Zoonotic spillover, which is the transmission of a pathogen from a vertebrate animal to a human, presents a global public health burden but is a poorly understood phenomenon. Zoonotic spillover requires several factors to align, including the ecological, epidemiological and behavioural determinants of pathogen exposure, and the within-human factors that affect susceptibility to infection. In this Opinion article, we propose a synthetic framework for animal-to-human transmission that integrates the relevant mechanisms. This framework reveals that all zoonotic pathogens must overcome a hierarchical series of barriers to cause spillover infections in humans. Understanding how these barriers are functionally and quantitatively linked, and how they interact in space and time, will substantially improve our ability to predict or prevent spillover events. This work provides a foundation for transdisciplinary investigation of spillover and synthetic theory on zoonotic transmission.

Effective mentoring is a key mechanism propelling successful research and academic careers, particularly for early career scholars. Most mentoring programs focus on models pairing senior and early career researchers, with limited focus on peer mentoring. Peer mentoring may be especially advantageous within emerging areas such as implementation science (IS) where challenges to traditional mentoring may be more prevalent. This special communication highlights the value of peer mentoring by describing a case study of an early career IS peer mentoring group. We delineate our curriculum and structure; support and processes; and products and outcomes. We highlight important group member characteristics to consider during group formation and continuation. The group's long-term (6 years) success was attributed to the balance of similarities and differences among group members. Members were in a similar career phase and used similar methodologies but studied different health topics at different institutions. Group members gave and received instrumental and psychosocial support and shared resources and knowledge. Peer mentoring can serve an important function to provide emotional, logistical, and professional development support for early career scholars. Our case study highlights strategies to foster peer mentoring groups that provide a generalizable blueprint and opportunity for improved outcomes for early career professionals.

Human computer interaction (HCI) and implementation science (IS) each have been applied to improve the adoption and delivery of innovative health interventions, and the two fields have complementary goals, foci, and methods. While the IS community increasingly draws on methods from HCI, there are many unrealized opportunities for HCI to draw from IS and to catalyze bidirectional collaborations. This workshop will explore similarities and differences between fields, with a goal of articulating a research agenda at their intersection.

U.S. health care systems are tasked with alleviating the burden of mental health, but are frequently underprepared and lack workforce and resource capacity to deliver services to all in need. Digital mental health interventions (DMHIs) can increase access to evidence-based mental health care. However, DMHIs commonly do not fit into the day-to-day activities of the people who engage with them, resulting in a research-to-practice gap for DMHI implementation. For health care settings, differences between digital and traditional mental health services make alignment and integration challenging. Specialized attention is needed to improve the implementation of DMHIs in health care settings so that these services yield high uptake, engagement, and sustainment. The purpose of this article is to enhance efforts to integrate DMHIs in health care settings by proposing implementation strategies, selected and operationalized based on the discrete strategies established in the Expert Recommendations for Implementing Change project, that align to DMHI-specific barriers in these settings. Guidance is offered in how these strategies can be applied to DMHI implementation across four phases commonly distinguished in implementation science using the Exploration, Preparation, Implementation, Sustainment Framework. Next steps to advance research in this area and improve the research-to-practice gap for implementing DMHIs are recommended. Applying implementation strategies to DMHI implementation will enable psychologists to systematically evaluate this process, which can yield an enhanced understanding of the factors that facilitate implementation success and improve the translation of DMHIs from controlled trials to real-world settings. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

As digital mental health interventions (DMHIs) proliferate, there is a growing need to understand the complexities of moving these tools from concept and design to service-ready products. We highlight five case studies from a center that specializes in the design and evaluation of digital mental health interventions to illustrate pragmatic approaches to the development of digital mental health interventions, and to make transparent some of the key decision points researchers encounter along the design-to-product pipeline. Case studies cover different key points in the design process and focus on partnership building, understanding the problem or opportunity, prototyping the product or service, and testing the product or service. We illustrate lessons learned and offer a series of questions researchers can use to navigate key decision points in the digital mental health intervention (DMHI) development process.

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.