Impact of Social Media on Travel Behaviors during the COVID-19 Pandemic: Evidence from New York City

During the outbreak of COVID-19, people's reliance on social media for pandemic-related information exchange, daily communications, and online professional interactions increased because of self-isolation and lockdown implementation. Most of the published research addresses the performance of nonpharmaceutical interventions (NPIs) and measures on the issues impacted by COVID-19, such as health, education, and public safety;however, not much is known about the interplay between social media use and travel behaviors. This study aims to determine the effect of social media on human mobility before and after the COVID-19 outbreak, and its impact on personal vehicle and public transit use in New York City (NYC). Apple mobility trends and Twitter data are used as two data sources. The results indicate that Twitter volume and mobility trend correlations are negative for both driving and transit categories in general, especially at the beginning of the COVID-19 outbreak in NYC. A significant time lag (13 days) between the online communication rise and mobility drop can be observed, thereby providing evidence of social networks taking quicker reactions to the pandemic than the transportation system. In addition, social media and government policies had different impacts on vehicular traffic and public transit ridership during the pandemic with varied performance. This study provides insights on the complex influence of both anti-pandemic measures and user-generated content, namely social media, on people's travel decisions during pandemics. The empirical evidence can help decision-makers formulate timely emergency responses, prepare targeted traffic intervention policies, and conduct risk management in similar outbreaks in the future.

Impact of Social Media on Travel Behaviors during the COVID-19 Pandemic: Evidence from New York City.

During the outbreak of COVID-19, people's reliance on social media for pandemic-related information exchange, daily communications, and online professional interactions increased because of self-isolation and lockdown implementation. Most of the published research addresses the performance of nonpharmaceutical interventions (NPIs) and measures on the issues impacted by COVID-19, such as health, education, and public safety; however, not much is known about the interplay between social media use and travel behaviors. This study aims to determine the effect of social media on human mobility before and after the COVID-19 outbreak, and its impact on personal vehicle and public transit use in New York City (NYC). Apple mobility trends and Twitter data are used as two data sources. The results indicate that Twitter volume and mobility trend correlations are negative for both driving and transit categories in general, especially at the beginning of the COVID-19 outbreak in NYC. A significant time lag (13 days) between the online communication rise and mobility drop can be observed, thereby providing evidence of social networks taking quicker reactions to the pandemic than the transportation system. In addition, social media and government policies had different impacts on vehicular traffic and public transit ridership during the pandemic with varied performance. This study provides insights on the complex influence of both anti-pandemic measures and user-generated content, namely social media, on people's travel decisions during pandemics. The empirical evidence can help decision-makers formulate timely emergency responses, prepare targeted traffic intervention policies, and conduct risk management in similar outbreaks in the future.

A social media Data-Driven analysis for transport policy response to the COVID-19 pandemic outbreak in Wuhan, China.

Non-pharmacological interventions (NPI) such as social distancing and lockdown are essential in preventing and controlling emerging pandemic outbreaks. Many countries worldwide implemented lockdowns during the COVID-19 outbreaks. However, due to the lack of prior experience and knowledge about the pandemic, it is challenging to deal with short-term polices decision-making due to the highly stochastic and dynamic nature of the COVID-19. Thus, there is a need for the exploration of policy decision analysis to help agencies to adjust their current policies and adopt quickly. In this study, an analytical methodology is developed to analysis urban transport policy response for pandemic control based on social media data. Compared to traditional surveys or interviews, social media can provide timely data based on the feedback from public in terms of public demands, opinions, and acceptance of policy implementations. In particular, a sentiment-aware pre-trained language model is fine-tuned for sentiment analysis of policy. The Latent Dirichlet Allocation (LDA) model is used to classify documents, e.g., posts collected from social media, into specific topics in an unsupervised manner. Then, entropy weights method (EWM) is used to extract public policy demands based on the classified topics. Meanwhile, a Jaccard distance-based approach is proposed to conduct the response analysis of policy adjustments. A retrospective analysis of transport policies during the COVID-19 pandemic in Wuhan, China is presented using the developed methodology. The results show that the developed policymaking support methodology can be an effective tool to evaluate the acceptance of anti-pandemic policies from the public's perspective, to assess the balance between policies and people's demands, and to further perform the response analysis of a series of policy adjustments based on online feedback.

Towards a global One Health index: a potential assessment tool for One Health performance.

