Human factors, including contact structure, movement, impact on the environment and patterns of behaviour, can have significant influence on the emergence of novel infectious diseases and the transmission and amplification of established ones. As anthropogenic climate change alters natural systems and global economic forces drive land-use and land-cover change, it becomes increasingly important to understand both the ecological and social factors that impact infectious disease outcomes for human populations. While the field of disease ecology explicitly studies the ecological aspects of infectious disease transmission, the effects of the social context on zoonotic pathogen spillover and subsequent human-to-human transmission are comparatively neglected in the literature. The social sciences encompass a variety of disciplines and frameworks for understanding infectious diseases; however, here we focus on four primary areas of social systems that quantitatively and qualitatively contribute to infectious diseases as social–ecological systems. These areas are social mixing and structure, space and mobility, geography and environmental impact, and behaviour and behaviour change. Incorporation of these social factors requires empirical studies for parametrization, phenomena characterization and integrated theoretical modelling of social–ecological interactions. The social–ecological system that dictates infectious disease dynamics is a complex system rich in interacting variables with dynamically significant heterogeneous properties. Future discussions about infectious disease spillover and transmission in human populations need to address the social context that affects particular disease systems by identifying and measuring qualitatively important drivers. This article is part of the themed issue ‘Opening the black box: re-examining the ecology and evolution of parasite transmission’.

As biodiversity declines with anthropogenic land-use change, it is increasingly important to understand how changing biodiversity affects infectious disease risk. The dilution effect hypothesis, which points to decreases in biodiversity as critical to an increase in infection risk, has received considerable attention due to the allure of a win–win scenario for conservation and human well-being. Yet some empirical data suggest that the dilution effect is not a generalizable phenomenon. We explore the response of pathogen transmission dynamics to changes in biodiversity that are driven by habitat loss using an allometrically scaled multi-host model. With this model, we show that declining habitat, and thus declining biodiversity, can lead to either increasing or decreasing infectious-disease risk, measured as endemic prevalence. Whether larger habitats, and thus greater biodiversity, lead to a decrease (dilution effect) or increase (amplification effect) in infection prevalence depends upon the pathogen transmission mode and how host competence scales with body size. Dilution effects were detected for most frequency-transmitted pathogens and amplification effects were detected for density-dependent pathogens. Amplification effects were also observed over a particular range of habitat loss in frequency-dependent pathogens when we assumed that host competence was greatest in large-bodied species. By contrast, only amplification effects were observed for density-dependent pathogens; host competency only affected the magnitude of the effect. These models can be used to guide future empirical studies of biodiversity–disease relationships across gradients of habitat loss. The type of transmission, the relationship between host competence and community assembly, the identity of hosts contributing to transmission, and how transmission scales with area are essential factors to consider when elucidating the mechanisms driving disease risk in shrinking habitat. This article is part of the themed issue ‘Conservation, biodiversity and infectious disease: scientific evidence and policy implications'.

Consumer wearables have been successful at measuring sleep and may be useful in predicting changes in mental health measures such as stress. A key challenge remains in quantifying the relationship between sleep measures associated with physiologic stress and a user’s experience of stress. Students from a public university enrolled in the Lived Experiences Measured Using Rings Study (LEMURS) provided continuous biometric data and answered weekly surveys during their first semester of college between October-December 2022. We analyzed weekly associations between estimated sleep measures and perceived stress for participants (N = 525). Through mixed-effects regression models, we identified consistent associations between perceived stress scores and average nightly total sleep time (TST), resting heart rate (RHR), heart rate variability (HRV), and respiratory rate (ARR). These effects persisted after controlling for gender and week of the semester. Specifically, for every additional hour of TST, the odds of experiencing moderate-to-high stress decreased by 0.617 or by 38.3% (p<0.01). For each 1 beat per minute increase in RHR, the odds of experiencing moderate-to-high stress increased by 1.036 or by 3.6% (p<0.01). For each 1 millisecond increase in HRV, the odds of experiencing moderate-to-high stress decreased by 0.988 or by 1.2% (p<0.05). For each additional breath per minute increase in ARR, the odds of experiencing moderate-to-high stress increased by 1.230 or by 23.0% (p<0.01). Consistent with previous research, participants who did not identify as male (i.e., female, nonbinary, and transgender participants) had significantly higher self-reported stress throughout the study. The week of the semester was also a significant predictor of stress. Sleep data from wearable devices may help us understand and to better predict stress, a strong signal of the ongoing mental health epidemic among college students.

The transition to college is a period of growth and vulnerability for young adult health and well-being and provides a critical window for potential behavioral interventions. In this study, we sought to examine the trajectory of anxiety symptoms and their association with individual characteristics, exposure to stressors, and sleep behaviors during the transition to college. We recruited full-time, incoming undergraduate students at a university in the northeastern United States to participate during the first semester of college between October 21, 2022, and December 12, 2022. In a longitudinal cohort study (N = 556), we collected baseline demographic and health history information and weekly survey assessments with the outcome measure of anxiety. Predictors included weekly stressors and sleep measures during this period. Mixed-effects linear models were used to examine trajectories in anxiety symptoms during the first semester of college. We had 6 main findings. First, there were significantly higher anxiety symptoms in non-male participants compared to male participants. Second, a previous mental health diagnosis and previous traumatic exposures were significant predictors of anxiety symptoms. Third, the personality traits of extraversion and neuroticism were significant predictors of anxiety symptoms. Fourth, perceived sleep duration, quality, and satisfaction were significant predictors of anxiety symptoms. Fifth, sleep duration estimates collected by a biometric wearable were also a significant predictor of anxiety in covariate-adjusted, corrected models. Sixth, weekly stressors and specifically academic stressors were significant predictors of anxiety symptoms. Programs that support young adults entering college may promote sleep hygiene behaviors and target times of particularly elevated stress such as examination periods. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper self-identifies as living with a disability.