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Since the outset of the COVID-19 pandemic, substantial public attention has focused on the role of seasonality in suppressing transmission. Misconceptions have relied on seasonal mediation of respiratory diseases driven solely by environmental variables. However, seasonality is expected to be driven by host social behavior, particularly in highly susceptible populations. A key gap in understanding the role of social behavior in respiratory disease seasonality is our incomplete understanding of the seasonality of indoor human activity. We leverage a novel data stream on human mobility to characterize activity in indoor versus outdoor environments in the United States. We use a mobile app-based location dataset encompassing over 5 million locations nationally. We classify locations as primarily indoor (e.g. stores, offices) or outdoor (e.g. playgrounds, farmers markets), disentangling location-specific visitor counts into indoor and outdoor, to arrive at a fine-scale measure of indoor to outdoor human activity across time and space. We find the proportion of indoor to outdoor activity during a baseline year is seasonal, peaking in winter months. The measure displays a latitudinal gradient with stronger seasonality at northern latitudes and an additional summer peak in southern latitudes. We statistically fit this baseline indoor-outdoor activity measure to inform incorporation of this complex empirical pattern into infectious disease dynamic models. However, we find that the disruption of the COVID-19 pandemic caused these patterns to shift significantly from baseline, and the empirical patterns are necessary to predict spatio-temporal heterogeneity in disease dynamics. Our work empirically characterizes, for the first time, the seasonality of human social behavior at a large-scale with high spatio-temporal resolution, and provides a parsimonious parameterization of seasonal behavior that can be included in infectious disease dynamics models. We provide critical evidence and methods necessary to inform the public health of seasonal and pandemic respiratory pathogens and improve our understanding of the relationship between the physical environment and infection risk in the context of global ecological change.

Background: Since the outset of the COVID-19 pandemic, substantial public attention has focused on the role of seasonality in impacting transmission. Misconceptions have relied on seasonal mediation of respiratory diseases driven solely by environmental variables. However, seasonality is expected to be driven by host social behavior, particularly in highly susceptible populations. A key gap in understanding the role of social behavior in respiratory disease seasonality is our incomplete understanding of the seasonality of indoor human activity.Methods: We leverage a novel data stream on human mobility to characterize activity in indoor versus outdoor environments in the United States. We use an observational mobile app-based location dataset encompassing over 5 million locations nationally. We classify locations as primarily indoor (e.g. stores, offices) or outdoor (e.g. playgrounds, farmers markets), disentangling location-specific visits into indoor and outdoor, to arrive at a fine-scale measure of indoor to outdoor human activity across time and space.Results: We find the proportion of indoor to outdoor activity during a baseline year is seasonal, peaking in winter months. The measure displays a latitudinal gradient with stronger seasonality at northern latitudes and an additional summer peak in southern latitudes. We statistically fit this baseline indoor-outdoor activity measure to inform the incorporation of this complex empirical pattern into infectious disease dynamic models. However, we find that the disruption of the COVID-19 pandemic caused these patterns to shift significantly from baseline, and the empirical patterns are necessary to predict spatiotemporal heterogeneity in disease dynamics.Conclusions: Our work empirically characterizes, for the first time, the seasonality of human social behavior at a large scale with high spatiotemporal resolution, and provides a parsimonious parameterization of seasonal behavior that can be included in infectious disease dynamics models. We provide critical evidence and methods necessary to inform the public health of seasonal and pandemic respiratory pathogens and improve our understanding of the relationship between the physical environment and infection risk in the context of global change.Funding: Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM123007.

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Introduction In a sample of dual users of cigarettes and electronic nicotine delivery systems (ENDS; e-cigarettes), we evaluated psychometric properties of ENDS versions of the Nicotine Dependence Syndrome Scale (NDSS), the brief Wisconsin Inventory of Smoking Dependence Motives (WISDM), and the Fagerstrom Test of Nicotine Dependence (FTND). Using the NDSS, we tested the hypothesis that there would be one common underlying factor of dependence across the cigarette and ENDS scales and other product specific factors. Methods Adult dual users (N=404) completed baseline cigarette and ENDS versions of the NDSS, WISDM, and FTND, and biweekly surveys of their smoking and vaping. Analyses included bifactor modeling, which helps to identify both a general and product-specific factor for each item, and exploratory factor analyses of the combined cigarette and ENDS NDSS items and examinations of concurrent and predictive validity. Results The bifactor model was not a good fit, suggesting the lack of one common underlying dependence factor. Factor analyses revealed separate, similar factors for both products, with only one factor (priority) showing overlap of cigarette and ENDS items. ENDS scales significantly predicted ENDS use over time, but not cigarette use. Cigarette scales did not predict ENDS use over time. Conclusions Although the cigarette and ENDS NDSS versions showed similar factor structure, there was not a primary common underlying factor reflecting drive or tolerance, but rather product-specific factors. The cigarette scales were not valid for predicting ENDS use. These results highlight the importance of separately assessing dependence for cigarettes and ENDS in dual users. Implications Although underlying dimensions of nicotine dependence may be similar for ENDS and cigarettes, separate, product-specific measures may be needed to understand differences in product-specific dependency and predict changes in use of each product over time.