BACKGROUND: A One Health approach has been increasingly mainstreamed by the international community, as it provides for holistic thinking in recognizing the close links and inter-dependence of the health of humans, animals and the environment. However, the dearth of real-world evidence has hampered application of a One Health approach in shaping policies and practice. This study proposes the development of a potential evaluation tool for One Health performance, in order to contribute to the scientific measurement of One Health approach and the identification of gaps where One Health capacity building is most urgently needed. METHODS: We describe five steps towards a global One Health index (GOHI), including (i) framework formulation; (ii) indicator selection; (iii) database building; (iv) weight determination; and (v) GOHI scores calculation. A cell-like framework for GOHI is proposed, which comprises an external drivers index (EDI), an intrinsic drivers index (IDI) and a core drivers index (CDI). We construct the indicator scheme for GOHI based on this framework after multiple rounds of panel discussions with our expert advisory committee. A fuzzy analytical hierarchy process is adopted to determine the weights for each of the indicators. RESULTS: The weighted indicator scheme of GOHI comprises three first-level indicators, 13 second-level indicators, and 57 third-level indicators. According to the pilot analysis based on the data from more than 200 countries/territories the GOHI scores overall are far from ideal (the highest score of 65.0 out of a maximum score of 100), and we found considerable variations among different countries/territories (31.8-65.0). The results from the pilot analysis are consistent with the results from a literature review, which suggests that a GOHI as a potential tool for the assessment of One Health performance might be feasible. CONCLUSIONS: GOHI-subject to rigorous validation-would represent the world's first evaluation tool that constructs the conceptual framework from a holistic perspective of One Health. Future application of GOHI might promote a common understanding of a strong One Health approach and provide reference for promoting effective measures to strengthen One Health capacity building. With further adaptations under various scenarios, GOHI, along with its technical protocols and databases, will be updated regularly to address current technical limitations, and capture new knowledge.

Estimating the Economic Effects of the Early Covid-19 Emergency Response in Cities Using Intracity Travel Intensity Data

In the early days of the Covid-19 pandemic, China implemented the most stringent and serious emergency response. To understand the effect of such an emergency response strategy on the economic system, this study proposed a simultaneous overall estimation method using intracity travel intensity data. The overall effect is represented by the difference between intracity travel intensity with and without the emergency response. Using historical data and time series analysis, we compared intracity travel intensity post China’s implementation of the emergency response with predicted intracity travel intensity without such a response. The loss rates, defined by the proportion of intracity travel intensity loss, were calculated for 360 cities within 33 provincial-level regions in China based on data availability. We found that 30 days after the emergency response, 21% of the cities saw over 80% recovery and 10% of the cities showed more than 90% recovery;45 days after the emergency response, more than 83% of the 360 cities witnessed 80% recovery. The correlation between gross domestic production loss rate and travel intensity loss rate was studied quantitatively to demonstrate the representativeness of the intracity travel intensity loss rate. This indicator was also used to analyze the spatial and temporal patterns of the effects on the economy. The results of this study can help us understand the economic effects caused by the early Covid-19 emergency response and the method can be a reference for fast and real-time economic loss estimation to support emergency response decision making under pandemic conditions.

Experience for Glaucoma Patients During the COVID-19 Outbreak (preprint)

Purpose: To reviewed our measures and other experiences associated with the treatment of glaucoma patients during the 2019 novel coronavirus disease (COVID-19) outbreak . Methods: : Measures consistent with the clinical situation and the type of glaucoma were implemented in each case. Results: : Consultation for treatment was provided through a WeChat glaucoma patient group. In our survey, 58.5%patients indicated an increase in negative emotions during the outbreak, and psychological counseling was provided frequently. Difficulties in purchasing eye drops were reported by 79.2% of glaucoma patients, so an online medicine supply system was set up and eyedrops from the pharmacy or hospital pharmacy were mailed to the patients. Phacoemulsification with goniosynechialysis was performed in7acute primary angle-closure glaucomapatients (11eyes) who experienced sustained high IOP(onset time, 12-65d). The IOP decreased significantly after the operation(t=7.895，P<0.01) without showing serious complications or infections. In addition, lectureson glaucoma prevention were promoted online. Conclusion: Effective measures can relieve thepsychological pressure and intraocular symptoms in glaucoma patientsin epidemic outbreak areas.

Orthogonal genome-wide screens of bat cells identify MTHFD1 as a target of broad antiviral therapy.

