learning, we develop a solution to the tour planning problem based on the semi-supervised learning paradigm. An important aspect of our solution is that it does not involve any feature engineering. Specifically, we propose the Trip Recommendation method via trajectory Encoder and Decoder—a novel end-to-end approach encoding historical trajectories into vectors, while capturing both the intrinsic characteristics of individual POIs and the transition patterns among POIs. We also incorporate historical attention mechanism in our sequence-to-sequence trip recommendation task to improve the effectiveness. Experiments conducted on multiple publicly available LBSN datasets demonstrate significantly superior performance of our method.

Renal cell carcinoma (RCC) is a disease characterized by excessive administration complexity because it exhibits extraordinary nonuniformity among distinct molecular subtypes. We herein intended to delineate the metabolic aspects of clear cell RCC (ccRCC) in terms of the gene expression profile. Recent studies have revealed that metabolic variations within tumors are related to the responsiveness to immune checkpoint inhibitor (ICI) therapy and patient prognosis. We used 100 previously reported metabolic (MTB) pathways to quantify the metabolic landscape of the 729 ccRCC patients. Three MTB subtypes were established, and the MTB scores were calculated using principal component analysis (PCA). The high MTB score group had better overall survival (OS) and was associated with higher expression of immune-checkpoint and immune-activity signatures. The opposite was true of the low MTB score group, which may explain the poor prognosis of these patients. Three ICI-treated cohorts or tyrosine kinase inhibitor (TKI) treated cohort proved that patients with higher MTB scores exhibited notable therapeutic benefits and clinical gains. This research explained that the MTB score could be applied as a powerful prognostic indicator and predictive of ICI or TKI therapy. Assessing the MTB scores in a more extended group will facilitate our perception of tumor metabolism and provide guidance for studies on targeted approaches for ccRCC patients.