Integration of protein context improves protein- based COVID-19 patient stratification (preprint)

Background: Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating the four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19. Methods: A SWATH-based proteomic data set of 54 sera samples from 40 COVID-19 patients was employed as the training cohort. Results: Machine learning prioritized two complexes, one stoichiometric ratio, five pathways, twelve proteins and five network degrees. A model based on these 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP complex, the stoichiometric ratio of SAA2/ YLPM1, and the network extent of SIRT7 and A2M were highlighted in this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort and an independent SWATH-based proteomic data set from Germany, reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type. Conclusion: Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

The Demand for Social Interactions in Consumer Cities: Evidence from Restaurant Industry During the COVID-19 Pandemic (preprint)

We study the impact of the COVID-19 pandemic on restaurant visits in the post-lockdown era in the U.S., from the lens of social interactions. We construct a “social index” based on pre-pandemic mobile phone data to measure levels of social interactions that happened in different restaurants. We utilize a unique data structure of chain restaurants to disentangle restaurant attributes such as food and service types (which vary across chains) and the local market attributes such as local infection risk (which vary with the geographical location of each establishment). Our results suggest that chains with higher social indices experienced larger drops when local new cases increased in 2020, but when vaccination programs expanded in 2021, visits to these restaurants also rebounded faster. In addition, the demand for restaurants in urban centers with denser consumer amenities recovered faster than those in surrounding commuting areas. Our results provide evidence of a persisting demand for social interactions in consumer cities, and such demand has demonstrated its resilience when the economy started to recover from the COVID-19 pandemic.

Measuring daily-life fear perception change: a computational study in the context of COVID-19 (preprint)

COVID-19, as a global health crisis, has triggered the fear emotion with unprecedented intensity. Besides the fear of getting infected, the outbreak of COVID-19 also created significant disruptions in people's daily life and thus evoked intensive psychological responses indirect to COVID-19 infections. Here, we construct an expressed fear database using 16 million social media posts generated by 536 thousand users between January 1st, 2019 and August 31st, 2020 in China. We employ deep learning techniques to detect the fear emotion within each post and apply topic models to extract the central fear topics. Based on this database, we find that sleep disorders ("nightmare" and "insomnia") take up the largest share of fear-labeled posts in the pre-pandemic period (January 2019-December 2019), and significantly increase during the COVID-19. We identify health and work-related concerns are the two major sources of fear induced by the COVID-19. We also detect gender differences, with females generating more posts containing the daily-life fear sources during the COVID-19 period. This research adopts a data-driven approach to trace back public emotion, which can be used to complement traditional surveys to achieve real-time emotion monitoring to discern societal concerns and support policy decision-making.

Encouraging the resumption of economic activity after COVID-19: Evidence from a large scale field experiment in China (preprint)

As the COVID-19 pandemic comes to an end, governments find themselves facing a new challenge: motivating citizens to resume economic activity. What is an effective way to do so? We investigate this question using a field experiment in the city of Zhengzhou, China immediately following the end of the city's COVID-19 lockdown. Using self-reports and GPS trajectory data from participants' phones, we assessed the effect of providing information about the proportion of participants' neighbors who have resumed economic activity. We find that informing individuals about their neighbors' plans to visit restaurants increases the fraction of participants visiting restaurants by 12 percentage points (37%), amongst those participants who underestimated the proportion of neighbors who resumed economic activity. Those who overestimated did not respond by reducing restaurant attendance, so the intervention yielded no `boomerang' effect. We explore moderators, risk perceptions, and a placebo intervention for parks. All of these analyses suggest our intervention worked by reducing the perceived risk of going to restaurants.