Bats are responsible for the zoonotic transmission of several major viral diseases, including those leading to the 2003 SARS outbreak and likely the ongoing COVID-19 pandemic. While comparative genomics studies have revealed characteristic adaptations of the bat innate immune system, functional genomic studies are urgently needed to provide a foundation for the molecular dissection of the viral tolerance in bats. Here we report the establishment of genome-wide RNA interference (RNAi) and CRISPR libraries for the screening of the model megabat, Pteropus alecto. We used the complementary RNAi and CRISPR libraries to interrogate P. alecto cells for infection with two different viruses: mumps virus and influenza A virus, respectively. Independent screening results converged on the endocytosis pathway and the protein secretory pathway as required for both viral infections. Additionally, we revealed a general dependence of the C1-tetrahydrofolate synthase gene, MTHFD1, for viral replication in bat cells and human cells. The MTHFD1 inhibitor, carolacton, potently blocked replication of several RNA viruses, including SARS-CoV-2. We also discovered that bats have lower expression levels of MTHFD1 than humans. Our studies provide a resource for systematic inquiry into the genetic underpinnings of bat biology and a potential target for developing broad-spectrum antiviral therapy.

Impact of Social Media Use on Travel Behavior during COVID19 Outbreak: Evidence from New York City

Understanding the impact of COVID-19 on human mobility has now become one of the most important research questions after the nation-wide impacts of the pandemic have become prominent. Some recent studies address the effect of governmental policies or orders on issues impacted by COVID-19 including health, education, public safety, and, mobility. However, not much is known about the public concerns on social media platforms related to COVID-19 and its impact on people’s travel behaviors. This study investigates social media use and impact on traveling before and after the outbreak of the pandemic. New York City is selected as the case study site for studying the interplay between mobility trends and Twitter data. Results show that social media use is negatively correlated with mobility trends for both driving and transit use, and a significant time lag between tweets and mobility trends were found. Moreover, it is found that different influence mechanisms are resulting from user-generated content and governments/healthcare organizations' actions, both affecting people’s mode choice preferences for vehicle and transit use. This study provides insights into the impacts of COVID-19 by improving the authors' understanding of the complexities of travel behavior in the new information ecosystem due to both traditional sources of information and user-generated content. The findings of this paper can be used to support effective decision-making in response to any similar major disruptive events in the future.

Atrial Arrhythmias in Patients with Severe COVID-19.

The number of confirmed COVID-19 cases has increased drastically; however, information regarding the impact of this disease on the occurrence of arrhythmias is scarce. The aim of this study was to determine the impact of COVID-19 on arrhythmia occurrence. This prospective study included patients with COVID-19 treated at the Leishenshan Temporary Hospital of Wuhan City, China, from February 24 to April 5, 2020. Demographic, comorbidity, and arrhythmias data were collected from patients with COVID-19 (n = 84) and compared with control data from patients with bacterial pneumonia (n = 84) infection. Furthermore, comparisons were made between patients with severe and nonsevere COVID-19 and between older and younger patients. Compared with patients with bacterial pneumonia, those with COVID-19 had higher total, mean, and minimum heart rates (all P < 0.01). Patients with severe COVID-19 (severe and critical type diseases) developed more atrial arrhythmias compared with those with nonsevere symptoms. Plasma creatine kinase isoenzyme (CKMB) levels (P=0.01) were higher in the severe group than in the nonsevere group, and there were more deaths in the severe group than in the nonsevere group (6 (15%) vs. 3 (2.30%); P=0.05). Premature atrial contractions (PAC) and nonsustained atrial tachycardia (NSAT) were significantly positively correlated with plasma CKMB levels but not with high-sensitive cardiac troponin I or myoglobin levels. Our data demonstrate that COVID-19 patients have higher total, mean, and minimum heart rates compared with those with bacterial pneumonia. Patients with severe or critical disease had more frequent atrial arrhythmias (including PAC and AF) and higher CKMB levels and mortality than those with nonsevere symptoms.

Examining Public Concerns and Attitudes toward Unfair Events Involving Elderly Travelers during the COVID-19 Pandemic Using Weibo Data.

The Chinese government has launched a digital health code system to detect people potentially exposed to the coronavirus 2019 (COVID-19) disease and to curb its spread. Citizens are required to show the health code on their smartphones when using public transport. However, many seniors are not allowed to use public transport due to their difficulties in obtaining health codes, leading to widespread debates about these unfair events. Traditionally, public perceptions and attitudes toward such unfair events are investigated using analytical methods based on interviews or questionnaires. This study crawled seven-month messages from Sina Weibo, the Chinese version of Twitter, and developed a hybrid approach integrating term-frequency-inverse-document-frequency, latent Dirichlet allocation, and sentiment classification. Results indicate that a rumor about the unfair treatment of elderly travelers triggered public concerns. Primary subjects of concern were the status quo of elderly travelers, the provision of transport services, and unfair event descriptions. Following the government's responses, people still had negative attitudes toward transport services, while they became more positive about the status quo of elderly travelers. These findings will guide government authorities to explore new forms of automated social control and to improve transport policies in terms of equity and fairness in future pandemics.