The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible–exposed–infectious–removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What’s more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

This theoretical viewpoint paper presents a new perspective on urban governance in an information age. Smart city governance is not only about technology but also about reorganizing collaboration between a variety of actors. The introduction of new tools for open collaboration in the public domain is rapidly changing the way collaborative action is organized. These technologies reduce the transaction costs for massive collaboration dramatically and thus facilitate new forms of collaboration that we could call "open governance": new innovative forms of collective action aimed at solving complex public policy issues, contributing to public knowledge, or replacing traditional forms of public service provision. These innovative open and collaborative organizational forms in cities seem to point toward not only a wide variety of digitally connected actors but also to a fundamentally different and more invisible role of government in these arrangements. We argue that the recently emerging paradigm of New Public Governance (NPG) (Osborne, 2010) also fails to capture the dynamics of open governance since it does not acknowledge the emergent-pop-up-character of the new collaborations; neither does it present an understanding of massive individualized collaboration in cities. This paper aims to theoretically and empirically explore the core elements and the underlying socio-technical developments of this new Open Governance (OG) paradigm and compare and contrast OG with existing governance paradigms. Based on illustrative real-life cases, we will argue that we need a new paradigm that is better capable of explaining these emerging innovative forms of governing cities. We will argue that this requires an understanding of governance as a platform that facilitates an urban ecosystem. By connecting new insights from studies on digital governance to the debate about governance paradigms, this paper results in a set crucial empirical and normative questions about governance of cities and also in guidelines for urban governance that builds upon the rich, emerging interactions in cities that are facilitated by new technologies.

This paper studies conspiracy and debunking narratives about the origins of COVID-19 on a major Chinese social media platform, Weibo, from January to April 2020. Popular conspiracies about COVID-19 on Weibo, including that the virus is human-synthesized or a bioweapon, differ substan-tially from those in the United States. They attribute more responsibility to the United States than to China, especially following Sino-U.S. confrontations. Compared to conspiracy posts, debunking posts are associated with lower user participation but higher mobilization. Debunking narratives can be more engaging when they come from women and influencers and cite scientists. Our find-ings suggest that conspiracy narratives can carry highly cultural and political orientations. Correc-tion efforts should consider political motives and identify important stakeholders to reconstruct international dialogues toward intercultural understanding.

A prerequisite for the durability of authoritarian regimes as well as their effective governance is the regime’s ability to gather reliable information about the actions of lower-tier officials. Allowing public participation in the form of online complaints is one approach authoritarian regimes have taken to improve monitoring of lower-tier officials. In this paper, we gain rare access to internal communications between a monitoring agency and upper-level officials in China. We show that citizen grievances posted publicly online that contain complaints of corruption are systematically concealed from upper-level authorities when they implicate lower-tier officials or associates connected to lower-tier officials through patronage ties. Information manipulation occurs primarily through omission of wrongdoing rather than censorship or falsification, suggesting that even in the digital age, in a highly determined and capable regime where reports of corruption are actively and publicly voiced, monitoring the behavior of regime agents remains a challenge.

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