With advanced communication technologies, business managers can globally recruit talented members to form virtual teams and collaborate on innovative projects. While virtual teams enjoy superiority in their composition of talents, they also face more collaborative issues resulting from the diversity of members’ backgrounds and the limitations of communication technologies. These issues include task conflict, coordination delays, and reduced consensus in group discussions. Formal task interventions, such as imposed temporal coordination mechanisms, have been suggested to mitigate the severity of these collaborative issues in virtual teams. In this study, we aim to investigate the underlying mechanisms through which task interventions compensate the limitations of communication technologies and facilitate the exchange of individuals’ perspectives. By adopting the lens of structuration theory and focusing on task-oriented communication, we identify three patterns of taskoriented communication that are essential to the function of innovative virtual teams – the degree to which task-related issues are explored, the level of concern raising or attention switches, and the level of convergence on a common task view – as well as the structural properties of the team-task environment that influence the development of task-oriented communication. We hypothesize that task interventions establish or modify these structural properties of the team-task environment, which in turn shape virtual teams’ communication patterns. This study can provide a better understanding of how virtual teams learn to coordinate their task work more effectively by initiating task interventions. The insights gained in this study can suggest the management support that an organization can offer to facilitate the communication of its virtual teams.

Increasing global connectivity makes emerging infectious diseases (EID) more threatening than ever before. Various information systems (IS) projects have been undertaken to enhance public health capacity for detecting EID in a timely manner and disseminating important public health information to concerned parties. While those initiatives seemed to offer promising solutions, public health researchers and practitioners raised concerns about their overall effectiveness. In this paper, we argue that the concerns about current public health IS projects are partially rooted in the lack of a comprehensive framework that captures the complexity of EID management to inform and evaluate the development of public health IS. We leverage loose coupling to analyze news coverage and contact tracing data from 479 patients associated with the severe acute respiratory syndrome (SARS) outbreak in Taiwan. From this analysis, we develop a framework for outbreak management. Our proposed framework identifies two types of causal circles-coupling and decoupling circles-between the central public health administration and the local capacity for detecting unusual patient cases. These two circles are triggered by important information-centric activities in public health practices and can have significant influence on the effectiveness of EID management. We derive seven design guidelines from the framework and our analysis of the SARS outbreak in Taiwan to inform the development of public health IS. We leverage the guidelines to evaluate current public health initiatives. By doing so, we identify limitations of existing public health IS, highlight the direction future development should consider, and discuss implications for research and public health policy.

Contact tracing is an important control measure in the fight against infectious disease. Healthcare workers deduce potential disease pathways and propose corresponding containment strategies from collecting and reviewing patients' contact history. Social Network Analysis (SNA) provides healthcare workers with a network approach for integrating and analyzing all collected contact records via a simple network graph, called a contact network. Through SNA, they are able to identify prominent individuals in disease pathways as well as study the dynamics of disease transmission. In this chapter, we review the role of SNA in supplementing contact tracing and present a case study of the Taiwan SARS outbreak in 2003 to demonstrate the usefulness of geographical contacts in disease investigation.

In this project summary, we briefly present the disease monitoring and situational awareness infrastructure developed in BioPortal, our NSF-funded Infectious Disease Informatics project. We focus on two areas: social network analysis for Severe Acute Respiratory Syndrome (SARS) and global Foot-and-mouth disease (FMD) surveillance based on news monitoring. SARS is an example of a highly contagious disease that transmits via multiple vectors. By incorporating geographical contacts into social network analysis (SNA) and contact tracing, we are able to achieve a much higher connectivity than through contact tracing alone. FMD is a disease that wreaks havoc on farm animals and therefore on farm economy, and global FMD situational awareness is critical to protecting agronomy investments. By monitoring online news sources for FMD-related news and developing an accurate classification system, we can keep public health personnel apprised of outbreaks and potential outbreak situations. The results reported herein are based on collaborative efforts between the Artificial Intelligence (AI) Lab at the University of Arizona, the National Taiwan University, and the FMD Lab at University of California-Davis.

In epidemiology, contact tracing is a process to control the spread of an infectious disease and identify individuals who were previously exposed to patients with the disease. After the emergence of AIDS, Social Network Analysis (SNA) was demonstrated to be a good supplementary tool for contact tracing. Traditionally, social networks for disease investigations are constructed only with personal contacts. However, for diseases which transmit not only through personal contacts, incorporating geographical contacts into SNA has been demonstrated to reveal potential contacts among patients. In this research, we use Taiwan SARS data to investigate the differences in connectivity between personal and geographical contacts in the construction of social networks for these diseases. According to our results, geographical contacts, which increase the average degree of nodes from 0 to 108.62 and decrease the number of components from 961 to 82, provide much higher connectivity than personal contacts. Therefore, including geographical contacts is important to understand the underlying context of the transmission of these diseases. We further explore the differences in network topology between one-mode networks with only patients and multi-mode networks with patients and geographical locations for disease investigation. We find that including geographical locations as nodes in a social network provides a good way to see the role that those locations play in the disease transmission and reveal potential bridges among those geographical locations and households.