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  1. Gao, Song (Ed.)
    The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people’s travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states. 
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  2. null (Ed.)
    Background Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). Objective Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). Methods We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. Results This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. Conclusions Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. International Registered Report Identifier (IRRID) DERR1-10.2196/24432 
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  3. Abstract

    Over the past two decades, scientific research on the connections between the health and resilience of marine ecosystems and human health, well‐being, and community prosperity has expanded and evolved into a distinct “metadiscipline” known as Oceans and Human Health (OHH), recognized by the scientific community as well as policy makers. OHH goals are diverse and seek to improve public health outcomes, promote sustainable use of aquatic systems and resources, and strengthen community resilience. OHH research has historically included some level of community outreach and partner involvement; however, the increasing disruption of aquatic environments and urgency of public health impacts calls for a more systematic approach to effectively identify and engage with community partners to achieve project goals and outcomes. Herein, we present a strategic framework developed collaboratively by community engagement personnel from the four recently established U.S. Centers for Oceans and Human Health (COHH). This framework supports researchers in defining levels of community engagement and in aligning partners, purpose, activities, and approaches intentionally in their community engagement efforts. Specifically, we describe: (a) a framework for a range of outreach and engagement approaches; (b) the need for identifying partners, purpose, activities, and approaches; and (c) the importance of making intentional alignment among them. Misalignment across these dimensions may lead to wasting time or resources, eroding public trust, or failing to achieve intended outcomes. We illustrate the framework with examples from current COHH case studies and conclude with future directions for strategic community engagement in OHH and other environmental health contexts.

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